© 2013

AMY B. HOLLINGSWORTH

ALL RIGHTS RESERVED

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Q METHODOLOGY AS A NEEDS ASSESSMENT TOOL FOR BIOLOGY

GRADUATE TEACHING ASSISTANTS PARTICIPATING IN AN

INSTRUCTIONAL TRAINING PROGRAM

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

i

Amy B. Hollingsworth

November 1, 2013

Q METHODOLOGY AS A NEEDS ASSESSMENT TOOL FOR BIOLOGY

GRADUATE TEACHING ASSISTANTS PARTICIPATING IN AN

INSTRUCTIONAL TRAINING PROGRAM

Amy B. Hollingsworth

Dissertation

ii

ABSTRACT

The purpose of this study is to demonstrate how Q

Methodology can be used as a needs assessment tool for a

Biology graduate teaching assistant (GTA) instructional

training program. GTAs are used as the instructors of an

increasingly diverse population of undergraduate students.

GTAs are a diverse population of students with varying

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Approved: Accepted:

______________________________

______________________________

Co-Chair Department ChairJennifer L. Milam, Ph.D. Susan J. Olson, Ph.D.

______________________________

______________________________

Co-Chair/ Methodologist Dean of the CollegeSusan E. Ramlo, Ph.D. Susan G. Clark, Ph.D.,

J.D.______________________________

______________________________

Committee Member Dean of the Graduate School

Robert Joel Duff, Ph.D. Dr. George R. Newkome

______________________________

______________________________

Committee Member DateGary M. Holliday, Ph.D.

amounts of pedagogical preparation, research abilities, and

motivation to complete their graduate study. They are often

expected to prepare and grade exams, write their own

syllabi, design course curriculum, prepare and present

lectures, monitor student progress, hold office hours, and

assign final grades, all with minimal faculty supervision.

Although not all GTAs will become professors, many will, and

the teaching assistantship remains the major preparation for

their roles as faculty members. Since the majority of

science professors have been GTAs, this instructional

training program is of critical importance.

Approaches to developing instructional training

programs for GTAs vary from departmental workshops to

campus-wide instructional seminars. Program evaluation is an

intrinsic part of assuring that such programs best serve GTA

needs, and that GTAs can best fulfill their roles in their

respective departments. Q Methodology offers a number of

potential advantages over traditional survey techniques for

assessing needs of GTAs throughout their graduate school

career, allowing program supervisors to evaluate and modify

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the program relative to GTA needs. Q Methodology allows the

researcher to identify and interpret various viewpoints the

GTAs hold in regard to graduate school. This is not only

important to the supervisors of GTA instructional programs,

but to the GTAs.

This Q Methodology study led to three GTA viewpoints

(“The Emerging Teacher,” “The Preferred Researcher,” and

“The Anxious GTA”) that provide insight about GTA and

programmatic needs. Q Methodology can provide predictor

profiles, or “typologies” that are more useful than simple

variables and demographic information for the classification

of people, especially within program evaluation (Newman &

Ramlo, 2011). “The Anxious GTA” viewpoint, which suggests a

group of GTAs who may be at risk for failure in their degree

program, may be further investigated for retention and

program completion. The results of this study will be used

to consider potential changes or updates to the existing

training program that may include scaffolding,

differentiation, peer or faculty mentoring, or self-directed

learning strategies.

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ACKNOWLEDGEMENTS

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TABLE OF CONTENTS

ABSTRACTACKNOWLEDGEMENTSTABLE OF CONTENTSList of TablesList of FiguresList of DefinitionsPrologueResearcher Positionality

CHAPTER IINTRODUCTION TO THE STUDYIntroductionPurpose of the StudyStatement of the ProblemSignificance of the StudyGeneral Research QuestionsDelimitationsSummary

Chapter IIREVIEW OF THE LITERATUREWhy go to graduate school?The Usage of Graduate Teaching Assistants in Higher EducationTeaching “Assistant” or Course Instructor?Instructional Training Programs for GTAsGraduate School and the Socialization of AcademicsConflicting Priorities in a Graduate School Program

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National Training Programs vs. Locally Developed Training ProgramsThe Modern Academic WorkplaceEvaluating Graduate Teaching Assistant Training ProgramsQ MethodologySummary

CHAPTER IIIMETHODOLOGYIntroduction and OverviewGeneral Research QuestionsRationale for the Research DesignBasic Procedures of Q MethodologySettingThe P-SetThe ConcourseSRQ – Self Reflection QuestionnaireThe Perceptions of Graduate School SurveyStatements from the Literature

Q SampleQ SortThe Pilot StudyData Collection ProceduresRole of the ResearcherLimitationsSummary

CHAPTER IVRESULTSDescriptive DemographicsData Collection

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Data AnalysisAnalysis and InterpretationFactor 1Factor 2Factor 3

Consensus StatementsResults of Testing the Research HypothesesGeneral Research Hypothesis 1General Research Hypothesis 2General Research Hypothesis 3General Research Hypothesis 4

SummaryCHAPTER VSUMMARY, CONCLUSIONS, AND IMPLICATIONSSummary of the StudyStatement of the ProblemStatement of the ProceduresThe Research HypothesesGeneral Research Hypothesis 1General Research Hypothesis 2General Research Hypothesis 3General Research Hypothesis 4

ConclusionsGeneral Research Questions

ImplicationsDifferentiating the Instructional Training ProgramQ Methodology as a Self-Diagnostic ToolCollective MentoringPromises and Challenges of Q Methodology

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Suggested Further ResearchSummary

ReferencesAppendicesAppendix 1: Concourse DevelopmentAppendix 2: Q SampleAppendix 3: Conditions of InstructionAppendix 4: IRB Informed Consent LetterAppendix 5: IRB Exemption RequestAppendix 6: IRB Exemption

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LIST OF TABLES

TABLE PAGE

Table 1 – P-Set Demographics

Table 2 - Development of the Concourse and Q Sample

Table 3 - Demographic Characteristics of GTAs completing the

SRQ

Table 4 - Demographic Characteristics of TAs Completing the

“Perceptions of Graduate School Survey”

Table 5 – Demographics of New and Experienced Biology GTA

Table 6 - Coding System for Study Participants

Table 7 - Factor Matrix with X Indicating a Defining Sort

Table 8 - Factor Values for Each Statement

Table 9 - Eight Most-Like My View Statements for Factor 1

"The Emerging Teacher" with a † indicating a Distinguishing

Statement.

Table 10 - Eight Least-Like My View Statements for Factor 1

"The Emerging Teacher" with a † indicating a Distinguishing

Statement.

Table 11 - Distinguishing Statements for Factor 1 " The

Emerging Teacher ".

Table 12 - Post-Sort Interview Responses for Factor 1

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Table 13 - Eight Most-Like My View Statements for Factor 2

"The Preferred Researcher” with a † indicating a

Distinguishing Statement.

Table 14 - Eight Least-Like My View Statements for Factor 2

"The Preferred Researcher” with a † indicating a

Distinguishing Statement.

Table 15 - Distinguishing Statements for Factor 2 "The

Preferred Researcher".

Table 16 - Post-Sort Interview Responses for Factor 2 “The

Preferred Researchers”

Table 17 - Eight Most-Like My View Statements for Factor 3

“The Anxious GTA” with a † indicating a Distinguishing

Statement.

Table 18 - Eight Least-Like My View Statements for Factor 3

“The Anxious GTA” with a † indicating a Distinguishing

Statement.

Table 19 - Distinguishing Statements for Factor 3 “The

Anxious GTA.”

Table 20 - Post-Sort Interview Responses for Factor 3 “The

Anxious GTA.”

Table 21 - Consensus Statements – Statements in Common

Amongst Factors

Table 22 – Number of Q-Sorts Included in Each Factor

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Table 23 – Breakdown of Number of Q-Sorts Included in Each

Factor

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LIST OF FIGURES

FIGURE PAGE

Figure 1 - "GTA Preparedness" based upon Cho et. al.

Figure 2 - The Five Stages of GEM (based upon McNeil et al.,

2005)

Figure 3 - Sample Grid

Figure 4 - Sample Grid Showing “Normalized” or Gaussian

Distribution

Figure 5 – Conditions of Instruction for “GTA Perceptions of

Graduate School Q Sort

Figure 6 - Distribution Grid for “GTA Perceptions of

Graduate School Q Sort”

Figure 7 – Representative Sort for Factor 1

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LIST OF DEFINITIONS

Age Measured chronologically, in years; self-

reported by participants.

Biology Lab Coordinator

A staff member in The Department of Biology in

a large, research-focus, degree granting

university, whose primary duty is to supervise

Biology GTAs while teaching undergraduate

Biology laboratories.

Biology Lead Faculty Member

A faculty member in The Department of Biology

in a large, research-focused, degree granting

university, who directs the teaching education

of new Biology GTAs.

Career Track Following a professionally developed path

towards a desired career.

Concourse The flow of communicability surrounding any

topic (Brown, 1993). The collection of all the

possible statements the respondents can make

about the subject at hand (Van Exel & De Graaf,

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2005).

Condition of Instruction

Provided by the researcher, this is a set of

instructions, used by a participant, for

sorting the Q Sort cards from his or her own

point of view (Brown, 1993; McKeown & Thomas,

1988; Van Exel & de Graaf, 2005).

Country of origin - United States GTAs

A graduate level student born in and primarily

educated in The United States. Self-reported.

Country of origin - International GTAs

A graduate level student born in and primarily

educated in a country other than The United

States. Self-reported.

Experience, insemesters

A division constituting half of the regular

academic year, lasting typically from 15 to

18weeks (“the definition of semester,” n.d.).

Self-reported.

Experienced Biology GTA

A graduate level student who is seeking a

master’s or doctoral degree through The

Department of Biology in a large, research-

focused, degree-granting university, with more

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than one year of formal teaching experience,

and who teaches an undergraduate-level

laboratory for approximately 20-hours a week in

exchange for a fee-remission. This GTA has

completed an "Effective Teaching" GTA training

program.

Gender Self-identification with roles and expectations

attributed to men and women in a given society

(Phillips, 2005).

New Biology GTA

A graduate level student who is seeking a

master’s or doctoral degree through The

Department of Biology in a large, research-

focused, degree-granting university, with less

than one year of formal teaching experience,

and who teaches an undergraduate-level

laboratory for approximately 20-hours a week in

exchange for a fee-remission. This GTA is

currently enrolled in an "Effective Teaching"

GTA training program.

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Professional Development

The development of a person in his or her

professional roles. More specifically, “Teacher

professional development is the professional

growth a teacher achieves as a result of

gaining increased experience and examining his

or her teaching systematically” (Glatthorn,

1995, p. 41).

P - Set The purposefully chosen set of participants,

also called the sorters, or the respondents

(Brown, 1993).

Q Methodology A methodological tool that provides an

objective way to measure subjectivity. (Newman

& Ramlo, 2011; Brown, 1980; Stephenson, 1953)

Q Sample The set of statements, selected from the

concourse, which represent the communicability

of the topic; the respondents will sort these

statements into a grid, based on the condition

of instruction (Newman & Ramlo, 2011; Brown,

1980; Stephenson, 1953).

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Q Sort The process of distributing the Q Sample into a

researcher provided grid. The statements are

administered in the form of a pack of randomly

numbered cards (one statement to a card) with

which the person is instructed to sort

according to "condition of instruction (Brown,

1993).

Teaching experience, Formal

Teaching in an educational setting such as a

university or training institution, with a set

curriculum, which is leading towards a

certification or degree (Dib, 1988).

Teaching experience, Informal

Teaching that occurs alongside formal teaching,

such as tutoring, afterschool, or informal

learning situations, with a flexible

curriculum, that does not lead towards a degree

or certification (Dib, 1988).

Theoretical Sorting

A process where a study participant sorts their

statements, according to the conditions of

instruction, based upon their own beliefs of

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how another participant would sort.

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PROLOGUE

Researcher Positionality

In September of 2000, having just graduated from my

undergraduate university with a degree in Biology, I moved from

my small hometown in North Eastern Ohio, to Eagle Pass, Texas, a

Mexican border town. Even though I had not had a single education

class, I was hired at the local high school. At the age of 22,

with no formal teacher training, I began teaching an 11th grade

Chemistry class. I was expected to teach 100 primarily Spanish-

speaking students, classified “at-risk” due to low socioeconomic

status. I was only two to four years older than most of them. My

degree in Biology couldn’t have begun to prepare me for teaching.

I taught Chemistry the same way I had been taught Chemistry -

“chalk and talk.”

Every morning during my first period “teacher prep time,” my

colleague and I would sit down in his classroom, eat breakfast

tacos made by his lovely wife, and write lectures, find

worksheets, or figure out problems. He handed me what I was going

to teach for the day, every morning. Some days, my teaching was

terrible. My students were difficult to understand, because they

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were so unlike me. I wondered if they were learning, and I

questioned whether I should be teaching at all. Other days, I

felt breakthroughs where they “got it,” we had fun actively

engaging in the laboratories, and I counseled them concerning

problems in their lives. I would advise them on getting into

college, classes with other teachers, frustrations with their

parents, or achieving their dreams. Outwardly, it appeared I was

“successful at teaching.” But were my students successful at

learning?

I continued teaching high school for ten years. After

completing a teaching certification program and a Master’s Degree

in Education while teaching full time, I was offered a position

in my hometown writing Biology curriculum, working with Biology

graduate teaching assistants as the laboratory coordinator of the

Natural Science Biology lab, and teaching at the college level.

While working at the university, I could also pursue a Ph.D. I

became a graduate student in Curriculum and Instruction, working

alongside graduate student TAs in Biology.

I recognized in these GTAs many of the same feelings,

insecurities, frustrations, and fears that I had as an untrained

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high school teacher. Just as I was expected by the school

district to become a trained secondary teacher, GTAs are expected

to utilize their teaching opportunities to transform into a

college instructor – whether that is their planned career path or

not. Just as I faced my students with no instructional training,

so do these GTAs. However when I taught high school, I was

expected to take pedagogy courses to train as a teacher. Those

courses were invaluable in developing my skills in instruction,

engaging with students, and classroom management. These GTAs face

their own students with no formal training, little feedback on their

teaching, and a feeling of “What am I doing here?” They just hope

to survive the semester.

I recognize GTAs’ struggles, and make note of the challenges

they face as they work with undergraduate students, teach the

lab, work with their advisors, take their own classes, do

original research, write theses and dissertations, and attempt to

juggle it all with a personal life. Each GTA comes to me with a

unique story, a different path, and an individualized perspective

on graduate school. I have observed GTAs who were paralyzed with

fear each time they faced the class as well as those who were so

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brazenly cocky they saw their students as “stupid undergrads.”

GTAs with a “know-it-all” attitude often ended up with their

classes revolting against them. I wish I could hand them some

equation, some formula for teaching that works for all GTAs,

which would answer all their questions before they ever faced

with a student of their own. Their faculty mentors often express

that “all professors felt this way when they were GTAs” and that

the GTAs must face this awkward, frustrating experience of

teaching just as they did, and will either “sink or swim.”

Graduate school is hugely uncomfortable, for so many

reasons, and I recognize this as I struggle through graduate

school myself. You just don’t know what you don’t know. It’s as

challenging for me as I know it is for my GTAs. In striving to

make at least some parts of graduate school less painful for

them, I have come to understand the transformative graduate

school process for myself. Though I am a “participant observer”

in my research, I also feel I have been given a huge gift in my

own doctoral program. While I have been researching the

challenges of masters and doctoral Biology students and looking

at ways to increase their teaching effectiveness and program

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completion, I have become a better teacher myself, and have

completed my own program.

My positionality, perspectives, and biography undoubtedly

affect my work with Biology GTAs on an everyday basis, and have

affected my fieldwork. I am incapable of extracting myself from

my research, and I arguably should not try. I embrace my position

as participant, my shifting subjectivity, and my situated

knowledge. My enthusiasm for teaching, research, and science co-

mingle inextricably. Q Methodology, which I have been drawn to

for my research, is inherently linked to who I am. Biology

research is empirical, looking at how systems interact, observing

how organisms communicate with others, and within their

environment. The scientist in me wants to make observations,

collect data, and do statistical analyses. The social scientist

in me wants thick, rich descriptions that persist in qualitative

research. The perspectives of GTAs and faculty who work with them

have driven my research, and drive my daily life. Q Methodology,

a mixed method, allows me to study people’s subjectivities, or

viewpoints, in a way that pays homage to both my social sciences

and hard sciences backgrounds. My research is my attempt to

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provide instructional training for GTAs that is meaningful,

relevant, and positively impacts all the stakeholders involved.

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CHAPTER I

INTRODUCTION TO THE STUDY

The purpose of this chapter is to present the problem,

purpose of the study, and research questions. In addition, the

researcher discusses the significance of the study. A brief

review of the literature provides introductory information

related to the six major topics of this study: The history of

graduate teaching assistants (GTAs) in higher education, the use

of GTAs as course instructors, the varying aspects of GTA

instructional training programs, GTA socialization as future

faculty, needs assessments in program evaluation, and Q

Methodology. Finally, the delimitations of the study are stated.

Introduction

Graduate Teaching Assistants (GTAs) are frequently utilized

as instructors in undergraduate classrooms and science

laboratories (Kendall & Schussler, 2012; Luft, Kurdziel, Roehrig,

& Turner, 2004; Nyquist & et al., 1991). GTAs provide

universities a cost-effective form of instructor while the GTAs

are being simultaneously socialized into the roles of teacher,

researcher, and scholar (Carroll, 1980; Garland, 1983). GTAs

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represent a diverse population of masters and doctoral-level

students, with varying amounts of pedagogical preparation,

research abilities, and motivation to complete their graduate

study (Boyle & Boice, 1998). GTAs who are not adequately prepared

to engage in teaching activities may display a wide range of

behaviors, from an overblown confidence in their abilities (Golde

& Dore, 2001), to frustration and insecurity (Eison & Vanderford,

1993). The main preparation for new faculty has been teaching

assistantships, so they are limited in their teaching repertoire

by the nature of their particular assignment—usually in a

discussion section or laboratory for a large lecture class, often

without supervision or adequate mentoring (Luft et al., 2004;

Nyquist & Woodford, 2000).

Instructional training programs for professionally

developing graduate teaching assistants vary extensively from

institution to institution, and even between departments at the

same institution (Nyquist & Woodford, 2000; Parrett, 1987;

Stockdale & Wochok, 1974). Calls for instructional training

programs for teaching assistants in the sciences (Carroll, 1980;

Luft et al., 2004), and more specifically in biology (Rushin et

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al., 1997; Tanner & Allen, 2006) have created a continual demand

for pedagogical training, in addition to content area mastery.

Responses to the calls for instructional training programs

have included national projects such as “Re-Envisioning the

Ph.D.” (Nyquist & Woodford, 2000), the “Preparing Future Faculty”

project (Pruitt-Logan, Gaff, & Jentoft, 2002), and the

“Responsive Ph.D.” project (Woodrow Wilson National Fellowship

Foundation, 2000). These projects focus broadly on improving the

outcomes of Ph.D. degree programs (Gilbert, Balatti, Turner, &

Whitehouse, 2004). These large-scale projects are dependent on

external grant funding, and though institutions may retain

certain aspects of these programs after the grant ends, their

sustained existence after the termination of funding has proved

difficult (Ferren, Gaff, & Clayton-Pedersen, 2002).

Locally developed GTA instructional training programs are

much more common in graduate schools or disciplinary departments,

and are described at length in Chapter II. These programs are led

by graduate school or disciplinary faculty or GTA supervisors,

and vary widely in programmatic elements and effectiveness

(Carroll, 1980; Parrett, 1987; Thornburg, Wood, & Davis, 2000).

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Programs range from half day university-wide orientation sessions

that introduce new GTAs to university policies but provide no

departmental training, to multiday university-wide training,

department-specific training, or even university-wide training

coupled with full-semester courses and seminars on teaching

methods offered by specific departments (Rushin et al., 1997).

Thus the amount and type of professional development made

available to GTAs remains highly variable in higher education

institutions.

Whether the GTA instructional training program emerges

nationally, from the graduate school, or the individual

disciplinary department, the evaluation of that program is a

complex and necessary part of any type of professional

development (Garet, Porter, Desimone, Birman, & Yoon, 2001;

Guskey, 1994). Program evaluation is an intrinsic part of any

program or project because it is used to both measure the

effectiveness of that program or project as well as investigate

ways to increase that effectiveness (Newman & Ramlo, 2011). The

literature surrounding GTA training programs describes GTAs as

having varying programmatic needs based on numerous factors –

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prior formal or informal teaching experience, familiarity with

content, exposure to prior instructional training, demographic

variables, career aspirations, international status, etc. GTA

programs often group cohorts of GTAs together for training

(Muzaka, 2009) – all masters students or all doctoral students in

one department, all the GTAs in a department or graduate school

at the beginning of their program, all the GTAs teaching a common

laboratory course, etc. – the combinations are numerous. One of

the first steps in effective program evaluation is assessing the

needs of the particular set of participants in that program

(Chen, 2005; McNeil, Newman, & Steinhauser, 2005).

A needs assessment is a “systematic set of procedures for

the purpose of setting priorities and making decisions about a

program or organizational improvement and allocation of

resources. The priorities are based on identified needs (Witkin,

1995).” A need is a discrepancy or gap between “what is,” or the

present state of affairs in regards to the group and situation of

interest, and “what should be,” or a desired state of affairs. A

needs assessment seeks to determine such discrepancies, examine

their nature, and set priorities for future action (Kaufman,

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Rojas, & Mayer, 1993; Kaufman & Valentine, 1989; Leigh, Watkins,

Platt, & Kaufman, 2000). In order to do a needs assessment, there

must be a needs assessment tool.

There are challenges to designing a needs assessment tool

for instructional training programs. GTA needs assessment tools

for instructional training programs have usually been modified

teaching inventories (Angelo & Cross, 1993; Gibson & Dembo, 1984;

Kohn, Lafreniere, & Gurevich, 1990; Prieto & Altmaier, 1994;

Renzulli & Smith, 1978), Likert-style questionnaires (Cho,

Sohoni, & French, 2010; Sohoni, Cho, & French, 2013), or basic

demographic surveys. These instruments may not provide useful or

adequate understandings of the various viewpoints that exist

among GTAs about their needs in an instructional training

program. Classification of GTAs based on typologies, or predictor

profiles, may be more useful for program evaluation, because

typically a program does not have the same level of effectiveness

for the entire population it serves (McNeil et al., 2005).

Typologies may also be helpful in determining the combination of

criteria that would accurately predict the success of at-risk

students in graduate education (Nelson, Nelson, & Malone, 2000).

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Q Methodology offers a number of potential advantages for

assessing needs of GTAs throughout their graduate school career –

Q Methodology can be used with small numbers of individuals,

within a group, and completed anonymously (Peritore, 1989;

Prasad, 2001). Q Methodology does not demand the large number of

participants that a Likert-style survey requires (Cummins &

Gullone, 2000). Because the literature about GTAs frequently

refers to GTAs in different disciplines or different types of

schools, the needs of GTAs in other disciplines are not

necessarily the needs of this specific group of Biology GTAs. Q

Methodology allows the researcher to determine the various

perspectives and consensus within the group (Ramlo, 2008).

Q Methodology was first described by William Stephenson in

1935 in “Correlating Persons Instead of Tests (Stephenson,

1935).” He described how Q Methodology allows researchers to

identify, both quantitatively and qualitatively, the various

viewpoints within a group and the number of people within the

group who hold these viewpoints (Ramlo, 2008). Q Methodology

provides a foundation for the systematic study of subjectivity, a

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person’s “viewpoint, opinion, beliefs, attitude, and the like

(Brown, 1993).”

Typically, in a Q Methodological study, sorters are

presented with a sample of statements about some topic, called

the Q Sample. Respondents, called the P-set, are asked to rank-

order the statements from their individual point of view,

according to some preference, judgment or feeling about them,

mostly using a quasi-normal distribution (Van Exel & de Graaf,

2005). By Q Sorting, people give their subjective meaning to the

statements, and by doing so reveal their subjective viewpoint

(Smith, 2001) or personal profile (Brouwer, 1999). Q Methodology

allows the researcher to identify and interpret various

viewpoints, such as viewpoints held by GTAs in regard to graduate

school. These viewpoints may be important to both the supervisors

of GTA instructional programs and to the GTAs.

Purpose of the Study

The purpose of this study was to demonstrate that Q

Methodology can be used as an effective needs assessment tool for

a Biology graduate teaching assistant (GTA) instructional

training program. Q Methodology offers a number of potential

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advantages in program evaluation over traditional survey

techniques for assessing needs of GTAs throughout their graduate

school career. Ramlo (2008) described how Q Methodology “is an

appropriate choice whenever a researcher wishes to determine the

various perspectives and consensus within a group regarding any

topic.” GTAs often express frustration with balancing the

challenges of teaching, working with undergraduate students,

rigorous graduate classes, learning to do research, and having a

personal life (Boyle & Boice, 1998; Drake, 2011; Gaff, 2002;

Tice, Gaff, & Pruitt-Logan, 1998). They are often expected to

prepare and grade exams, write their own syllabi, design the

course curriculum, order textbooks, prepare and present lectures,

monitor student progress, and assign final grades, all with

minimal faculty supervision (Mueller, Perlman, McCann, &

McFadden, 1997; Nyquist, Abbott, & Wulff, 1989). In addition to

the academic responsibilities that GTAs assume, they are also

called on to hold office hours (Mueller et al., 1997), which

typically involves assuming an advising role - guiding students

on topics such as mastery of course material, academic concerns,

applying to graduate school, and even counseling students through

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personal problems (Moore, 1991). As instructors of undergraduates

, GTAs must make instructional, curricular, and assessment

decisions in their courses (Luft et al., 2004). GTAs are not

serving as merely “teaching assistants,” GTAs are often

responsible for the much of the instruction at the undergraduate

level at major universities in the United States (Allen & Rueter,

1990).

The challenges that GTAs experience in graduate school

evolve from the beginning of their program to the culmination of

a thesis or dissertation (Muzaka, 2009). GTAs may begin their

programs with serious doubts about their levels of content

knowledge or abilities to teach, which may evolve into

frustrations about demands on their time, pressures to publish,

and difficulties with research. While many faculty and

administrators posit the purpose of doctoral education to be the

preparation to conduct original research (e.g., Council of

Graduate Schools, 1990), others contend that the purposes of

doctoral education should be further reaching, including the

training to teach (Adams, 2002; Gaff, 2002a) as well as the

development of generic or transferable skills such as public

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speaking, writing for different types of audiences, teaching, how

to think about problems and dig into the literature unaided,

time-management, and people-management skills (Crebert, Bates,

Bell, Patrick, & Cragnolini, 2004; Cryer, 1998; Gilbert, Balatti,

Turner, & Whitehouse, 2004). These skills are necessary for both

teaching, and the labor market outside of academia (Atwell, 1996;

Golde & Walker, 2006; Jones, 2003). While their institutions may

articulate messages about the importance of the teaching mission,

their advisors, particularly in STEM fields, may urge them to

avoid spending too much time on anything besides research-related

activities (Austin et al., 2009).

Virtually all graduate students receive their Ph.D.'s from a

research university (Cassuto, 2011). They get their first

classroom experience there, and their dissertations are mainly

guided by professors whose research occupies a prominent place in

their work lives. The graduate student works his or her way from

outsider to the profession, to full member, under the mentorship

of their advisors (Filstad, 2004). But because most academic jobs

aren't at research universities (e.g. liberal arts college, for-

profit schools, 2-year colleges, community colleges), those other

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jobs look jarringly different to graduate students than the

positions held by their role models (Cassuto, 2011). Graduate

students express concern about their lack of explicit feedback

about their development (Austin et al., 2009).

Whereas at one time, biology GTAs would have transitioned

from graduate school to biology researcher, the labor market in

higher education is changing from tenure-track positions to

teaching-intensive positions (Anwar, 2013; Carpenter, 2010;

Jones, 2003). GTAs often struggle to gain the skills that help

them to be successful in either an academic career or in industry

(Austin & Wulff, 2004; Cassuto, 2012; Hayes, 2007). As GTAs

confront the challenges of graduate school, it is important for

their supervisors to evaluate the specific cohort’s needs and

modify the GTA program in relation to them.

Socialization in graduate school refers to the process

through which individuals gain the knowledge, skills, and values

necessary for successful entry into a professional career

requiring an advanced level of specialized knowledge and skills

(Gardner, 2005; Weidman, Twale, & Stein, 2001). Socialization is

also described as the process through which an individual learns

24

to adopt the values, skills, attitudes, norms, and knowledge

needed for membership in a given society, group, or organization

(Merton, 1968; Tierney, 1997; Van Maanen, 1976). The

socialization of graduate students is an unusual double

socialization. New students are simultaneously directly

socialized into the role of graduate student, while being given

preparatory socialization into the role of future faculty in a

research institution (Golde, 2002).

There has been a concerted effort by faculty in disciplinary

fields and in graduate schools to continually address whether

graduates are prepared adequately to perform the roles for which

they have been socialized, so that the graduate program can make

appropriate adjustments. It is desirable, but not always present,

that there be regular opportunities for the voices of graduate

students to be heard, so that their perspective informs program

development (Weidman et al., 2001).

Statement of the Problem

Despite the wealth of literature concerning elements of

instructional training programs for GTAs at the national,

institutional, or departmental level, typically a program does

25

not have the same level of effectiveness for the entire

population it serves (McNeil et al., 2005). The first step in

program evaluation – using a needs assessment tool to identify

participant needs – is often missing or incomplete. This study

demonstrated how Q Methodology can be used as a needs assessment

tool in a Biology GTA instructional training program. Q

Methodology can provide predictor typologies that are more useful

than simple variables and demographic information for the

classification of people, especially within program evaluation

(Newman & Ramlo, 2011).

The researcher used Q Methodology to investigate new and

experienced biology GTA views of graduate school, including their

views about teaching, learning, students, research, and

challenges to persisting in their program. Multiple survey

instruments were used to gather initial information about the

participants and their views about their biology graduate

program. The concourse, discussed in Chapter III, for this study

included a collection of statements made by GTAs in a Self-

Reflection Questionnaire, a “Perceptions of Graduate School

Survey,” a graduate student discussion forum (“Grad School Life,”

26

2012), and everyday conversations and emails made between Biology

GTAs and their supervisors. A Q Sample was selected from this

concourse. A pilot study with new Biology GTAs demonstrated the

viability of the research design and instrument and led to three

viewpoints (“The Confident Teachers,” “The Preferred

Researchers,” and “GTA to Professor”). The research study was

expanded to include both new and experienced GTAs. The results of

this study may be used to consider potential changes or updates

to the existing training program.

Significance of the Study

While the number of pre-service orientation programs, in-

service workshops, seminars, apprenticeship programs, intern

programs, and extern programs for GTAs have increased in the last

50 years (Carroll, 1980), the crucial step of conducting a needs

assessment to assess GTA need in their instructional training

programs is often missing or incomplete. A review of the

literature revealed that GTA needs in a program are often

collected using modified teacher inventories (Angelo & Cross,

1993; Gibson & Dembo, 1984; Kohn et al., 1990; Prieto & Altmaier,

1994; Renzulli & Smith, 1978), Likert-style surveys (Cho et al.,

27

2010; Gorsuch, 2003), using simple demographic variables – or are

not assessed at all (Shannon, Twale, & Moore, 1998; Worthen,

1992).

The most commonly used formal needs assessment tools used

for GTA “teaching needs” are modified secondary teaching

inventories. These have included The Learning Styles Inventory

(LSI) (Renzulli & Smith, 1978), The Teaching Goals Inventory

(TGI) (Angelo & Cross, 1993), The Teacher Efficacy Scale (TES)

(Gibson & Dembo, 1984), The Self-Efficacy Toward Teaching

Inventory (SETI) (Prieto & Altmaier, 1994), and The Inventory of

College Students' Recent Life Experiences (ICSRLE) (Kohn et al.,

1990). This is problematic, however, because higher education

instructors are vastly different than high school teachers

(Marston, 2010). GTAs will have different needs in an

instructional training program than secondary school teachers.

Likert-style surveys have been criticized for issues related

to construct validity, scale construction, the large number of

respondents needed, and reliability (Cummins & Gullone, 2000).

The Likert scale is used to measure attitudes and opinions

through statements as each subject expresses his/her agreement

28

with the contents of the statements by choosing one alternative:

strongly agree, agree, uncertain, disagree, strongly disagree

(Lalla, Facchinetti, & Mastroleo, 2005). The closed question

format obliges respondents to choose only from among the

available options that may not match their actual opinions or

attitudes. What distinguishes between strongly agree, and agree?

Will the respondent always choose agree, or can the choice vary

based on certain factors? These inconsistencies leads to an

increase in missing data and a possible drift toward the social

acceptability of the answers varying between individuals, over

space, and time (Orvik, 1972).

The only specific GTA needs assessment tool was a survey

developed by Cho et al. (2010) “to capture to what extent GTAs,

faculty, and undergraduate engineering students rate the

importance of typical GTA roles and responsibilities. “ The

Likert-style survey included 24 items, which were later grouped

into four categories. The four categories were 1) GTA

preparation, 2) Instructional Practices, 3) Engagement with

Students, and 4) Classroom Management. The survey takers were

asked to “rate the importance of typical GTA roles and

29

responsibilities” from “not at all important” to “critically

important.”

In the first category, “GTA Preparedness,” GTAs indicated

that all the items were between “critically important” and

“important” (See Figure 1). GTAs continued to mark all the

statements as close to “critically important” for the entire

survey. The faculty rated all the items as “important,” but not

“critically important.” This survey provides questionable value

when participants have no frame of reference for prioritizing the

statements, or can mark all the statements in one fashion.

Q Methodology allows researchers to identify, both

quantitatively and qualitatively, the various opinions within a

group, and the number of individuals who hold those opinions

(McKeown & Thomas, 1988; Stephenson, 1953). Thus, Q Methodology

is an appropriate choice whenever a researcher wishes to

determine the various perspectives and consensus within a group

(Brown, 1980). Q Methodology is similar to the Likert -style

survey in that the distribution on the grid typically ranges from

least like my view to most like my view (Ramlo, 2008). However,

it differs from Likert-style surveys in that Q Methodology

30

involves participants physically sorting items relative to each

other into a normalized or Gaussian distribution, based upon that

participant’s opinion within a particular setting, known as the

condition of instruction (Brown, 1993; 1980; McKeown & Thomas,

1988; Ramlo & Nicholas, 2009).

Likert (1967) assumed that every statement is equally

important to the overall attitude. McKeown (2001) criticized this

31

GTA Competence Rating by GTAs and Faculty

Item Category/Statement Rating by

GTA

Rating by

Faculty

Being familiar with the syllabus 4.30 3.28

Being familiar with the course objective 4.22 3.33

Being familiar with the course materials 4.32 3.67

Knowing answers to student questions 4.19 3.50

Knowing what is expected of the GTA 4.17 3.50

Dressing appropriately 4.08 3.65

Holding regular office hours 4.54 3.94

Figure 1 - "GTA Preparedness" based upon Cho et. al.

type of survey, in that the individuality of the respondents may

be lost, due to the averaging of scores. Q Methodology is self-

referential, meaning that the sorting refers to one’s own world

view, or subjectivity (McKeown & Thomas, 1988). Rather than

simply indicating agreement or disagreement with statements,

GTAs, when doing a Q Sort, are asked to sort the statements in

relation to the other statements in the Q Sample. After the GTAs

have completed their Q Sorts, factor analysis is performed. The

resulting analyses and tables will provide insight about the

various viewpoints held by GTAs in their training program.

Identifying and incorporating perspectives of GTAs into

their development program by performing a needs assessment is an

important first step in enhancing the effectiveness of the

training programs for GTAs. Fuller(1969) suggested that to ensure

effective teacher development programs, it is critical to

accurately assess teacher concerns. In addition, teacher training

or professional development programs that do not reflect the

needs and interests of participants are unlikely to motivate them

(Clarke & Hollingsworth, 2002), which in turn can result in the

failure to attain the program’s educational goals and objectives

32

(Cho et al., 2010). This speaks directly to the importance of

need assessment tools designed to identify what motivates and

concerns teachers, or in this case GTAs, in advance of developing

training programs.

If a program is to be useful to its stakeholders—in this

case, the Biology GTAs—it is important to keep their expectations

in mind. For graduate students to become proficient in the skills

desired from academia, they must be given opportunities to

develop their teaching skills, abilities, and knowledge with the

same guidance and practice that is afforded to the development of

a quality researcher (Golde & Dore, 2001).Because stakeholder

needs vary at different stages in the program (Chen, 2005),

identifying GTA needs as they progress from new to experienced

GTA allows for program supervisors to identify and modify program

elements relative to GTA needs.

General Research Questions

1. What are the various viewpoints that exist among Biology

GTAs about their graduate school experiences?

33

2. What are the various viewpoints of the supervisors of

graduate GTAs in The Department of Biology relative to those

of the GTAs?

3. What consensus exists among the GTAs in The Department of

Biology about their graduate school experiences?

4. How do the views differ between new GTAs versus experienced

GTAs?

5. Do the varying views and consensus of GTAs about their

graduate school experiences provide sufficient information

for a needs assessment that informs the existing training

program?

Delimitations

The researcher did not consider the content knowledge held

by the GTAs. A degree in Biology was considered to demonstrate

Biology content knowledge. Demographic information such as race

was not considered important to this study, however age, gender,

graduate status, teaching experience, and nationality may be

considered in the final analysis. The demographic information and

success rate from undergraduate students taught by GTAs was not

34

included in the study. The researcher did not sort with GTAs from

other disciplines.

The various viewpoints obtained in this study are not

considered to be generalizable to different groups of GTAs or

Biology supervisor populations, as Q Methodology results are not

considered to be generalizable to the larger population. Because

this study used Q Methodology as a needs assessment, the study

was exploratory in nature, the viewpoints or typologies uncovered

by this study are not generalizable to larger GTA populations.

Small numbers of participants Q Sorting is not a problem because

the primary purpose is to identify typologies, not to test the

typology's proportional distribution within the larger population

(Valenta & Wigger, 1997). Within this study, the researcher is

solely interested in the GTA population within this department at

this time.

Summary

Because GTAs are frequently used in college classrooms as

the instructors for the course or laboratory, their preparation

for that role is immensely important. Instructional training

programs for GTAs vary across institutions. GTA programs must

35

meet the needs of a diverse population of graduate students. Not

only do GTAs teach, but they are also being socialized into their

potential roles as future faculty and/or researchers. This study

demonstrates how Q Methodology can be used as a needs assessment

tool in a Biology GTA instructional training program. This study

aims to answer the following questions:

1. What are the various viewpoints that exist among Biology

GTAs about their graduate school experiences?

2. What are the various viewpoints of the supervisors of

graduate GTAs in The Department of Biology relative to those of

the GTAs?

3. What consensus exists among the GTAs in The Department of

Biology about their graduate school experiences?

4. How do the views differ between new GTAs versus experienced

GTAs?

5. Do the varying views and consensus of GTAs about their

graduate school experiences provide sufficient information for a

needs assessment that informs the existing training program?

36

CHAPTER II

REVIEW OF THE LITERATURE

The purpose of this chapter is to present a comprehensive

review of the literature related to this study. The literature

review explores the motives and distinguishing characteristics of

graduate students and provides a historical overview of the use

of Graduate Teaching Assistants (GTAs) as instructors of

undergraduates in the university system across The United States.

This chapter also contains a discussion of the shifting nature of

the academic workplace and considers the role of graduate school

as socialization into academia. The details of various types of

GTA training programs are described. Finally, the chapter offers

a deeper understanding of the role of program evaluation in

graduate education, and explains the use of Q Methodology as a

framework for the study.

Why go to graduate school?

Graduate school often gives students a chance to pursue

theories they may hold, gather recognition for their talents, or

upgrade an outdated education (Evans, Forney, Guido, Patton, &

Renn, 2009). Graduate degrees also offer the chance for changing

37

careers, whether out of desire or necessity (Mason, Goulden, &

Frasch, 2009). A graduate degree typically offers students

greater earning power and advancement in their careers (Astin,

1997). Some students enjoy traveling opportunities, teaching

opportunities, and the chance to do original research. Others

attend because they desire to be a part of a research team and to

work on advanced and multifaceted projects (Malaney, 1987). There

are also students who do not know what to do with their

undergraduate degree, and decide to pursue graduate school

because they lack employment opportunities. Interest in

postgraduate study is influenced by psychological and

sociological factors such as parental education, socioeconomic

status (SES), and role models (Betz & Fitzgerald, 1987). Graduate

students may receive free tuition and/or a stipend for being a

GTA. Quite often, graduate students have multiple reasons for

attending graduate school.

Admission criteria vary, but graduate schools and graduate

programs in the sciences generally look for a minimum B average

in upper division work, acceptable performance on the GRE,

favorable letters of recommendation, and evidence of motivation

38

and commitment to graduate study (Smith, 2012). Noteworthy

graduate programs require outstanding faculty with national or

international reputations in research and scholarship. “Critical

masses” of faculty are also necessary for excellence in graduate

education. The best graduate (especially doctoral) programs

include course requirements in other areas. Cross-disciplinary

and interdisciplinary programs offer unique opportunities, and

allow graduate students to advance with combined majors, giving

them a competitive career edge. Graduate students may spend two

to three years in graduate school for a master’s degree, or five

to seven years for a doctorate (Kuther, 2013).

Once students enter graduate school, they are often met with

unique challenges (Golde, 2005). Graduate school is often highly

competitive, and emotionally exhausting (Jacobs & Dodd, 2003). It

may be difficult to prioritize responsibilities when it comes to

teaching, research, studies, and balancing academics with a

personal life (Ward & Wolf-Wendel, 2004; Ward, 1998). There may

be stress in relationships, or due to finances (Mallinckrodt &

Leong, 1992). Writing a thesis or dissertation is extremely

challenging, and may take longer than the student expects

39

(Bowman, Bowman, & DeLucia, 1990; Ohashi, Ohashi, & Paltridge,

2008). Working with advisors or research teams is challenging,

and may make students feel frustrated, overwhelmed, isolated, or

out-of-touch. The student may not be prepared for the specialized

writing demanded for research and publication (Bloom, 1981).

In the sciences, the organizational unit of the “lab” is

critical to understanding life in the departments (Golde & Dore,

2001). Each faculty member sits at the center of a small solar

system—graduate students at various stages and postdoctoral

research fellows orbit around the faculty advisor (often referred

to as the P.I., or Principal Investigator, highlighting the

primacy of research). The faculty member both establishes the

research direction and sustains the group by garnering external

funding for research expenses, stipends, and tuition. This

organizational structure in turn defines a number of key features

of graduate student life. The lab is the site in which research

is carried out. There is an emphasis on knowledge acquisition in

the lab (e.g., through lab meetings, subfield specific journal

clubs, and informal interactions with lab mates) rather than

solely in classes. There is also an expectation that the

40

dissertation research topic relates to, stems from, and feeds

back into the advisor’s research, highlighting the interconnected

nature of the research projects of lab mates. The faculty member

provides the fledgling researcher a topic for research and the

stability of funding for the duration of graduate study (Golde,

2005).

Whether a student persists through a graduate degree program

is a well-studied phenomenon. Girves and Wemmerus (1988) describe

how department characteristics, student characteristics,

financial support, and student perceptions of their relationships

with faculty influence graduate student persistence. After the

initial year, graduate grades, involvement in one's program,

satisfaction with the department, and alienation could contribute

directly to graduate student degree progress (Quist, 2011). There

are distinct and unique challenges to developing graduate

programs that maximize completion rates while still allowing

students to recognize and acquire the skills they will need for

future careers. Not only should GTAs be afforded the chance to

acquire the skills necessary to be successful in academia, but

there also exists the argument that GTAs need certain generic or

41

transferable skills such as public speaking, writing for

different types of audiences, teaching, how to think about

problems and dig into the literature unaided, time-management,

and people-management (Crebert, Bates, Bell, Patrick, &

Cragnolini, 2004; Cryer, 1998; Gilbert, Balatti, Turner, &

Whitehouse, 2004).

The Usage of Graduate Teaching Assistants in Higher Education

Graduate students have not always served as instructors for

courses, leaders of recitations, and laboratory instructors.

During Colonial times in The United States, the student/professor

relationship was often one of the faculty standing “in loco

parentis,” where the faculty not only supervised the student’s

room and board, but his worship, recreation, and his studies

(Bush, 1969).The traditional university model was a religiously-

affiliated clergy preparatory school, modeled after Cambridge and

Oxford in England (Brickman, 1972). As time progressed,

educational models evolved from being centered around a church,

to centered around a library. Thomas Jefferson (1743-1846),

believed educating people was a good way to establish an

organized society. He believed schools should be paid for by the

42

general public, so less wealthy people could be educated as

students (Grizzard, 2009).

The use of GTAs in higher education began in the late 1800s,

as some universities began offering fellowships (stipends offered

to graduate students in exchange for advanced research) in order

to attract graduate students to the institution (Allen & Rueter,

1990). These “research assistantships” were paid positions that

both lessened the financial burdens of graduate school, and

allowed students to do advanced research. Gradually, GTA duties

within the university were expanded. In the 1890’s, GTAs

progressed from research assistants to teaching assistants, and

services (such as grading, role-taking, and recitations) to the

university beyond research were increased to justify the payments

to the graduate students (Drake, 2011). Higher education was

expanding rapidly (Schofer & Meyer, 2005), and filling the role

of “university instructor” with people qualified to lead was

vital to the success of all higher education stakeholders

(Davies, Hides, & Casey, 2001).

43

With the end of World War II in 1945, a rapid influx of

students began attending school on the newly formed GI bill

(Coomes, 2000). A flood of veterans enrolled in America’s

colleges and universities, accounting for approximately 70% of

all male enrollment (Bound & Turner, 2002). The GI bill provided

financial support to veterans wanting to reeducate themselves for

post-war employment (Gelber, 2005). This increase in

undergraduate enrollment demanded professors use graduate

students as assistants to help with more administrative tasks

(Hendrix, 1995). Eventually, graduate assistants shifted from

being simple “assistants,” to teaching basic undergraduate

courses independently. This allowed professors to teach higher

level classes and focus on their research (McKeachie, 1990).

Expanding enrollment demanded an increasing number of

instructors, and rather than trying to find faculty that did not

yet exist, universities hired flexible graduate students

(Burmila, 2010).

This pivotal time in American history was monumental for

higher education. Sidney Burrell (1967) concludes that the G.I.

Bill led to “what may have been the most important educational

44

and social transformation in American history” (p. 3). The G.I.

Bill allowed a more diverse population of students to attend

college due to financial assistance, making college a viable

option for men from a range of socio-demographic backgrounds,

including minorities, first-generation Americans, and those from

low-income households (Bound & Turner, 2002). Colleges and

universities needed instructors for this flood of new

undergraduate students, and they needed them immediately. While

the GTA was an innovative approach to meeting the demands of an

ever-expanding undergraduate population, many GTAs were

un(der)prepared to teach – knowing little (if any) of good

instructional practice, how to deal with students unlike

themselves, and curriculum development.

Teaching “Assistant” or Course Instructor?

There is often a disconnect between GTA knowledge and

preparation, and their prioritization of teaching and researching

(Hendrix, 1995). Many students’ primary focus is research, rather

than instruction (Butler, Laumer, & Moore, 1993; Serow, 2000).

Between the 1930s and 1960s, the idea of training GTAs in

pedagogy gained support, as more institutions began focusing on

45

the need for their graduate instructors to be able to function

successfully in the college classroom (Drake, 2011). GTAs could

serve as the sole instructor for one or more classes a semester

(Butler et al., 1993) or as the instructor of laboratory or

discussion sections (Luft et al., 2004; Travers, 1989).

Administrators of university programs felt that GTAs should not

only show content mastery, but be able to teach that content

effectively. At some universities, equal preference was given to

graduate students who could demonstrate instructional

capabilities as well as research competence (Butler, Laumer, &

Moore, 1993).

GTAs today are being utilized by colleges and universities

to teach a variety of courses, in a variety of fields (Buerkel-

Rothfuss & Fink, 1993; DeBoer, 1979; Marting, 1987). They now

commonly assume the teaching roles that once only faculty

performed (Branstetter & Handelsman, 2000). GTAs are often

expected to prepare and grade exams, write their own syllabi,

design the course curriculum, order textbooks, prepare and

present lectures, monitor student progress, and assign final

grades, all with minimal faculty supervision (Mueller et al.,

46

1997; Nyquist et al., 1989). In addition to the academic

responsibilities that GTAs assume, they are also called upon to

hold office hours (Mueller et al., 1997), which typically

involves assuming an advising role - guiding undergraduate

students on topics such as mastery of course material, academic

concerns, applying to graduate school, and even counseling

students through personal problems (Moore, 1991). As instructors

of undergraduates, GTAs are not merely teaching “assistants.”

They must make instructional, curricular, and assessment

decisions in their courses (Luft et al., 2004). They assume the

role of professor, not apprentice (Burmila, 2010) – and they face

unique challenges in this role.

Amidst the ever-present fiscal restraints, limited or no-

growth policies, and unpredictable enrollment in universities

nationwide, funding setbacks have further expanded the reliance

on GTAs for undergraduate education (Koocher & Keith-Spiegel,

2008). They play a prominent role in undergraduate science

education in most large research-oriented universities and

colleges in the United States by instructing the majority of the

introductory laboratories and discussion sections (Travers,

47

1989). Perkinson (1996) asserted that GTAs spend more time in the

undergraduate classroom than do full-time faculty. Because of age

and status similarities, undergraduate students frequently relate

more strongly with GTAs than they do with professors (Hendrix,

1995; Moore, 1991). In addition, research has suggested that

educators who have the most impact on students are those with

whom students identify and have more out-of-classroom interaction

(e.g., (Gaff & Gaff, 1981). And, because of wavering

undergraduate and graduate enrollments, the need for new

instructors cannot always be met with new faculty hires. GTAs

allow for flexibility that is crucial in meeting oscillating

demand (Burmila, 2010). As GTAs play an increasingly significant

role in not just teaching, but in advising and mentoring

undergraduates, it is important to consider how this multifaceted

socialization impacts GTA development as graduate students and

future academics.

Instructional Training Programs for GTAs

Training Biology GTAs for the multiplicity of roles expected

of them in the academic community - graduate student, instructor,

advisor, fledgling researcher – is complex (Bhavsar et al.,

48

2007). Biology faculty are not simply preparing future research

Biologists, they are prepping GTAs to meet the challenges of

multiple roles – researcher, teacher, and academic. These

challenges are felt by all disciplines. Departments that

compartmentalize GTAs with only specialized disciplinary

knowledge are not adequately preparing them for the possible

careers they could hold outside of academia (Loughran, Mulhall, &

Berry, 2004). Supervisors of GTA professional development

programs have to prepare GTAs to teach undergraduate students who

may be nothing like themselves (Howard, Buskist, & Stowell, 1993;

Meitl, 2008), or who may be taking a general education course and

display no interest in the GTAs’ field. With so many stakeholders

in GTA success, the question of “who bears the responsibility of

preparing GTAs to teach” is a complex problem.

The first organized effort to provide this much-needed

instructional training for GTAs began in the 1930s with English

instructors at the University of Chicago's Institute for

Administrative Offices (Marting, 1987). This program was

developed because of complaints about the inept instructors

emerging from the graduate school, who needed further pedagogical

49

training in their content areas (Marting, 1987). It was then that

the Institute's members decided that content mastery alone was

not enough to produce effective teaching assistants - pedagogical

training was needed. Likewise, calls for training programs for

teaching assistants in the sciences (Carroll, 1980; Luft et al.,

2004), and more specifically in biology (Rushin et al., 1997;

Tanner & Allen, 2006) have created a continual demand for

pedagogical training, in addition to content area mastery.

Science graduate students have reported the most interest in

teaching amongst all GTAs. They display the most confidence in

their ability to teach and advise students, in comparison with

their peers from other disciplines (Luft et al., 2004). A survey

by Golde and Dore (2001) of over 4000 doctoral students at 27

universities clearly documented that graduate students in the

sciences reported holding more teaching assistantships than did

their peers in other disciplines. However, these assistantships

often consisted of limited placements, usually in laboratory

settings for a defined amount of time. Despite teaching more

courses, only a third of the graduate students in the sciences at

most universities indicated they had participated in a teaching

50

assistant (GTA) training session to prepare them for their

teaching duties.

Graduate students who are not adequately prepared to engage

in teaching activities may have an inflated confidence in their

abilities (Golde & Dore, 2001; Rhodes, 1997). To assist graduate

students in becoming proficient instructors, they must be given

quality opportunities to develop their teaching skills,

abilities, and knowledge with the same guidance and practice that

is afforded to the development of a quality researcher (Golde &

Dore, 2001). However, because teaching is often regarded as a

second-tier profession in academic settings, graduate students in

the sciences may experience limited educational environments

(Luft et al., 2004). It is well documented that an emphasis on

teaching is viewed as a secondary career in many academic

settings, such as in community colleges, at for-profit

institutions, or as an adjunct instructor (Shannon et al., 1998).

GTAs in the sciences commonly regard teaching as a “fallback

career,” only to be embarked upon after a student fails to obtain

a research position (Richardson & Watt, 2006).

51

GTAs may perceive teaching as a highly demanding career

having a heavy workload, high emotional demand (Hendrix, 1995),

anxiety-provoking, and generally requiring hard work (Deiro,

1996; Rhodes, 1997). At the same time, they may also perceive

teaching as relatively low in social status, paying a low salary,

and reported experiences of quite strong social dissuasion from a

teaching career (Rhodes, 1997; Watt & Richardson, 2008). In

addition, teaching assistantships are awarded on the basis of

academic potential, not teaching potential (DeBoer, 1979). Being

thrust into an instructional role that they feel unprepared for,

uncertain about, or even resentful of, is not ideal for either

graduate students or their students (Hendrix, 1995). No matter

what the perceptions of teaching GTAs hold, faculty who mentor

and supervise GTAs have a duty to prepare future science

instructors (Gardner, 2010b; Rosen & Bates, 1967).

Instructional training necessitates an ongoing series of

professional development courses that span GTAs’ graduate school

careers, rather than a one time, simple orientation. As Prieto

(1995) notes, less than half of all GTAs receive any type of

supervision on an ongoing basis. As Palmer (1993) notes, "we

52

would be better teachers if we had one simple thing: a rich on-

going discourse about teaching and learning, not the perfunctory

annual teaching-development workshop, but a community of

discourse that triangulates...from the many different angles

available from within the life of the faculty itself" (p. 9).

Rather than learning to become proficient researchers with

pedagogy as an additive, GTAs need to learn how to become

exceptional teachers and use research to enhance their teaching

and teaching to enhance their research (Rhodes, 1997). Training

can provide a safe environment to discuss alternative ways of

handling problems that may arise in and outside of the classroom

(Andrews, 1983). Directors or supervisors of these programs may

act as "emotional mentor" by offering emotional support and

providing models of emotional display when GTAs are in the

process of shaping their own personal feeling rules. Supervisors,

peers, and training in general can provide a supportive community

(Rhodes, 1997).

Graduate School and the Socialization of Academics

Socialization in graduate school refers to the process

through which individuals gain the knowledge, skills, and values

53

necessary for successful entry into a professional career

requiring an advanced level of specialized knowledge and skills

(Gardner, 2005; Weidman et al., 2001). Socialization is also

described as the process through which an individual learns to

adopt the values, skills, attitudes, norms, and knowledge needed

for membership in a given society, group, or organization

(Braxton, Lambert, & Clark, 1995; Merton, 1968; Tierney, 1997;

Van Maanen, 1976). Graduate schools aim to provide graduate

students with knowledge of research concerning the subject matter

in their fields, and to make certain that these students can

independently demonstrate the research skills of their chosen

field (Bess, 1978). Preparing GTAs to assume the types of

instructional roles and responsibilities of faculty members is an

equally integral part of graduate school (Nicklow, Marikunte, &

Chevalier, 2007). Bess (1978) argues that “since the source of

college faculty is the graduate school, one way to generate

faculty with these orientations [skills] might be through changes

in graduate education.” Faculty members play a myriad of roles in

the socialization of doctoral students, including instructors in

the classroom, supervisors for students with assistantships,

54

committee members for the thesis or dissertation, advisor or

chair of the research process, and even mentor (Isaac, Quinlan, &

Walker, 1992; Pease, 1967; Weidman & Stein, 2003). In this way,

faculty members serve as gatekeepers into and out of doctoral

programs (Weidman et al., 2001).

Golde (2002) described the process of graduate school

socialization as one “in which a newcomer is made a member of a

community—in the case of graduate students, the community of an

academic department in a particular discipline” (p. 56). She

continued, “The socialization of graduate students is an unusual

double socialization. New students are simultaneously directly

socialized into the role of graduate student and are given

preparatory socialization into a future career in academia” (p.

56).

Graduate students are also being immersed in the culture of

the discipline. Borrowing from Merton (1968), Tierney (1997)

stated, “Culture is the sum of activities in the organization,

and socialization is the process through which individuals

acquire and incorporate an understanding of those activities” (p.

4). He continued, “An organization’s culture, then, teaches

55

people how to behave, what to hope for, and what it means to

succeed or fail. Some individuals become competent, and others do

not. The new recruit’s task is to learn the cultural processes in

the organization and figure out how to use them” (p. 4). The

values, attitudes, and beliefs of the culture, in this case, the

academic culture, are often dictated by the discipline itself.

Disciplines have their own particular qualities, cultures, codes

of conduct, values, and distinctive intellectual tasks (Becher,

1981), which ultimately influence the experiences of the faculty,

staff, and students involved. Becher and Trowler (1989, p. 44)

underscored this point: “We may appropriately conceive of

disciplines as having recognizable identities and particular

cultural attributes.” In order to navigate a Biology department,

GTAs must acquire an understanding of what the department members

value, what faculty attitudes are towards the various activities

the GTAs will participate in, and the beliefs shared by the

department (Rushin et al., 1997). The GTA must quickly learn

which undertakings will help them persist in the field, and which

activities deserve less attention. In the “publish or perish”

world of academia, research and grant-obtaining are highly

56

prized, while teaching does not carry as many easily identifiable

rewards (Breen, Brew, Jenkins, & Lindsay, 2004; Sonnert, 1995;

Vannini, 2006). Research expectations for university faculty are

so valued that research productivity has become the dominant and

sometimes the sole criterion for hiring, tenure, and promotion at

research universities (Prince, Felder, & Brent, 2007; Rushin et

al., 1997).

Tierney and Bensimon (1996) suggest that the graduate school

experience acts as an agent of anticipatory socialization as the

graduate student begins to understand the role of faculty.

Doctoral students observe faculty and the activity of the

academic department and subsequently form attitudes and opinions

about life as an academic. As students assume their roles as

teaching assistants, they have some insight into the work roles

of faculty members and how to perform in those roles (Weimer et

al., 1989). They are also attempting to “fit in” to their new

environment based on the disciplinary norms of their chosen field

of study (Weidman et al., 2001). What anticipatory socialization

does not account for is the changing career trajectories of GTAs.

Though Biology GTAs may be able to see themselves stepping into

57

the role of research university faculty, they may not be able to

see themselves stepping into the role of community college

instructor, adjunct, non-tenure track faculty, or liberal arts

instructor.

New graduate students must investigate their place in the

organization in order to glean the necessary attributes that are

important to the existing members (Tierney, 1997; Weidman &

Stein, 2003; Weidman et al., 2001). Newcomers or novices within

the academic setting must make sense of their new roles and begin

to conform to the “normal behavior” as exhibited by those around

them (Tierney & Bensimon, 1996). In attempting to conform to

academic surroundings, the graduate student is forced to make

decisions as to which aspects of the graduate school process

assist the individual in socialization. Failure to understand the

priorities in academia may result in a negative experience while

in graduate school, which may contribute to a negative experience

when pursuing a faculty career (Tierney & Bensimon, 1996;

Tierney, 1997). After what may be a long, difficult process of

attempting to find a tenure-track position, multiple rejections,

and ultimately accepting a position outside academia, the

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socialization process must include generic or transferable skills

that help graduate students to be successful in multiple types of

careers, not just academia (Crebert et al., 2004; Gilbert et al.,

2004; Stoner & Milner, 2010).

Over 1 .5 million graduate students were enrolled in

graduate programs, including students pursuing both master’s and

doctoral degrees, in 2005 (Brown, 2005), as compared to 1.73

million graduate students today (Rampell, 2012). As the number of

graduate students pursuing Ph.D.'s increases, academic job

prospects are diminishing. Indeed, the number of students

receiving doctorates in biology increased from 3,803 in 1981 to

8,135 in 2011, while the number of biological-science Ph.D.

recipients in tenure-track positions dropped precipitously from

55 percent in 1973 to 15 percent in 2006. Thus, a large majority

of students are being trained for faculty positions they will

never obtain (Shea, 2013). American Society for Cell Biology

President Ron Vale (2013) wrote a column suggesting that an

acceptable, if not good, alternative career for science Ph.D.'s

is to become elementary- or secondary-school science teachers.

Ph.D. programs have not prepared GTAs to be elementary or

59

secondary school teachers, however. Going this route often

involves working in private or charter schools that do not

require certification, obtaining an emergency certification for

an area of need, or a program like “Teach For America (Berliner,

2002; Darling-Hammond, 2005; Decker, Mayer, & Glazerman, 2004).”

Other suggested career options besides academia or teaching

have included science policy, start-up businesses, science

communication/writing, nonprofit work, science publishing, patent

law, technology transfer, and consulting (Columbia University,

2013). Institutions and departments are slow to change. Even

though it is widely recognized that GTAs need additional training

and that their chances of becoming a Biology faculty member are

slim, they are not being prepared for alternative careers.

Virtually all graduate students receive their Ph.D.'s from a

research university (Cassuto, 2011). They get their first

classroom experience there, and their dissertations are mainly

guided by professors whose research occupies a prominent place in

their work lives. The graduate student works his or her way from

outsider to the profession, to full member, under the mentorship

of an advisor (Filstad, 2004). But because most academic jobs

60

aren't at research universities, those other jobs look jarringly

different to graduate students than the positions held by their

mentors (Cassuto, 2011). Developing training programs that

recognize the importance of communication skills, transferrable

skills, the scholarship of teaching, and student success as

pivotal and investment-worthy, while not sacrificing the research

component of a GTA program, are acknowledged as integral to GTA

professional development (Boyer, 1991; Kreber, 2001, 2005; Tulane

& Beckert, 2011).

While many posit the purpose of doctoral education to be the

preparation to conduct original research (e.g., (Council of

Graduate Schools, 1990), others contend that Ph.D. programs

should be further reaching, including training to teach (Adams,

2002; Gaff, 2002a) and skills necessary for the labor market

outside of academia (Atwell, 1996; Golde & Walker, 2006; Jones,

2003). The Council of Graduate Schools (2004, p. 4) clearly

delineated the independent nature of doctoral education: “Beyond

some beginning course work, the experience of each Ph.D. student

is individualized and varied. Ph.D. students bear a greater

responsibility for defining the scope of their educational

61

experience than do other students. Further, the degree requires

initiative and creativity, and the award of the degree depends

upon the individual performance of a student in completing

original research in the area of study.” The purpose of graduate

school, therefore, is a combination of what the graduate school

offers, and what graduate students view as their needs.

Supervisors of GTAs could alleviate some of their anxiety by

providing a clear picture of what previous GTAs in their

department, university, or discipline have struggled with, and a

tool to help them recognize how their own preconceptions will

shape their education. A successful GTA program should empower

graduate students to maximize their strengths and correct their

weaknesses.

Some might suggest that it’s up to the discipline to decide

what an advanced degree means. Institutional context and culture

uniquely influence the student experience (Kuh & Whitt, 1988).

Perhaps only a Biology Department can attest to the

characteristics of its master’s or doctorate holders (de Valero,

2001; Ehrenberg, Jakubson, Groen, So, & Price, 2007; Weidman &

Stein, 2003). While a Master’s Degree in Biology usually involves

62

two years of coursework and a thesis, a Ph.D. in Biology usually

involves a similar amount of coursework and an independent

research project demonstrating expertise in the field. A Ph.D.

may take four to eight years to complete (Kuther, 2013). By the

culmination of their graduate school career, GTAs should “know

what to do” when it comes to teaching, students, and research in

their given discipline (Luft et al., 2004).

In the four to eight years graduate students spend in

graduate school, under the guidance of their faculty advisor,

GTAs should be given the opportunity to improve on their

teaching, but a “sink or swim” philosophy is often employed

(Friedrich & Powell, 1979; Myers, 1998; Russell, 2011; Trowler &

Kreber, 2009). While academic advisors may provide guidance to

graduate students, they may also serve as a negative example of

faculty lifestyles (Austin, 2002). Over half of all doctoral

students in the sciences drop out in their first year, due to

poor career outlooks, being a bad fit with a disciplinary

department, or conflicts with advisors (Golde, 2002). Theories of

socialization have been connected to the issue of attrition in

doctoral education, with researchers often attributing poor or

63

inappropriate socialization to a student’s decision to depart the

graduate program (Clark & Corcoran, 1986; Ellis, 2001; Gardner,

2007; Golde, 1998; Lovitts, 2001). As newcomers to graduate

school, the institution, the department, and the laboratory, the

process is inherently anxiety-producing, and the support offered

to the GTA varies greatly (Gardner, 2007, 2008).

Conflicting Priorities in a Graduate School Program

Holding a teaching assistant position may help graduate

students pay for graduate school (Austin, 2002); however,

graduate students may be told by their advisors that research

should be their focus, and that teaching assistantships should

not be held for multiple years because this will jeopardize their

careers (Jones, 1993). In the sciences, graduate students

recognize the prestige of a research assistant position, and note

that a teaching assistant position holds less value (Fox, 1983).

In Serow’s (2000) study of faculty at research institutions, one

natural scientist said, “anyone not doing the right type and

amount of research would “never be accepted as a legitimate,

card-carrying member of the faculty.” This culture in which GTAs

exist places them in a situation that is wrought with tension and

64

difficult to change (Luft et al., 2004). GTAs may enjoy teaching

and perceive this work as important but may feel that their

interest in teaching does not contribute to their overall

professional development as scientists (Ethington & Pisani,

1993). A report published by the Association of American Colleges

maintains that, "Unless the reward system in higher education

measures teaching performance as well as research, all efforts to

improve college teaching will be to no avail" (1985, p. 37).

At this juncture, GTAs are surrounded by a myriad of

conflicting viewpoints, which may affect their desire and ability

to persist in their graduate programs (Tinto, 1991). As a

student, GTAs come to graduate school seeking to increase their

content and disciplinary knowledge. As teaching assistants, they

may feel unprepared to teach (Boice, 1991), uncertain about the

role of teacher (Svinicki, 1994), and stressed about their future

careers (Sorcinelli, 2006). As a researcher, they are looking to

their faculty advisor for guidance on navigating the university,

working with grant-funding agencies, or departmental politics.

With all of these (sometimes) conflicting interests, determining

which priorities gets the time and attention by the GTA is a

65

difficult decision. Tinto (1991, p. 110) suggests that graduate

persistence is "shaped by the personal and intellectual

interactions that occur within and between the students, faculty,

and student-faculty communities that make up the academic and

social systems of the institution.” Graduate programs may be

described by GTAs with feelings of “family’ or ““camaraderie,” or

conversely, feelings of isolation, ambiguity, and feeling lost

(Gardner, 2010a).

Despite the conflicting priorities GTAs express, the

institutional graduate program has multiple stakeholders invested

in the success of GTAs – the undergraduate students who are being

taught by them, the advisors who have included them in their

research and may serve as mentors, the graduate schools who want

a successful graduate program, and the universities who are

looking to GTAs as current students and future faculty (Coll,

Zegwaard, & Hodges, 2002; Duchelle et al., 2009; Enz, Renaghan, &

Geller, 1993). GTA training programs are being influenced by a

number of interested parties, and depending on who the programs

are being run by, may include a variety of components (Aubel,

1995). Academic departments have a stake in GTAs, both as

66

researchers and as potential future faculty, students have a

stake in the effectiveness of their instructors, and the

institution itself has a stake in completion rates. While

programs that provide training to GTAs have proliferated and the

literature surrounding GTA development has increased, models and

designs for best practice of these training programs remains

varied (e.g., Barrus, Armstrong, Renfrew, & Garrard, 1974; Clark

& McLean, 1979; Druger, 1997; Lawrence, Heller, Keith, & Heller,

1992; McComas & Cox, 1999; Nyquist & Wulff, 1996).

Descriptions of programs range from half day university-wide

orientation sessions that introduce new GTAs to university

policies but provide no departmental training, to multiday

university-wide training, department-specific training, or even

university-wide training coupled with full-semester courses and

seminars on teaching methods offered by specific departments

(Rushin et al., 1997). As departments or graduate schools weigh

the evidence for creating their own organic GTA training programs

or choosing one of the national GTA training programs, they must

know that stakeholder needs are being met by the program. The

supervisors of these programs must modify or replace programs

67

that do not meet GTA needs. Supervisors first must know what the

needs of the GTAs are.

National Training Programs vs. Locally Developed Training Programs

There are a series of large-scale projects, funded by

charitable foundations, which have reviewed the Ph.D. degree and

stimulated considerable activity for reform of the doctoral

curriculum. These projects include “Re-Envisioning the Ph.D.,”

developed at the University of Washington (Nyquist & Woodford,

2000), the “Preparing Future Faculty” project from the

Association of American Colleges and Universities and the Council

of Graduate Schools, 2002 (Pruitt-Logan et al., 2002), the

“Responsive Ph.D.” project, developed in the Woodrow Wilson

National Fellowship Foundation (Weisbuch, 2004), and the

“Carnegie Initiative on the Doctorate” developed by the Carnegie

Foundation for the Advancement of Teaching (Golde & Walker,

2002). These projects focus broadly on improving the outcomes of

Ph.D. degree programs (Gilbert, Balatti, Turner, & Whitehouse,

2004). There are challenges inherent in large, national, grant-

funded programs such as PFF. The program may not meet the needs

of GTAs locally. It may spend time reinforcing skills that GTAs

68

already possess, or that doesn’t fit the content area. A first

year graduate student, and a fourth year graduate student

certainly have different skill sets. An English GTA certainly has

different challenges than a Biology GTA. While departments may

feel ownership over their own, organically grown GTA programs,

they may be resentful of the time unwieldy national programs

demand. In order to maintain a training program, the needs of

all the stakeholders in the program must be heard and addressed.

One national program that focuses specifically on

instructional training in multiple institution types is the

Preparing Future Faculty (PFF) program (DeNeef, 2002). This

program involved 43 doctoral-granting institutions and 295

partner institutions that worked in clusters. The lead campus

established relationships with institutions in different higher

education sectors - community colleges, liberal arts colleges,

master’s degree granting institutions, public institutions, and

private institutions. The clusters of institutions offered an

opportunity for graduate students to learn about the various

roles and responsibilities of a faculty member. Offerings for

graduate students include meeting with teaching mentors,

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attending seminars about teaching, participating in extensive

programs designed to enhance instruction, and observing

outstanding instruction by senior faculty. Ultimately, PFF

designers make a conscious effort to prepare GTAs formally as

teachers.

The Council of Graduate Schools (CGS) and The Association of

American Colleges and Universities (AAUC) both promoted the PFF

program, and the using of best practices in the graduate school

education of GTAs. However, once the funding for the PFF programs

ended in 2010, few institutions continued the program in its

entirety (Newton, Soleil, Utschig, & Llewellyn, 2010). Reports

about PFF suggest that graduate students in the nation’s Research

I universities see their faculty mentors as not only generally

unsupportive of their desire for more pedagogical training, but

even antagonistic to such training, since the faculty assumption

has been that they are really preparing people for research

positions just like their own. Results from 1998 and 2001 surveys

of graduate students who had participated in the Preparing Future

Faculty program highlight this issue; one student illustrates

faculty’s negative attitudes towards non-research intensive jobs

70

by stating, “if you get a job at a liberal arts school, that’s

your failure rather than your success (DeNeef, 2002).” Even as

the chances of a GTA getting a research position as a faculty

member at a university decline, these coveted positions are also

transforming (Edgerton, Rice, & Chait, 1997; Finkelstein, Seal, &

Schuster, 1998; Finkelstein, 2006; Schuster & Finkelstein, 2006).

The Modern Academic Workplace

In addition to stepping into accepting professorial

responsibilities, “the modern academic workplace” is

characterized by student diversity, new technologies, changing

societal expectations, expanding faculty workloads, a shift in

emphasis toward the learner, and a new labor market for faculty

(Austin, 2002). The traditional full time, tenure-track, faculty

position that graduate students once strove for, as the

culminating point of their course of study, is no longer the

norm. The AAUP (American Association of University Professors)

reported (“Tenure and Teaching-Intensive Appointments,” 2007),

that almost 70 percent of faculty members were employed off the

tenure track, in part-time or non-tenure-track, full-time

teaching positions. Graduate programs continue accepting more

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graduate students than can possibly obtain tenure-track faculty

positions in academia (Berrett, 2012). Based upon these

statistics, graduate students are faced with the facts that they

may be doing everything right – conducting research, publishing

in prestigious journals, writing grant proposals, serving as a

GTA, teaching, serving on departmental committees – and still may

not obtain a tenure track job.

Whereas Biology GTAs of the past may have aspired only to a

Biology research-focused, tenure-track position at a

R1university, their new job prospects may include part-time

teaching, an instructor position, laboratory coordinator,

community college instructor, for-profit school, or a job for

which their Biology research credentials are less important than

their Biology instructional skills (Fleet et al., 2006). Biology

GTAs, in their position in graduate school, have a Bachelor’s

degree in Biology, and are being prepared to articulate clearly

their knowledge, and communicate it to students (Boyer, 1991).

Having a degree in Biology is not a guarantee that a GTA has

effective communication skills. Oral communication, a skill GTAs

will use extensively in their future career, is laden with

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contextual motivations, purposes, audiences, and strategies

speci c to each eld of inquiry fi fi (Dannels, 2002). Training

programs for GTAs must assess whether or not they can communicate

effectively within their discipline. Assessment practices that

evaluate the extent to which students achieve the communication

outcomes determined by certain disciplines to be valued, salient,

and relevant, must be developed (Dannels, 2001).

Evaluating Graduate Teaching Assistant Training Programs

The experiences of science doctoral students are unique and

complex and are influenced by multiple communities including the

discipline, institution, department, lab and advisor. Each

community may offer different types of support to the student at

different junctures of the doctoral journey (White, Nonnamaker, &

Smith, 2008). What works for one department, one college, one

university, or nationally may not be appropriate for another

particular cohort of GTAs. Couple these unique circumstances of

GTAs with the rapidly changing career opportunities, training

that occurred ten years ago may not meet the needs of GTAs today.

This juncture is where program evaluation is critical. Program

evaluation ought to be an intrinsic part of any program or

73

project because it is used to both measure the effectiveness of

that program or project, as well as investigate ways to increase

that effectiveness (Newman & Ramlo, 2011). In order to

effectively evaluate the components of a GTA instructional

training program, there must be a baseline for comparison (McNeil

et al., 2005).

The real test of the value of a program in a location is the

implementation and evaluation of a program in that location (McNeil

et al., 2005). Gredler (1996, p. 15) defines program evaluation

as a “systematic inquiry designed to provide information to

decision makers and/or groups interested in a particular program,

policy, or other intervention.” Program evaluation may also be

described as “an ongoing, collaboratively designed, and

stakeholder-led evaluation process that has the primary purpose

of serving organizational learning by evaluating the whole logic

model” (York, 2005, p. 8). Carroll (1980, p. 179) notes in his

review of the research surrounding GTA training programs, that

“programs should be structured to encourage the participation of

experienced, senior GTAs who can share their insights and

experiences with the novice GTAs.” Also, stakeholders should

74

insist on continuing evaluation of the training programs they

administer or support (Rossi, Lipsey, & Freeman, 2004). Since the

benefits to the department or institution could vary from one

cohort of GTAs to another, it is important that program

evaluation be conducted regularly (Carroll, 1980). While there is

literature on best practices for GTA instructional training

(Meitl, 2008), the literature on programs for training GTAs that

blend best practices with the needs of the particular cohort of

GTAs is absent.

Just as one size does not fit all in undergraduate teaching,

curriculum, instruction, and textbooks, neither does one size fit

all in training graduate GTAs. Not only will there be nuances in

subject matter by departments, there is no “one right way” of

teaching. No single view of learning or teaching dominated what

might be called “good teaching.” There have been five documented

perspectives on teaching, each with the potential to be good

teaching: transmission, developmental, apprenticeship, nurturing,

and social reform (Pratt, Boll, & Collins, 2007). There are

qualitative, quantitative, and mixed method approaches in

understanding instructional training (Creswell, 2008; Johnson &

75

Christensen, 2007). There are large scale, national GTA training

programs, and there are small, department created programs, along

with “no program at all” being a possibility (Christensen,

Alexander, Nelson-Laird, & Robinson, 2011; Gaff, 2002a; Nyquist

et al., 1989). Just as no two undergraduate students will be

exactly alike, neither will two graduate students. GTAs enter

school with varying degrees of experience, prior teaching,

experiences with students, approaches to diversity, and

motivation to persist in their programs. Understanding the

various viewpoints of GTAs serves as an important needs

assessment, which establishes a baseline starting point for their

instructional training.

Needs assessment is the first stage in the General

Evaluation Model (GEM) of program evaluation (McNeil et al.,

2005). Needs assessment is the process of collecting, from all

the stakeholders, information that indicates the nature of the

program. The information is the discrepancy between what should

be, and what is. The program is then designed to eliminate the

discrepancy between what is, and what should be (Altschuld &

Witkin, 1999). A needs assessment typically includes eight tasks:

76

1. Identify stakeholders

2. Identify program areas

3. Identify sources of information

4. Develop a needs assessment instrument

5. Conduct the needs assessment

6. Write the needs assessment report

7. Disseminate to the stakeholders

8. Make sure the stakeholders buy into the program (McNeil

et al., 2005, p. 30)

The General Evaluation Model (GEM) (McNeil et al., 2005),

allows for the evaluation of strengths and weaknesses in a

program. The GEM is composed of five stages that are sequenced

and form a feedback loop (Figure 2). The five stages are needs

assessment, baseline, procedures to achieve objectives, program

implementation, and post assessment. This research uses Q

Methodology as a needs assessment tool to identify the variety of

77

viewpoints of GTAs as they either begin their professional

development program, or have completed their professional

development program. Including the various GTA viewpoints as a

starting point for program

evaluation provides the unique opportunity to tailor GTA

professional development to best meet the various needs of

stakeholders. This crucial step of conducting a needs assessment

in GTA instructional training programs is what is often missing

from the literature.

Numerous instruments were examined to identify the needs of

GTAs in their professional development/training programs. There

was only one instrument specific to GTA development, which was

developed by Cho et al. (2010) from an earlier survey called “The

Teacher Concern Checklist (Borich & Fuller, 1974). This survey

was intended “to capture to what extent GTAs, faculty, and

undergraduate engineering students rate the importance of typical

GTA roles and responsibilities. “ The Likert-style survey

included 46 items, which were later grouped into four categories.

The four categories were 1) GTA preparation, 2) Instructional

Practices, 3) Engagement with Students, and 4) Classroom

78

Management. To complete the survey, participants were asked to

read each statement and ask themselves, “When I think about

teaching, am I concerned about this? “As in the original Teacher

Concerns Checklist, a 5-point Likert-style response scale (1-Not

Concerned through 5- Highly Concerned) was used. Statements

included items such as “Having too many students in a class,” or

“Whether the students respect me.” McKeown (2001) has suggested

that Likert-style surveys may lead to a loss of meaning, as in

this case, where choosing “Highly concerned” or “Not Concerned”

has no real meaning to GTAs. McKeown also suggests that Likert-

style scales “fail to account for respondent intent and

interpretation of scale items and imposing a priori meanings

external and prior to the respondents’ actions on the scale.”

GTAs could answer every question with “Highly concerned.”

Answering “Highly concerned” on one statement had no relation to

answers on other statements. This study may not have captured the

subjective views of the GTAs under study.

Other instruments evaluated for use as a needs assessment

for K12 teachers, but not specifically for GTAs, included The

Learning Styles Inventory (LSI) (Renzulli & Smith, 1978), The

79

Teaching Goals Inventory (TGI) (Angelo & Cross, 1993), The

Teacher Efficacy Scale (TES) (Gibson & Dembo, 1984), The Self-

Efficacy Toward Teaching Inventory (SETI) (Prieto & Altmaier,

1994), and The Inventory of College Students' Recent Life

Experiences (ICSRLE) (Kohn et al., 1990). Inventories for

secondary school teachers would not provide appropriate data as a

needs assessment for GTAs, however, because the many factors are

different between secondary teachers and GTAs, such as motivation

to work with young people, serving society, fulfilling a

professional commitment, satisfaction with the subject area,

intellectual challenges, and the opportunity to be creative

(Brunetti, 2001; Marston & Brunetti, 2009; Marston, 2010). None

of these inventories provided the data needed to evaluate GTA

viewpoints, or were able to be modified to provide the data

needed. In order to provide an accurate portrayal of the

perspectives of GTAs enrolled in the “Effective Teaching” course,

a Q Methodology instrument was developed.

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Q Methodology

Developed by psychologist William Stephenson in the 1930’s,

Q Methodology, also called the Q Sorting technique, or simply Q,

allows researchers to identify, both quantitatively and

qualitatively, the various opinions within a group and the number

of people within the group who hold these opinions (Brown, 1993;

Ramlo, 2008). Stephenson, an English physicist and psychologist

who criticized psychometrics, revealed Q Methodology in a letter

to Nature in 1935 and later described it specifically as a way

to scientifically measure subjectivity (Stephenson, 1953). Q

Methodology is an appropriate choice whenever a researcher wishes

to determine the various perspectives and consensus within a

group regarding any topic (Ramlo, 2008).

Stephenson was critical of the available tests that measured

behavior. A crucial premise of Q is that subjectivity is

communicable, because only when subjectivity is communicated,

when it is expressed operantly, it can be systematically

analyzed, just as any other behavior (Stephenson, 1953, 1968). If

each individual would have her/his own specific likes and

dislikes, Stephenson (1935) argued, their profiles will not

81

correlate; if, however, significant clusters of correlations

exist, they could be factorized, described as common viewpoints

(or tastes, preferences, dominant accounts, typologies, et

cetera), and individuals could be measured with respect to them

(Van Exel & de Graaf, 2005).

Although Q Methodology uses numerical classification, it is

a mixed methods approach because it also uses qualitative

research techniques (Newman & Ramlo, 2010; Stainton Rogers &

Rogers, 2004). Q Methodology provides the researcher a systematic

and rigorously quantitative means for examining human

subjectivity by encompassing a distinctive set of psychometric

and operational principles that are coupled with specialized

statistical applications of correlational and factor-analysis

techniques (McKeown & Thomas, 1988, p. 7). Q Methodology has been

discussed qualitatively, with a focus on subjectivity and self-

referential meaning (Brown, 2008; Watts & Stenner, 2005) but also

has been designated specifically as a quantitative method,

focusing on factor analysis and interpretation (Block, 2008;

Brown, 2008; McKeown & Thomas, 1988; Nunnally, 19780. One of the

reasons Q Methodology is attractive to educational researchers is

82

because of its position in the mixed-methods continuum (Newman &

Ramlo, 2010, 2011; Ramlo & Newman, 2011).

Brown (1993) describes the Q Sorting process as, “most

typically, a person is presented with a set of statements about

some topic, and is asked to rank-order them (usually from ‘agree’

to ‘disagree’), an operation referred to as ‘Q Sorting.’ The

statements are matters of opinion only (not fact), and the fact

that the Q Sorter is ranking the statements from his or her own

point of view is what brings subjectivity into the picture.” Q

Sorting may be used in a single case study, where a single

respondent is asked to sort the sample of statements under

multiple conditions of instruction, or Q Sorting may be used to

with groups and then statistically analyzed. Statistical analysis

leads to correlation and factor analysis that exposes patterns of

findings within the group (Brown, 1980) .

One differentiating quality of Q Methodology is that the

statements being sorted are the sample, while the participants

are described as the P Set, or set of persons who are

theoretically relevant to the problem under consideration (Brown,

1980). Stephenson (1935)wrote of Q Methodology , “[w]hereas

83

previously a large number of people were given a small number of

tests, now we give a small number of people a large number of

test-items” Correlation between personal profiles then indicates

similar viewpoints, or segments of subjectivity which exist

(Brown 1993). Q is intended to get at patterning within

individuals (case-wise) rather than simply across individuals

(factor-wise sorting) (Brown, 1997).

Q Methodology allows participants, in effect, to “create

their own categories. (Brown & Narayan, 2005)” If all

participants were to hold the same beliefs, Q factor analysis

would register as a single factor. If there were two belief

systems, there would be two factors, and so on. The number and

character of the factors is a function of the participants

themselves, not of how the investigator categorizes the

statements used. Q factor analysis allows the researcher to group

sorters with similar viewpoints for tailoring training in order

to improve program effectiveness (Newman & Ramlo, 2011). Through

Q Methodology, artificial categories are replaced by operant

categories that represent functional, not just logical

distinctions (Brown, 1991). An additional advantage of Q

84

Methodology studies is that wholly unexpected Q factors may

emerge (Brown & Narayan, 2005).

In Q Methodology a list of statements is developed that is

sufficiently representative of the “universe of viewpoints” about

a topic, which is called the concourse (Brown, 1993).The

concourse, which is used to develop the set of statements to be

sorted, can be developed using a variety of techniques including

interviews, focus groups, free writing, etc. (Newman & Ramlo,

2010). The concourse is followed by a selection, called the Q

Sample that the participants will be asked to sort (Brown, 1980;

McKeown & Thomas, 1988; Ramlo, 2008). The participants pre-sort

the items, typically statements on numbered strips of paper, into

three categories, most like my view, neutral and least like my

view, according to a set of “conditions of instruction.” Once

this pre-sorting has been completed, participants physically sort

items, relative to each other into a normalized or Gaussian

distribution onto a grid (Brown, 1980; Stephenson, 1953). An

example of a Q Sort grid is shown in Figure 3.

The “conditions of instruction” are the set of instructions

given to the sorter, describing the conditions under which the

85

sorter should place the statements. This is an important guide to

the actual sorting process, and must be clearly defined before

the sorting process begins (Watts & Stenner, 2005). The

respondent is instructed to rank the statements according to some

rule – the condition of instruction, typically the person’s point

of view regarding the issue (Van Exel & de Graaf, 2005). For

example, in one Q Methodology study by Ramlo (2005), faculty

participants were asked to sort statements based upon their views

about the creation of a School of Technology. This study was, in

effect, a needs assessment such as that which often takes place

86

within program evaluation (McNeil et al., 2005). In another Q

Study, engineering and engineering technology educators were

asked to sort statement based on their view on the use of

educational technology in the classroom, and their views of

student learning. Respondents were asked to sort statements from

“least like my view to most like my view” (Nicholas, 2011).

87

Mostunlikemy

viewneutral

Mostlikemyview

-5 -4 -3 -2 -1 0 1 2 3 4 5

The sorters interpret statements based upon their own views

of the statement’s meaning. Q Methodology is self-referential,

meaning that the sorting refers to one’s own experience, or

subjectivity. As such, the sorting process represents a

communicative process (Brown, 1980; Stephenson, 1953). Because of

the self-reference of the sorters, post-sort interviews or

written comments are typically used to assist the researcher in

interpreting the meaning of the sorts (Ramlo & Newman, 2011).

These post sort interviews aid in gathering of supporting

information from the participant (Watts & Stenner, 2005). This

can be done via a brief post-sorting interview (which can then be

transcribed and subjected to analysis), or simply via some form

of ‘response booklet’ or post-sorting questionnaire with open

ended questions (Wong, Eiser, Mrtek, & Heckerling, 2004). Such

post hoc analyses ordinarily investigate: (a) how the participant

has interpreted the items given especially high or low rankings

in their Q Sort, and what implications those items have in the

context of their overall viewpoint; (b) if there are any

additional items they might have included in their own Q set

(what they are, why they are important, and so on); and (c) if

88

there are any further items about which the participant would

like to pass comment, which they have not understood, or which

they simply found confusing (Watts & Stenner, 2005).

After the participants have completed and the researcher has

compiled the sorts, the analysis of the data must be conducted.

The analyses of the Q Sorts involve correlation, factor loadings,

factor analysis, and the calculation of factor scores (Brown,

1980; McKeown & Thomas, 1988). Conceptually, factor loadings are

correlation coefficients. The higher the factor loading, the more

highly the sorter is correlated with that factor or view (Cuppen,

Breukers, Hisschemöller, & Bergsma, 2010). Those sorters with

similar views are more highly correlated with the same factor.

Several programs exist to specifically handle the type of data

collection and analyses in Q Methodology. PQ Method is one of the

most common, and is available for free (Schmolck & Atkinson,

2002).

The factor loadings express the extent to which each Q Sort

is associated with each factor. With little exception, only the

first two or three factors contain significant loadings, although

it is possible that more than three factors may emerge (Brown,

89

1980; 1993). However, the original set of factors is the raw

materials from which the probing of these subjective

relationships can take place from the vantage points of interest

(Brown, 1980, 1993, 2009; McKeown & Thomas, 1988; Newman & Ramlo,

2010).

At this stage, the factors themselves have no meaning beyond

identifying groups and remain numerical abstractions until final

interpretation (Eden, Donaldson, & Walker, 2005). The aim of the

post-Q Sort interview is to discover the rationale behind

participants' placing of the cards in the Q Sort response grid

(Gallagher & Porock, 2010). Typically, when interpreting their

results, researchers coin a name for each factor and describe its

viewpoint in a paragraph or two of prose that rephrases key

statements or lists key statements from the ‘ideal’ sort (Eden et

al., 2005). Although previous research may be used to explore the

factor, it may not capture the rationale behind these particular

participants’ placement of the statements. Interviewing

participants after they have Q Sorted allows for the

interpretation of the factors to be based on the participants’

perceptions and attitudes to the phenomenon under study. This can

90

be discussed in line with previous research yet allows for new

theory to be generated (Gallagher & Porock, 2010).

The major concern of Q Methodology is not with how many

people subscribe to a particular belief, but with why they

believe what they do (McKeown & Thomas, 1988; Sexton, Snyder,

Wadsworth, Jardine, & Ernest, 1998). With Q Methodology small

sample sizes are psychometrically acceptable because the

observational perspective is the respondents own (McKeown &

Thomas, 1988). This means that any observations or interpretive

accounts that are advanced by the researchers are subservient to

the respondent’s frame of reference as made operant by Q Sorting.

Because of this, the validity and reliability tests that are so

important in conventional research are unessential within the

psychometric framework of Q Methodology (Brown, 1980, 1993;

McKeown & Thomas, 1988).

Although Q Methodology is similar to the Likert-style survey

in that the distribution on the grid typically ranges from least

like my view to most like my view (Ramlo, 2008), Q differs from

Likert-style surveys in that Q involves participants physically

sorting items relative to each other into a normalized or Gaussian

91

distribution (Brown, 1993; Brown, 1980; McKeown & Thomas, 1988;

Ramlo, 2008; Ramlo & Nicholas, 2009). Likert (1967) assumed that

every statement is equally important to the overall attitude.

Likert scales do not consider the weight that sorters attach to

individual items (ten Klooster, Visser, & de Jong, 2008) which

can therefore result in the loss of meaning (McKeown, 2001; Ramlo

& McConnell, 2008).

Summary

There has been a concerted effort by faculty in disciplinary

fields and in graduate schools to continually address whether

graduates are prepared adequately to perform the roles for which

they have been socialized, so that the graduate program can make

appropriate adjustments. It is desirable, but not always present,

that there be regular opportunities for the voices of graduate

students to be heard, so that their perspective informs program

development (Weidman et al., 2001). The use of graduate students

as instructors for undergraduate students has increased

significantly as the number of undergraduate students has risen.

GTA instructional training programs vary significantly in

structure and effectiveness. Understanding the needs of GTAs may

92

assist supervisors of programs in evaluating their professional

development programs, and increasing the effectiveness of their

program. Q Methodology is an appropriate methodology for

uncovering the various viewpoints of GTAs in their instructional

training program. Improving their training program will assist

GTAs in meeting the challenges of an evolving job market, a

diverse set of undergraduate students, the complexities of doing

original research, and balancing life in graduate school.

93

CHAPTER III

METHODOLOGY

The purpose of this chapter is to present a comprehensive

review of the methodology related to this study. This chapter

provides an overview of the research design, the derivations of

the general and specific research hypotheses, and the research

questions. Other sections designate the participants and sampling

procedures. The basic procedures for a Q Methodology study are

described in detail. The instrument section describes the

compilation of the concourse, the Q Sample, the Q Sort, the

conditions of instruction, and the pilot study conducted during

the Fall semester of 2012. The statistical treatment section

explains how the results of the Q Sorts will be factor analyzed

and interpreted. The role of the researcher and limitations of

the study conclude the chapter.

Introduction and Overview

Since the purpose of this study is to explore Biology

Graduate Teaching Assistants’ (GTA) experiences of graduate

school, it is important to have a baseline assessment of GTA

94

needs. Using Q Methodology, GTA’s viewpoints can be made

operational. Developed by William Stephenson in 1935, Q was

created to the scientific study of subjectivity. Q Methodology

studies patterns of subjective perspectives across participants

rather than patterns across variables (McKeown & Thomas, 1988;

Watts & Stenner, 2005). Through Q Methodology, artificial

categories (from objective tests) are replaced by operant

categories that represent functional, not just logical

distinctions (Brown, 1991).

Stephenson (1935)wrote of Q Methodology , “[w]hereas

previously a large number of people were given a small number of

tests, now we give a small number of people a large number of

test-items” Correlation between personal profiles then indicates

similar viewpoints, or segments of subjectivity which exist

(Brown 1993). Q is intended to get at patterning within

individuals (case-wise) rather than simply across individuals

(factor-wise sorting) (Brown, 1997). The statements being sorted

are the Q Sample, while the participants are described as the P-

set, or set of persons who are theoretically relevant to the

problem under consideration (Brown, 1980).

95

Q Methodology allows researchers to identify, both

quantitatively and qualitatively, the various opinions within a

group and the number of people within the group who hold these

opinions (Brown, 1993; McKeown & Thomas, 1988; Ramlo, 2008;

Stephenson, 1953). Q Methodology is an appropriate choice

whenever a researcher recognizes that there are differing and

consensus viewpoints in a group. Common perspectives held by

group members can be used to scaffold the instructional training

of the participants (van der Valk & de Jong, 2009), based upon

consensus statements. This consensus within a group regarding any

topic is uncovered and made operational (Ramlo, 2008). Thus, Q

Methodology is an appropriate choice whenever a researcher wishes

to determine and describe the various perspectives and consensus

within a group regarding any topic (Brown, 1980; Ramlo, 2008).

General Research Questions

The research questions were developed to study the range of

viewpoints that exist among Biology GTAs about their graduate

school experience, particular their instructional training

program. The researcher was interested in how the viewpoints of

the GTAs and their viewpoints of their supervisors would be

96

similar or different. The Biology Lab Coordinator and the Lead

Biology Faculty Member have different backgrounds, positions

within the university, and priorities for GTAs. The GTAs

themselves have different needs within their programs, which may

change as they complete their “Effective Teaching” course,

experience teaching, take classes of their own, and learn to do

research. This chapter outlines the research design and data

analysis that were used to investigate the following research

questions:

1. What are the various viewpoints that exist among Biology

GTAs about their graduate school experiences?

2. What are the various viewpoints of the supervisors of

graduate GTAs in The Department of Biology, relative to

those of the GTAs?

3. What consensus exists among the GTAs in The Department of

Biology about their graduate school experiences?

4. How do the views differ between new GTAs versus experienced

GTAs?

5. Do the varying views and consensus of GTAs about their

graduate school experiences provide sufficient information

97

for a needs assessment that informs the existing training

program?

Next, a rationale for the use of Q Methodology as the

research design for this study is presented, along with a

description of the P-Set, compilation of the concourse, the Q

Sample, the Q Sorting process, and the data analysis procedures

that were used.

Rationale for the Research Design

This study utilized Q Methodology as a research approach.

Ramlo (2008) described how Q Methodology “is an appropriate

choice whenever a researcher wishes to determine the various

perspectives and consensus within a group regarding any topic.”

As Robbins and Krueger (2000) stated, “Q Method’s approach

renders empirical the question of who is similar, under what

conditions difference is expressed, and why (p. 644).” Q

Methodology focuses on grouping individuals with similar

viewpoints, perspectives, ideas, or beliefs. Q Methodology is

used to study participants’ subjectivity, that is, their

viewpoints, in a systematic way (Brown, 1991; McKeown & Thomas,

1988). It allows a researcher to “understand a human experience

98

rather than identify cause-and-effect relationships” (Broady-

Ortmann, 2002) while finding out different opinions of group

members and how many people in the group share specific opinions

(McKeown & Thomas, 1988; Ramlo, 2008; Stephenson, 1935).

GTAs may possess different views of graduate school.

Professional development programs for training GTAs vary

extensively from institution to institution, and even between

departments at the same institution (Rushin et al., 1997). New

GTAs enter graduate school with vastly differing amounts of

content knowledge, pedagogical knowledge, and research skills

(Gess-Newsome & Lederman, 1999; Shulman, 1986). They have had

varied experiences with students, and as students. “Teaching as

they were taught” in their own science courses may lead to

further lack of understanding (Brown, Abell, Demir, & Schmidt,

2006; Longbottom & Butler, 1999).

GTA skills and knowledge change as they encounter learning

experiences in graduate school (Luft et al., 2004; Muzaka, 2009;

Park, 2002; Prieto & Altmaier, 1994). In order to improve the

instructional training program for GTAs, the supervisors of the

program must first understand the various views held by the

99

targeted population about their situation and needs (Sohoni et

al., 2013). Literature about GTA training has classically focused

on faculty’s perceived needs of GTAs (Boyle & Boice, 1998;

DeChenne, 2010; Sohoni et al., 2013; Young & Bippus, 2008).

National training programs, such as The Preparing Future Faculty

program, are designed entirely by faculty, using faculty

perceptions of what GTAs should know (Anderson, Gaff, & Pruitt-

Logan, 1997). While training programs must address material

gleaned from faculty experience, educational research, learning

theory, etc., programs should also address critical concerns from

the GTA perspective as well (Williams & Roach, 1992).

Q Methodology has advantages over survey research for this

study even though both methods are used to obtain participants’

perceptions. Likert-style surveys of GTAs may have limited

effectiveness in understanding GTA needs (Cho et al., 2010).

Surveys are common methods for collecting feedback; however, they

allow responders to give similar or identical ratings to many or

all items (Dennis, 1986). They can also result in missing data

(Li-Fen Lilly Lu & Jeng, 2006; McKeown, 2001; Sexton, Snyder,

Wadsworth, Jardine, & Ernest, 1998). Missing data or non-response

100

bias resulting from non-respondents can be alleviated through use

of Q since data is collected one-on-one (Dennis, 1986). Surveys,

polls, and scales can highlight common or shared opinions that

exist in the total group, but do not provide empirical evidence

of the differing views/factors (Collins, 2009).

Surveys rely upon large numbers of participants in order to

generalize results from the study to a larger population

(Previte, Pini, & Haslam-McKenzie, 2007), however, the small

number of factors that emerge in most Q studies require

relatively small numbers of participants (Dennis, 1986; Previte

et al., 2007). In fact, “a larger number of participants can be

problematic, because they can negate the complexities and fine

distinctions which are essential features” in carrying out a Q

study (Previte et al., 2007, p. 139). Q Methodology employs a by-

person factor analysis in order to identify groups of participants who

make sense of (and who hence Q ‘sort’) a pool of items in

comparable ways (Watts & Stenner, 2005). Q can be seen as a tool

to make more explicit the expectations and beliefs held by a

group with respect to the dialogue (Steelman & Maguire, 1999). Q

reveals correlations and factors among persons, while R

101

methodology, or survey research, reveals correlations and factors

among traits. In Q Methodology, the correlations are based on the

assumption that “persons significantly associated with a given

factor … share a common perspective” (McKeown & Thomas, 1988, p.

17). Thus, Q is useful in understanding participant perspectives

within groups (Cross, 2005; Previte et al., 2007; Ramlo, 2008;

Steelman & Maguire, 1999).

Further advantages of the Q Methodology are identi ed by fi

Peritore (1989), who described how Q “respects the integrity of

the respondent, results can be recorded anonymously and factorial

results cannot be predicted.” It is argued that Q Methodology

combines the strengths of both qualitative and quantitative

research (Dennis & Goldberg, 1996) and provides a bridge between

the two paradigms of inquiry (Sell & Brown, 1984). Zraick and

Boone (1991) emphasize that Q Methodology is more focused than a

general attitude questionnaire, and that Q Sorts are normally

distributed and therefore can also be used parametrically in

intergroup comparisons. Another factor underlying the Q approach

to participants is that Q Methodology has no interest in

estimating population statistics; rather, the aim is to sample

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the range and diversity of views expressed, not to make claims

about the percentage of people expressing them (Kitzinger, 1987).

By determining the diversity of perspectives held by the

GTAs, the supervisors can then tailor meaningful, relevant

professional development opportunities that prepare GTAs for

future challenges such as those suggested by Marincovich,

Prostko, and Stout (1998); Ross and Dunphy (2007), and Tice et

al. (1998). GTAs often express frustration in graduate school,

for reasons that include teaching, learning, research, working

with students, and persisting in their program (Muzaka, 2009).

Completing a needs assessment with incoming GTAs can help to

determine the variety of viewpoints that exist in an

instructional training program. Conducting this needs assessment

with GTAs who have completed the instructional training program

and have continued teaching can identify if there are aspects of

the training program that could be better addressed.

Despite the literature related to training teaching

assistants, faculty who work with graduate students may be

unprepared themselves to mentor GTAs for any career other than a

faculty research career (DeNeef, 2002), GTAs express frustration

103

with teaching, working with undergraduate students, the

challenges of graduate classes, learning to do research, and

balancing demands of their time with having a personal life

(Drake, 2011; Eison & Vanderford, 1993). Boyle and Boice (1998)

found that new GTAs, who are just beginning graduate school,

voice different frustrations than experienced GTAs who may be

writing a thesis, working with advanced classes, writing articles

for publication, or doing research. Q Methodology allowed the

researcher to uncover common viewpoints, or “predictor profiles”

that may be different than grouping GTAs based on “new or

experienced” or “master’s or doctoral” or “teaching-focused or

research-focused.” Uncovering these typologies may lead to more

effective means of scaffolding the training for GTAs who express

different viewpoints. With most of the current-day job

opportunities being outside of academia, GTAs often struggle to

gain the transferrable skills that help them to be successful

outside of academia in a teaching career, industry, or an

alternative profession (Jenkins, 1996; Park, 2002). Understanding

the needs of GTAs, which can lead to improving the effectiveness

of GTA training programs, may eventually lead to benefits for the

104

stakeholders involved in this program. Q Methodology has been

shown to be an effective needs assessment tool in program

evaluation (Newman & Ramlo, 2011; Ramlo & Berit, 2013).

Basic Procedures of Q Methodology

The first general step of conducting a Q Methodology study

is the purposeful selection of participants, or the P-Set. The

usual number ranges from 30 - 50, but this could vary according

to need. It is preferable to try to find people with varying

views in order to experience a variety of responses in the

sorting process (Reid, 1999). Watts and Stenner (2005) stress the

importance of finding participants with a defined viewpoint,

whose viewpoint matters in relation to the subject at hand. They

also reiterate that” more is better” does not apply to Q

Methodology, when it comes to participants. Brown (1980) suggests

that Q Methodology only requires “enough participants to

establish the existence of a factor for purposes of comparing one

factor with another (p. 192).” Q Methodology has little interest

in taking head counts, or generalizing to a population of people.

Q is more concerned with the exploration of meaning and quality

(Willig & Stainton-Rogers, 2007).

105

Van Exel and De Graaf (2005) emphasized that Q

Methodological studies do not require large sample sizes. They

contended that the P-set is selected intentionally by compiling a

sample of “respondents who are theoretically relevant to the

problem under consideration” (p. 6). Participants are not

randomly chosen (Brown, 1980; McKeown & Thomas, 1988; Quiles,

2009; Webler, Danielson, & Tuler, 2009). Instead, individuals are

recruited who are representative of the issues and could provide

the best insights on the topic under study. Baker et al. (2006)

describes how, in Q Methodology, “individuals are purposefully

selected according to their personal attributes, views they might

express, or on the basis of their social position and background.

The sample will therefore depend on the research topic in

question rather than on the basis of statistical power.”

The next procedure in a Q Methodology study is development

of the concourse. Concourse development involves the creation of

a large set of statements that illustrate a range of attitudes

and perceptions that have been expressed by people related to a

particular subjective topic of exploration. Van Exel and De Graaf

(2005) noted, “The gathered material represents existing opinions

106

and arguments, things lay people, politicians, representative

organizations, professionals, scientists have to say about the

topic; this is the raw material for a Q” (p. 4). The theme of the

research, and the inclusion of statements remain controlled by

the researcher (Eden, Donaldson, & Walker, 2005). The ideal

concourse contains all the relevant aspects of themes identified

in all discourses about a given topic (De Graaf & Van Exel,

2008). The level of discourse dictates the sophistication of the

concourse (Brown, 1980).

Stephen Brown stated,

The concourse is the flow of communicability surrounding any

topic. Concourse is the very stuff of life, from the playful

banter of lovers or chums to the heady discussions of

philosophers and scientists to the private thoughts found in

dreams and diaries. From concourse, new meanings arise, bright

ideas are hatched, and discoveries are made: it is the wellspring

of creativity and identity formation in individuals…and it is Q

Methodology’s task to reveal the inherent structure of a

concourse. (1993, pp. 94-95).

107

A concourse can be collected in a number of ways. The two

most typical methods include reviewing literature (theoretical)

and/or interviewing people (naturalistic) and recording what is

said (McKeown & Thomas, 1988). A concourse may also be comprised

of statements from both naturalistic and theoretical sources, and

would be considered a hybrid approach (Delnero & Montgomery,

2001).

After the compilation of the concourse comes the development

of the Q Sample. The Q Sample items are a subset of the full

concourse (Valenta & Wigger, 1997). Selections for the Q Sample

are made by the researcher. A wide variety of statements from the

concourse must be selected in order to create a Q Sample that is

manageable, but is also representative of the same perceptions

and attitudes that are expressed in the full range of statements

in the concourse (Van Exel & De Graaf, 2005). The Q Sample is

often made up of 40 – 80 items, but this number might vary

according to need. As the items are selected, any two statements

should be positively associated, negatively associated, or

unassociated (Reid, 1999).

108

As with sampling persons in survey research, the main goal

in selecting a Q Sample is to provide a miniature, representative

sample of the concourse, which, in major respects, contains the

comprehensiveness of the larger process being modeled (Brown,

1980; De Graaf & Van Exel, 2008). The problem, of course, is how

to select from the concourse so as to provide representativeness

in the Q Sample, and the main device relied upon to achieve this

is Fisher's experimental design principles (Brown & Ungs, 1970).

The next step Reid (1999) described as the administration of

the Q Sort, following the directions, known as the conditions of

instruction, or the procedures participants follow as they sort.

Van Exel and De Graaf (2005) explained that the cards comprising

the Q Sample are given to participants in a pack of randomly

numbered cards with one statement written on each one (p. 6). A

participant must be able to effectively respond to the question

by sorting the set of provided items along a single, face-valid

dimension, such as most agree to most disagree, most important to

most unimportant and so on. The condition of instruction must be

written down and kept in front of each participant as they sort,

109

because the researcher must be certain the P-Set are all

answering the same question (Watts & Stenner, 2005).

The researcher tells the participant to make 3 general piles

containing the same number of statements, reflecting least like

their view, neutral, and most like their view. Then, the

participants go on to discriminate further to into a forced,

symmetrical/quasi normal distribution. Participants are

instructed to rank the cards onto a sorting grid, for example on

a scale of -5 to +5, with -5 being the most unlike their personal

point of view, 0 being neutral, and +5 being the most like the

point of view that they most identify with (See Figure 4). It is

recommended that Q Sorts be followed with interviews to provide

participants the opportunity to elaborate on their points of

view, especially in regard to the extreme ends of the spectrum,

those most unlike and those most closely aligned the

participants’ points of view (p. 7).

110

Using an intentionally forced distribution in the sorting

process limits the number of items that participants can place in

each category or ranking level. Unlike surveys and Likert scales,

sorting into a grid ensures that the participants make explicit

choices about the ranking of the sort items relative to the other

items (Corr, 2001; McKeown & Thomas, 1988; Ramlo, 2008). By

sorting the items into a forced distribution, participants are

required to discriminate among them in a way they would not do

otherwise (Dennis, 1986).

111

Mostunlikemy

viewneutral

Mostlikemyview

-5 -4 -3 -2 -1 0 1 2 3 4 5

The forced distribution ensures fine discrimination by the

participant who cannot simply sort all items into two categories

such as strongly agree strongly disagree. The participant must

follow a symmetrical distribution and sort items into categories

that reflect degrees of opinion of preference (Dennis, 1986;

Reid, 1999; Sexton, et al., 1998). The ranking may stretch, for

example, over a span of -5 to +5 for the sorter to show a range

of opinions. The Q Sorting process forces the sorter to make

choices about what is more or less like their views. A clear and

'gestalt' configuration of items will duly emerge (Watts &

Stenner, 2005). If the participant is happy with this

configuration, the various item numbers (and hence the 'form' of

the overall configuration) should be recorded (Brown, 1993). Each

Q Sort is simply the perspective of the person whose Q Sort it is

(Brown, 1997). Participants inject statements with their own

understandings. Objective measures (e.g., IQ tests) have right

answers, but this is not the case within the realm of

subjectivity, and it is for this reason that Stephenson always

utilized factor analysis rather than variance analysis in

analyzing data obtained from Q technique (Brown, 1997).

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The results of the sorting activity lead to the interview.

Some researchers feel the interview is optional, but for the full

and complete administration of Q Methodology, the interview is a

necessary component and should not be left out (Reid, 1999).

After completion of the Q Sorting activity, the researcher

discusses with the sorter the way decisions were made, focusing

on apparent contradictions, outlying selections, extreme

responses, or unclear points. The interview is often tape

recorded. Along with the results of the sort, the interview

information serves as essential data for the study (Collins,

2009).

The final step in the study is the interpretation of the

data. Following the sort, statistical analysis commences in Q

Methodology. The sorts are entered into PQMethod, the software

package design specifically for analyzing Q Methodology data

(Schmolck & Atkinson, 2002). This software package provides a

variety of outputs, such as a correlation matrix, factor

loadings, distinguishing statements, and consensus statements.

Data analysis occurs with factor analysis highlighting

intercorrelations of the Q Sorts as variables persons, not traits

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or Q Sample items are correlated). The combined respondents’

factor loading indicates the extent to which each Q Sort is

similar or dissimilar to others (McKeown & Thomas, 1988).

The researcher looks for areas of agreement among sorters.

The level of agreement or disagreement among sorters ultimately

gives rise to the factors. A factor analysis is applied to the

results of the sorts, looking for patterns that arise from among

sorts (Collins, 2009). The factor analysis reduces the many

viewpoints down to a few salient factors, which reflect common or

shared ways of thinking (Reid, 1999; Sexton, et al., 1998).

Generally, if four or more sorters load on a common factor, that

is an indication of increased reliability (Brown, 1980; Sexton et

al., 1998).

Additional insights into what is different about the two

factors’ perspectives can be achieved by examining the

distinguishing statements, which are statement rankings which

distinguish the factors from each other, and consensus

statements, which represent agreement among all the factors

(Brown, 1980; McKeown & Thomas, 1988). Brown (1991) pointed out

that when comparing the rankings assigned to the sort items,

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differences of 2 between ranking scores could be considered

significant. However, this was a guide and could serve as an

alert for the researcher to look more closely at these items and

see what it was about distinguishing statements that caused

individuals to rank them so differently (Brown, 1980). These

differences could be used to help define distinctions between

groups (Donner, 2001). Consensus statements have allowed

researchers to focus on agreement among different views, which

can be used to start a dialogue related to commonality (Ramlo &

Newman, 2011; Ramlo, 2005).

From the review of Q Methodology design and unique

characteristics, it was obvious that Q Methodology was a suitable

fit for the goals of this study. Q Methodology could assist in

answering the research questions, and was robust enough to

satisfy both the quantitative and qualitative aspects of this

study. And, Q Methodology allowed the researcher to answer more

sophisticated questions about this group of people than either

qualitative or quantitative analysis could do on its own (Ramlo &

Newman, 2011).

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Setting

The research study was conducted at a large, public, urban

university in the Midwest. Enrollment was 28,771 in the Fall 2012

semester. The university offers over 300 Baccalaureate programs,

200 Master’s programs, and 37 Ph.D. programs. The Department of

Biology offers eight Bachelors of Science degrees, two Master’s

degrees, and one International Baccalaureate (IB) doctoral

program. In the Fall 2012 semester, 828 undergraduate students,

24 master’s degree students, and 37 IB doctoral students were

enrolled. There were 22 full time faculty and eight part time

faculty, 12 full time staff, 12 graduate research assistants, and

40 graduate teaching assistants employed by the department (“The

University of Akron : IR Home,” 2013). The Department of Biology

emphasizes collaborative and integrative research. Facilities

include a live animal research center, 400 acre field station,

and greenhouse (The University of Akron, 2013a).

The Department of Biology has many areas of strength,

including Ecology and Evolutionary Biology, Physiology, Molecular

Biology, and Organismal Biology. Areas of interest for graduate

research include: pollination biology, conservation biology,

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physiological ecology, life history evolution, mating systems,

aquatic ecology, evolution in developmental processes, behavioral

evolution, spider biology, biomedical research, hypertension and

stress research, bio-materials and biomechanics, developmental

molecular biology and physiology, comparative biochemistry, and

evolutionary biomechanics (The University of Akron, 2013a).

Admission Requirements for the Biology graduate programs

include:

A baccalaureate degree in biology or equivalent training.

A minimum cumulative grade point average of 3.0 (4.0=A) and a 3.0 average in biology (minimum 32 semester credit hours or equivalent).

Competence in chemistry and mathematics.

Scores from any one or more of the following standardized tests: GRE (General Test), GRE (Biology-specific Test), or the MCAT. Scores are expected to be above the 25th percentile to be competitive for admission.

A letter of interest indicating the proposed area of specialization and possible advisers in the Department of Biology.

Strong letters of recommendation (3 preferred).

A letter from the potential Biology Adviser indicating willingness to sponsor the applicant (The University of Akron, 2013a).

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The M.S. degree in Biology is obtained upon completion of

required coursework and a research thesis. Each student, in

conjunction with a graduate committee, plans coursework, seminars

and research based upon the student's background and interests. A

total of 40 graduate credit hours are required for the degree. Of

these 40, a minimum of 12 must be in thesis research credits, 24

in formal coursework, and 4 in colloquium. A non-thesis option is

available for individuals with a current teaching certificate or

co-registration with the College of Education toward obtaining

teaching certification (The University of Akron, 2013a).

Full-time master’s degree graduate students pursuing thesis

research may be supported with graduate assistantships, either

teaching or research assistant, by the appropriate Department,

generally for a period of two years. Full-time teaching

assistants (GTAs) are expected to work 20 hours per week and must

enroll as full-time students (currently 9 or more credit hours

per semester, including research). master’s degree GTAs are

expected to enroll in a one credit-hour “Effective Teaching”

course at the beginning of their program, which serves as an

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orientation to the department, to the graduate program, and to

teaching a laboratory course (The University of Akron, 2013a).

The Integrated Bioscience Ph.D. is obtained upon completion

of required coursework and a research dissertation. Each student,

in conjunction with a graduate committee, plans coursework,

seminars and research based upon the student's background and

interests. A minimum of 80 credit hours is divided between formal

courses, elective courses, colloquia, and research. The mission

of the Integrated Bioscience program is to address the need for

Ph.D. level graduates who have both deep and specific expertise

in a bioscience, bioengineering or biotechnology discipline and

broad adaptability across related disciplines (The University of

Akron, 2013b).

The program is composed of six areas of excellence:

1. Molecular cell biology and genetics

2. Biochemistry and biopolymers

3. Bioinformatics and computational biology

4. Bio-engineering

5. Physiology and organismal biology, and

6. Ecology and evolutionary biology (The University of Akron, 2013b).

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Full-time Integrated Bioscience Ph.D. graduate students pursuing

dissertation research may be supported with graduate

assistantships, either teaching or research assistant, by the

appropriate Department, generally for a period of five years.

Full-time teaching assistants are expected to work 20 hours per

week and must enroll as full-time students (currently 9 or more

credit hours per semester, including research). Integrated

Bioscience Ph.D. GTAs are expected to enroll in a one credit-hour

“Effective Teaching” course at the beginning of their program,

which serves as an orientation to the department, to the graduate

program, and to teaching a laboratory course (The University of

Akron, 2013b).

The P-Set

The P-Set for this study was purposefully selected, and

included both new and experienced Biology GTAs. A new Biology GTA

is defined as a graduate level student who is seeking a master’s

or doctoral degree, has less than one year of formal teaching

experience, and teaches an undergraduate-level laboratory for

approximately 20-hours a week in exchange for a fee-remission.

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This GTA is currently enrolled in an "Effective Teaching"

instructional training course. An experienced Biology GTA is

defined as a graduate level student, who is seeking a master’s or

doctoral Degree, has more than one year of formal teaching

experience, and teaches an undergraduate-level laboratory for

approximately 20-hours a week in exchange for a fee-remission.

This GTA has completed an "Effective Teaching" instructional

training course. This P-Set was selected intentionally to include

respondents that were stakeholders in a Biology GTA instructional

training program.

The study included participants sorting during two different

phases. Q Sorting was completed first by new GTAs enrolled in an

“Effective Teaching” course in The Department of Biology in the

Fall 2012 semester, and the two supervisors of the course. These

sorts occurred during the second week of the “Effective Teaching”

course. In this phase of the study, there were 21 total Q Sorts

collected from 17 new Biology GTAs, the Biology Lab Coordinator

sorting twice, and the Biology Lead Faculty Member sorting twice.

The Biology Lab Coordinator and Biology Lead Faculty Member

sorts were included in the study because of their large degree of

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involvement with the instructional training of Biology GTAs, and

these supervisors of GTAs were most familiar with the viewpoints

of both new Biology GTAs and experienced Biology GTAs. The

researcher was interested in whether the supervisors of Biology

GTAs would have similar or differing viewpoints than the actual

GTAs. The Biology Lab Coordinator and Biology Lead Faculty Member

sorted during the first phase of the study.

The second phase of this study included experienced GTAs who

had completed the “Effective Teaching” course and who had

successfully taught in the Biology department for more than one

year. This sorting included all experienced GTAs who attended a

weekly mandatory Biology departmental colloquium meeting. There

were an additional 14 Q Sorts collected from the experienced

GTAs, and one Q Sort collected from an additional Biology Lab

Coordinator, theoretically sorting as an experienced GTA. The

demographics for the P-Set are described in Table 1.

Table 1 – P-Set Demographics

Number Percent

Participation Rate 36 Sorts 100%

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Number PercentSession of Sort

Completion

Effective

Teaching Course 21 58%

Colloquium 15 42%

Type of Participant

New GTAs 17 48%

Experienced

GTAs 14 42%

Biology Lab

Coordinator 3 7%

Biology

Faculty Member 2 5%

Degree Track

Doctoral 16 52%

Masters 15 48%

Gender

Male 19 52%

Female 17 48%

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Number PercentOrigin

International 2 6%

United States 34 94%

Teaching Experience

None 2 6%

Informal 6 17%

Formal 28 77%

The Concourse

The “GTA Perceptions of Graduate School Q Sort” was

developed during the summer of 2012 for a Q Methodology seminar.

The first stage of designing the Q Sort involved the compilation

of the concourse. The concourse for this Q Methodology study was

created through the examination of statements made by GTAs in a

Self-Reflection Questionnaire (SRQ), a “Perceptions of Graduate

School Survey,” a graduate student discussion forum (“Grad School

Life,” 2012), everyday conversations and emails made between

Biology GTAs and their supervisors, and a thorough literature

review. Being that both theoretical and naturalistic sources were

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used for the compilation of the concourse, this would be

considered a hybrid approach. There were 93 statements collected

from these sources (see Appendix 1). The themes for the concourse

in this study are identified in Table 2.

Table 2 - Development of the Concourse and Q Sample

ThemeNumber of Statements in the Concourse

Added to Q Sample

Advisor 3 1Anxiety 10 4Balance 4 2Career 2 2Collaboration 1 1Confidence 10 7Diversity 4 2Effort 4 4Emotional 1 1Ethical 2 1Fairness 2 1Intelligence 2 1Learning Styles 11 5Practical 2 1Preparation 8 4Research 8 6Respect 3 3Teaching 16 9

Total number of 93 54

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statements

SRQ – Self Reflection Questionnaire

The first place that statements were compiled from was the

Self Reflection Questionnaire. Eight biology GTAs who taught

Natural Science, a general education course for non-majors, were

asked to complete a Self-Reflection Questionnaire (SRQ) after

successfully teaching in the laboratory during the Spring

semester of 2010. The questionnaire was developed as part of a

program improvement initiative under the direction of a new

faculty coordinator for Natural Science Biology. The SRQ

consisted of eight open-ended questions which prompted GTAs to

reflect on their teaching philosophy, knowledge of biology

concepts, and teaching skills. The SRQ was designed using

Shulman’s (1986; 1987) theory of Pedagogical Content Knowledge

(PCK), connecting teachers’ content knowledge and pedagogical

principles and practices, and was modeled after Hammrich’s (1996)

open ended questionnaire that explored how biology graduate

students defined the “teaching of science.” The SRQ was critiqued

by Biology faculty and professors from the Department of

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Education. The eight prompts were analyzed using content

analysis techniques. The questions on the SRQ were:

1. When you took your Effective GTA Training course, you were

asked to describe your teaching philosophy. Do you still

have a copy of this? If so, please copy and paste below.

2. How knowledgeable do you think you are about the Biological

concepts covered in the course? (Extremely knowledgeable,

very knowledgeable, somewhat knowledgeable) What factors

made you come to this conclusion?

3. What do you feel are your strengths? (Please list 3 to 5 of

each)

4. What do you feel you could improve upon? (Please list 3 to

5 of each)

5. By the end of the semester, what do you think students

should know once they have finished the lab course (i.e.

procedures, subject matter, skills) Please list the 3 to 5

most important.

6. If you had the opportunity to teach these same students

again, what would you do differently?

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7. What do you feel is the most challenging thing about

teaching the lab?

8. What do you feel is the most challenging thing for students

taking the lab?

The eight GTAs who completed the SRQ did so after teaching

Natural Science Biology for at least one semester. Their

demographics are detailed in Table 3. Answers to the SRQ were

grouped into categories of statements displaying content

knowledge, pedagogical knowledge, and pedagogical content

knowledge. Answers were used in the redesign of the instructional

training for GTAs who teach the non-majors Natural Science

Biology laboratory in the Fall of 2010. GTAs who completed the

SRQ did not complete the Q Sort.

Table 3 - Demographic Characteristics of GTAs completing the SRQ

8 graduate GTAs

Sex 3 females 5 males

Program 6 Master’s 2 Doctoral

Nationality 2 International 6 United States

Experience 4 first time 4 experienced

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teachers teachers

The Perceptions of Graduate School Survey

The second place that statements for the concourse were

compiled from was the “Perceptions of Graduate School” survey.

Nine GTAs in The Department of Biology, who taught Natural

Science Biology, were asked to complete the “Perceptions of

Graduate School Survey” after teaching one semester of non-majors

Natural Science Biology laboratory in the Spring of 2012. The

“Perceptions of Graduate School Survey” was created through a

literature review of GTAs and their beliefs, attitudes, and

perceptions of teaching, learning, students, and research.

The “Perceptions of Graduate School Survey” was designed to

uncover teacher beliefs. Puchta (1999) describes teaching beliefs

as “the guiding principles” of teacher practice. He describes how

teaching beliefs guide teaching practice. Based upon their

beliefs, teachers make generalizations about teaching, learning,

and students that shape the way they approach their work. Through

this iterative practice, they come to realizations that help them

to make sense of the world. Teachers form inner representations

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of cause and effect. Their beliefs influence the way teachers

think and act. Their presuppositions about students, teaching,

and learning may be displayed consistently or inconsistently in

their practices. Pajares (1992) describes the study of beliefs as

a “messy construct.” He expresses frustration in the lack of

precision that studying beliefs is afforded. Other authors have

connected teacher beliefs with practice both in university

academics (Kane, Sandretto, & Heath, 2002) and in physics

teaching assistants (Spike & Finkelstein, 2010).

The open-ended questions included in the “Perceptions of

Graduate School Survey” included:

1. Before you started teaching, what did you believe your

students that you were going to teach would be like?

2. Since you have been teaching, what do you believe about your

students, or students in general?

3. What do you wish all teaching assistants knew about

students, before they started teaching?

4. Before you started teaching, what did you think that “being

a teaching assistant” was going to be like?

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5. After you had taught, how did your perceptions of being a

teaching assistant change?

6. What do you wish all GTAs knew about teaching, before they

started teaching?

7. Before you started teaching, what did you believe “doing

research” would be like?

8. Since you have been doing research, how have your beliefs

about “doing research” changed?

9. What do you wish all GTAs knew before they started doing

research?

10. What did you believe that graduate school was going to

be like, before you started?

11. Did you have any surprises or challenges once you were

in graduate school?

There were nine GTAs who completed the Perceptions of

Graduate School Survey (See Table 4). Responses to the

Perceptions of Graduate School Survey were grouped into

categories of statements concerning students, teaching, research,

and graduate school. Responses were used in shaping the

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instructional training of new GTAs taking the “Effective

Teaching” course in the Fall 2012. GTAs who completed the

“Perceptions of Graduate School Survey” did not complete the Q

Sort.

Table 4 - Demographic Characteristics of TAs Completing the “Perceptions of Graduate School Survey”

9 graduate GTAs

Sex 4 females 5 males

Program 8 Master’s 1 Doctoral

Nationality 3 International 6 United States

Experience 4 first time

teachers

5 experienced

teachers

Statements from the Literature

The remaining statements populating the concourse were

compiled from an extensive review of the academic literature

concerning GTA and their experiences in graduate school

instructional training programs. Themes that had been identified

through a review of the literature while developing the SRQ and

the Perceptions of Graduate School Survey were further expanded

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to include statements made by GTAs in other studies. The theories

depicted in the literature reviews for the SRQ and the

Perceptions of Graduate School Survey were a combination of

Shulman’s (1986, 1987) theory of Pedagogical Content Knowledge

and Pajares’ (1992) theory of Teacher’s Beliefs. These studies

contained qualitative data, and included statements made during

case studies, interviews, focus groups, in emails, and in

communications with supervisors. These statements allowed the

researcher to provide a representative and balanced coverage of

the themes in the discourse surrounding GTAs and their

perceptions of graduate school.

Q Sample

Each of the 93 statements from the concourse were placed on

a strip of paper, and were physically sorted into piles that

contained statements which expressed a similar theme. For Strauss

and Corbin (1990), the links between expressions and themes are

“conceptual labels placed on discrete happenings, events, and

other instances of phenomena.” Themes, or categories, are the

classification of more discrete concepts. “This classification is

discovered when concepts are compared one against another and

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appear to pertain to a similar phenomenon. Thus, the concepts are

grouped together under a higher order, more abstract concept

called a category” (p. 61). As Stephenson said, a Q Set “may be

designed purely on theoretical grounds, or from naturally

occurring (ecological) conditions, or as required for

experimental purposes, to suit the particular requirements of an

investigation” (1952, p. 223).

The statements made by GTAs that populated the concourse

represented their perspectives about graduate school. The

researcher collected statements that broadly represented all the

identified themes of discourse around a topic – in this case, a

sample of statements that was representative of the various

statements made by GTAs about their graduate school instructional

training program in graduate school. The key themes and issues

that defined the subject matter for the study were identified by

the researcher, who was looking for ways to improve instructional

training programs, common views of GTAs concerning their

instructional training programs, and ways to alleviate GTA

frustration in their graduate school program. The statements were

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systematically identified according to theme by the researcher

and two Biology GTAs who did not take part in the Q Sort.

The Q Sample (see Appendix 2) was derived by selecting nine

representative statements from each of six categories of interest

to the researcher – teaching, learning, students, research,

challenges in graduate school, and GTA persistence in their

program – for a total of 54 statements in the Q Sample. This

represented a Fisherian design (Brown & Ungs, 1970). It is this

set of statements that was eventually presented to participants

in the form of the “GTA Perceptions of Graduate School Q Sort”

(See Appendix 3), or the research instrument (Van Exel & de

Graaf, 2005). The Q Sample represented subjectivity on a given

topic, in this case GTA viewpoints on graduate school.

Q Sort

For the “GTA Perceptions of Graduate School Q Sort” the 54

chosen statements were randomly numbered. Each statement was

typed on an individual strip of paper, about the size of an

address label. The statements were printed on white paper, cut

into strips, and placed in their own envelope. The statements

were administered in the form of an envelope of randomly numbered

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strips of paper (one statement to a strip) with which the

respondent was instructed to operate according to the condition

of instruction. The researcher was interested in the GTA’s own

point of view, and the GTA was instructed to sort the statements

into three piles, based upon their views of graduate school. The

conditions of instruction for this study are shown in Figure 5.

Figure 5 – Conditions of Instruction for “GTA Perceptions of

Graduate School Q Sort

The grid onto which the GTAs were asked to sort the statements

was a quasi-normal distribution. Although there has been some

debate about the merits of “forced” versus “free” distributions

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(Brown, 1968), quasi-normal distributions are most commonly

employed in Q Methodological research because of the statistical

advantages they yield (Kitzinger, 1987). This grid was chosen

because it accommodated all 54 statements, and yielded a “top

eight” most unlike my view, and most like my view categories. The

range of columns and the frequencies for statements was

4 – 4 – 5 – 5 – 6 – 6 – 6 – 5 – 5 – 4 – 4. That is four

statements were placed in column one, four in column two, five in

column three, and so on. The array positions for columns one

through eleven were given values of -5, -4, -3, -2, -1, 0, +1,

+2, +3, +4 and +5 for statistical analysis, as demonstrated in

Figure 6.

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The Pilot Study

A pilot study was conducted to test logistics and gather

information prior to the research study. The pilot demonstrated

the feasibility of the study, and tested the Q Sample instrument,

the instructions to the participants, the conditions of

instruction, and the factor analysis of the sorts. The pilot

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4 4 5 5 6 6 6 5 5 4 4Mostunlikemyview

neutral

Mostlikemyview

-5 -4 -3 -2 -1 0 1 2 3 4 5

Figure 5 - Distribution Grid for “GTA Perceptions of Graduate School Q Sort”

demonstrated that the materials did not need to be modified, and

were suitable for incorporation into the main study. When the

sorting for the pilot study was completed, the sorts were entered

into PQMethod, the software package design specifically for

analyzing Q Methodology data (Schmolck & Atkinson, 2002).

The analysis of the pilot study revealed three factors

concerning the views of New Biology GTAs and graduate school:

Factor 1 or “The Confident Teachers,” are confident in both

their teaching abilities, and their place in graduate school as a

GTA.

Factor 2 or “The Preferred Researchers” are characterized by

their preference for research over teaching, and their skepticism

about working with students.

Factor 3 or “The GTA to Professor” are characterized by

wanting to teach as they were taught by their favorite professor

and by wanting to use their time as a GTA to lead them towards

becoming a better professor.

The pilot study allowed the researcher to test Q Methodology

as a needs assessment tool. Because the pilot study demonstrated

that there were various viewpoints among the population of new

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Biology GTAs and their supervisors, Q Methodology could be used

to modify the existing training program. It was determined that Q

Methodology was an appropriate design for the study.

Data Collection Procedures

The researcher personally collected Q Sorts in two phases.

During the first phase, the Biology Lab Coordinator, the Lead

Biology Faculty Member, and the new GTAs sorted in their

“Effective Teaching” course. The researcher distributed the Q

Sorts, the instructions for the sort, and a copy of the IRB

“Informed Consent” letter (See Appendix 4) to each member of the

class. The basic concept of Q Sorting and instructions on how to

perform a Q Sort were explained to each participant in order to

ensure that the content was fully understood. They were

instructed to sort these statements from a range of -5 to +5

indicating how the statement was most unlike to most like their

view of being a Biology GTA. Statements that participants felt

neutral about were placed in the zero column, while those

statements they most strongly identified with were placed in the

positive number columns, and those they did not identify with

were placed into the negative number columns. The participants

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were then asked to complete short-answer exit interview

questions. When the participants completed their sort, the

researcher collected the statements and the completed grids.

There was no discussion during the sorting process by any of the

participants.

During the second phase, a second Biology Lab Coordinator

and the experienced GTAs were asked to participate after their

weekly Biology colloquium meeting. The researcher distributed the

Q Sorts, the instructions for the sort, and a copy of the IRB

“Informed Consent” letter to each participant, following the same

procedures as used with the new GTAs. The experienced GTAs openly

discussed their sorts as they were completing them, and their

discussion was tape recorded and transcribed.

Role of the Researcher

The researcher is a staff member in The Department of

Biology who supervises a non-major, undergraduate Biology lab,

and co-taught the “Effective Teaching” course for new Biology

GTAs, along with the Lead Biology Faculty Member. The researcher

obtained IRB approval prior to collecting data by requesting an

IRB Exemption (Appendix 5). The researcher submitted the “GTA

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Perceptions of Graduate School Q Sort” and used the instrument in

a commonly accepted educational setting, involving normal

educational practices (The “Effective Teaching” course and a

departmental gathering of GTAs) (Appendix 6). The researcher

explained the study to potential participants and distributed all

materials relating to the Q Sort. The researcher collected the

letters of consent (See Appendix 4) from willing participants

before conducting the Q Sorting activity. The researcher is

unable to make decisions about the hiring or firing, or

assignment of Biology GTAs.

Limitations

The limitations of this pilot study follow. The first

limitation is that Q Methodology is not generalizable to the

general population (Brown, 1980). With Q Methodology, statistical

reliability or the ability to generalize sample results to the

general population is of less concern because the results are the

distinct subjectivities about a topic that are operant or

measurable within the group of participants. In this research,

the viewpoints that arise are characteristic of only this group

of GTAs, at this university, in this department, at this point in

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time. This is the group of interest for the study thus the larger

population of GTAs in other content areas, at different

universities, or at different times is not the focus of the

research. The focus of the views within a specific group of

people is an important distinction within Q research. Q

Methodological results are not the percentage of the sample or

the general population that adheres to any of the operant

subjectivities (Thomas & Baas, 1992).

The second limitation is the Q Sample. The researcher

conducted post-sort focus group interviews during the “Effective

Teaching” course with the participants of this pilot study. These

interviews revealed that new GTAs believed some of the statements

used were irrelevant to their position, and other statements

seemed overly repetitive. Those statements have been noted, and

may be removed for future studies. Q Methodology “is useful in

that it allows the researcher to identify groups of participants

whose viewpoints are similar, and to examine their differences

from participants having alternative viewpoints. In other words,

Q Methodology employs a form of multivariate analysis that is

designed to identify the systematically different ways in which

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people respond to propositional statements about a particular

topic or issue (LeCouteur & Delfabbro, 2001, p. 209).” Though the

participants in the pilot study found some of the statements

questionable, the second set of sorters did not express the same

sentiments about the statements. Using the same Q Sort with the

proposed sorters as used with the pilot study to compare groups

is necessary.

The final limitation is the experienced GTAs. Collecting

sorts from the new GTAs as they begin their “Effective Teaching”

course provides the viewpoints of all the initial participants.

Some GTAs may leave the program after the “Effective Teaching”

course, or may leave prior to graduation, and thus have their

viewpoint removed from the study. GTAs who leave the program have

an important viewpoint that is important to include, but may be

hard to execute. Using Q Methodology as a needs assessment tool

may eventually be used to uncover viewpoints that exist among

GTAs who exit the program, but the enrollment statistics were not

included in this study.

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Summary

This chapter provided an overview of the research design,

the derivations of the general and specific research hypotheses,

and the research questions. Other sections designated the

participants and sampling procedures. The basic procedures for a

Q Methodology study were described in detail. The instrument

section described the compilation of the concourse, the Q Sample,

the Q Sort, the conditions of instruction, and the pilot study

conducted during the Fall semester of 2012. There was a detailed

descriptions of the methods used in this study. The statistical

treatment section explained how the results of the Q Sorts will

be factor analyzed and interpreted. The role of the researcher

and limitations of the study conclude the chapter.

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CHAPTER IV

RESULTS

The purpose of this chapter is to provide the demographic

information on the participants who sorted for this study, who

are new and experienced Biology Graduate Teaching Assistants

(GTAs), two Biology Lab Coordinators, and a Lead Biology Faculty

Member. This chapter further provides the results of the analysis

of the Q Sorts, and describes the results of the testing of the

specific research hypotheses. The various viewpoints that emerge

from the data, using factor analysis, are described.

Descriptive Demographics

The American Psychological Association Publication Manual

(2010) states that, when the participants in a research study are

human, certain information about them such as demographic

variables, the number of participants, the assignment to groups,

and other descriptors should be adequately described. This would

aid in assessing the results, generalizing the findings, and

making comparisons or replications. Q studies, however, are

better suited to the exploration of the specifics; the viewpoints

of specific people, specific groups, specific demographics, or

146

the viewpoints at specific institutions (Watts & Stenner, 2005).

Q Methodology is not a test of differences among people, it looks

for the similarities and differences between viewpoints (Van Exel

& de Graaf, 2005). Q allows individuals to self-categorize on the

basis of the Q Sort they produce. At the end of the analyses, we

may come to understand an individual in terms of their

association with a particular group or factor (Watts & Stenner,

2005). The results of a Q Methodological study can be used to

describe a population of viewpoints and not a population of

people (Risdon, Eccleston, Crombez, & McCracken, 2003). The

reporting of descriptive demographics allows the researcher to

look for patterns among sorters who share the same viewpoints.

There were 34 participants who completed the Q Sort, who are

considered the P-Set, or Person-Set (See Table 5) (McKeown &

Thomas, 1988; Van Exel & de Graaf, 2005). Participants in the

initial phase were nine master’s and eight doctoral-level new

GTAs in The Department of Biology in the Fall 2012 semester, who

were enrolled in an “Effective Teaching” course for new GTAs, and

the two supervisors of this course. A “new GTA” is defined as “A

graduate level student, who is seeking a master’s or doctoral

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degree through The Department of Biology in a large, research-

focused, degree granting university, who has less than one year

of formal teaching experience, and who teaches an undergraduate-

level laboratory for approximately 20-hours a week, in exchange

for a fee-remission. This GTA is currently enrolled in an

"Effective Teaching" GTA training program.”

The Biology Lab Coordinator and Biology Lead Faculty Member

sorted twice each during the initial phase of the study. They

first sorted under the conditions of instruction, “Sort based

upon your view of how a new Biology GTA would perceive graduate

school,” and then completed a second sort under the condition of

instruction, “Sort based upon your view of how an experienced

Biology GTA would perceive graduate school.” This type of sort

would be considered a Theoretical Q Sort, which can be

constructed under a theoretical condition of instruction, to

represent the point of view of a participant (Brown, 1980). The

Theoretical Q Sorts of the supervisors were included in this

study because of the supervisors’ large degree of involvement

with the instructional training of Biology GTAs. Both the Biology

Lab Coordinator and the Lead Biology Faculty Member were

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supervisors of this cohort of GTAs, and were most familiar with

the viewpoints of both new Biology GTAs and experienced Biology

GTAs. The Biology Lab Coordinator had never been a GTA, despite

working closely with them, but the Lead Biology Faculty Member

had held a GTA position when completing his graduate degree. The

researcher was interested in whether the supervisors of Biology

GTAs would have similar or differing viewpoints from the actual

GTAs. In this phase, there were 21 total Q Sorts collected from

17 new Biology GTAs, the Biology Lab Coordinator theoretically

sorting twice, and the Biology Lead Faculty Member theoretically

sorting twice, for a total of 19 participants in the P-Set (See

Table 5).

There were an additional 10 Q Sorts collected from the

experienced GTAs and one sort from a second Biology Lab

Coordinator, in the second phase of the study. These experienced

GTAs had completed an "Effective Teaching" GTA training program.

Additionally, these participants attended the weekly mandatory

Biology departmental colloquium for master’s degree students,

meeting on a Thursday evening, and were asked to participate in

the study. The additional Biology Lab Coordinator was asked to

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sort under the condition of instruction “Sort based upon your

view of how an experienced Biology GTA would perceive graduate

school.” Of the GTAs who attended this colloquium, 100%

participated in the Q Sort.

The final set of Q Sorts were collected from experienced

GTAs after their doctoral colloquium, and consisted of four

experienced GTAs. The experienced GTAs had completed an

"Effective Teaching" GTA training program. Of the doctoral

students who attended this colloquium, 100% participated in the Q

Sort.

There were a final total of 34 participants in the P-Set,

and 36 Q Sorts. There were a final total of 16 doctoral GTAs, and

15 master’s degree GTAs who completed sorts. Because the Q Sorts,

conditions of instruction, and analysis remained the same, sorts

from all the phases of the study could be analyzed together to

address the purpose of this study. The demographics of the P-Set

are further described in Table 5. There were roughly the same

number of males and females completing Q Sorts. There were only a

small number of International GTAs (two) completing the Q Sort.

There was a wide variety of formal and informal teaching

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experience among the GTAs, with only two GTAs having no teaching

experience at all.

Table 5 – Demographics of New and Experienced Biology GTA

Number PercentParticipation Rate 36 Sorts 100%Session of Sort Completion Effective Teaching Course 21 58% Colloquium 15 42%Type of Participant New GTAs 17 48% Experienced GTAs 14 42% Biology Lab Coordinator 3 7% Biology Faculty Member 2 5%Degree Track Doctoral 16 52% Masters 15 48%Gender Male 19 52% Female 17 48%Origin International 2 6% United States 34 94%Teaching Experience

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None 2 6% Informal 6 17% Formal 28 77%

Data Collection

Data collection for this study occurred during the Fall 2012

semester. Data used in this research study were Q Sorts taken

from two groups of participants. The first group of participants

included the new Biology GTAs, a Biology Lab Coordinator, and the

Lead Biology Faculty Member, sorting during their “Effective

Teaching” course. The second group of participants included the

experienced Biology GTAs and an additional lab coordinator,

sorting after a master’s or doctoral weekly mandatory Biology

departmental colloquium meeting.

The P-Set were coded based on certain characteristics. The

participant identifiers represented more detail about each

participant, to aid in factor interpretation and participant

identification. Table 6 explains the coding system used to

identify each participant. As an example, Sorter #1 (coded

den32mf) was a doctoral student (d), an experienced GTA (e), born

and educated in the United States (n), who was 32 years old (32),

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male (m), and had formal teaching experience (f). Another

example, Sorter #11 (coded men23mu) was a master’s degree student

(m), who was an experienced GTA (e), born and educated in the

United States (n), who was 23 years old (23), male (m), and did

not answer the question about teaching experience on the post-

sort interview questions (u). Finally, Sorter #36 (coded blc1exp)

was the first (1) The Biology Lab Coordinator (BLC),

theoretically sorting as an experienced GTA (exp), under the

conditions of instruction, “Sort based upon your view of how an

experienced Biology GTA would perceive graduate school.”

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Table 6 - Coding System for Study Participants

Category Identifier MeaningProgram M Master’s degree

D Doctoral degree

BLC Biology Lab Coordinator

BFM Biology Lead FacultyMember

Experience N NewE Experienced

International Status Y International Student

N Born and Educated inthe United States

Age (numerical) Age in YearsSex M Male

F Female

Teaching Experience F Formal Teaching Experiences

I Informal Teaching Experiences

U Unanswered

Q Sorting is the process in which participants are asked to

sort a Q Sample, developed from a concourse. The participants

sorted based on the same set of conditions of instruction used in

the pilot study, “Based upon your views of graduate school, place

each statement into one of three piles.” The sorting was 154

completed based on their perception of how strongly the

statements were like their views, unlike their views, or if they

had a neutral feeling about the statement. Sorting occurred via a

predetermined number of groups (4 – 4 – 5 – 5 – 6 – 6 – 6 – 5 – 5

– 4 – 4) between the two ends of the continuum (-5, -4, -3, -2, -

1, 0, +1, +2, +3, +4, +5). The ends of the continuum ranged from

most unlike my view (-5) to most like my view (+5).

Unlike traditional surveys that require participants to

answer each question independently of their other responses, Q

Sorting enables participants to consider the Q Statements in

relation to each other, creating a focalization on the

participant’s individual subjectivity on the subject (Bowe,

2010). Prasad (2001) argues that use of the forced choice method

(forced matrix) means that the respondents have to consider their

attitudes more carefully, which can bring out true feelings in

response.

Data Analysis

All of the Q Sorts were analyzed using PQMethod (Schmolck &

Atkinson, 2002). Because there is no dedicated function in SPSS

for flagging, or creating the descriptive table required for

155

interpretation of the factors used in Q Methodology, and PQMethod

has been purposefully built to do Q Methodology analyses, it is

appropriate to use in a Q Study (Brown, 1980; McKeown & Thomas,

1988; Newman & Ramlo, 2010; Schmolck & Atkinson, 2002). According

to McKeown and Thomas (1988), “data analysis in Q Methodology

typically involves the sequential application of three sets of

statistical procedures: correlation, factor analysis, and the

computation of factor scores” (p. 46). After each respondent has

provided his own ranking of the statements, the various ranks are

correlated, and the correlation matrix is factor analyzed. A

factor in this case represents a group of persons who have ranked

the statements in essentially the same order - persons who have

displayed a common perspective (Brown & Ungs, 1970).

The higher the factor loading, the more highly that sorter

is correlated with that factor (Newman & Ramlo, 2010; Ramlo,

2008). Consequently, those sorters whose views are similar are

highly correlated with the same factor. Thus, the factor loadings

determine who loaded on which factor (Brown, 1980). The factors

represent viewpoints or perspectives which exist with respect to

the issue under consideration. The factors which result from a Q

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Study, therefore, in a very real sense are results of behavior -

that is, they exist as the consequence of a group of respondents

having responded in the same fashion. “Viewpoints,” in this

usage, are operant, or measurable. Factors in Q Methodology

studies arise from the actual concrete operations of persons as

they model their viewpoints; a factor is the result of behavior

(Brown & Ungs, 1970). The factor-categories are genuine, as

opposed to ad hoc categorical, and reflect true viewpoint

segmentation. They are more genuinely "operational definitions"

of this-or-that attitude, since whatever it is they are

definitions of - for example, a pro-labor viewpoint has been made

manifest by virtue of behavioral operations expressed through the

medium of Q Methodology (Brown & Ungs, 1970).

Q analyses recognize the sorted-items (statements) as the

sample, and the participants as the variable (McKeown & Thomas,

1988). The fact that these opinions were “amenable to numerical

treatment opens the door to the possibility of clarity in

understanding through the detection of connections which unaided

perception might pass over (Brown, 1991, Section 5).” Factor

analysis in Q Methodology reveals how many different perspectives

157

there are (Brown, 1980, 1993). For example, in the pilot study,

the factors indicated three different perspectives new GTAs

possess about graduate school. The GTAs who share a common view

define the same factor. In the pilot study, there were three

different factors, or “viewpoints,” among new Biology GTAs.

Analysis and Interpretation

The three factor matrix shown in Table 7 is the result of

centroid factor analysis and Varimax rotation. The combination of

centroid and Varimax was chosen because it produced a clear and

detailed description of the data. Other combinations of principle

component analysis (PCA), centroid, Varimax, and hand rotations

were completed, but the particular 3-factor solution revealed the

most connections, and was in line with post-sort interview

questions. The three distinct factors that emerged from this

combination of data analysis techniques are shown in Table 7,

with automatic pre-flagging. An ‘X’ next to the factor loading,

in bold, for that participant, indicating a loading on that

factor. The X’s indicate a sort that represents that particular

factor/viewpoint. Participants who did not load significantly on

any of the factors, and therefore were not flagged by the

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software, are not indicated by any X’s. Their sorts are not

included in a factor view and were not included in the

development of the tables related to these views.

The descriptions and analysis of the factor descriptions are

determined by only those Q-Sorters who are flagged on that factor

(Brown, 2009; McKeown & Thomas, 1988; Newman & Ramlo, 2010). It

is necessary to flag sorters before the analyses produce a report

that involves a variety of tables (Newman & Ramlo, 2010). These

tables are developed statistically using PQMethod software

(Schmolck & Atkinson, 2002), and they help the researcher’s

description of the views developed from the factor scores. Q

Methodology maintains the relationship among themes within the

data as it minimizes the impact of the researcher’s frame of

reference (Stainton Rogers, 1995). It minimizes this impact

through complex statistical analysis including correlation and

factor analysis (Brown, 1980; Newman & Ramlo, 2010; Stephenson,

1953).

Table 7 - Factor Matrix with X Indicating a Defining Sort

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QSORT 1 2 3

1 den32mf 0.4890X 0.4702 -0.02212 den30ff 0.4274 0.6692X 0.09743 den29mf 0.5002X 0.323 0.05884 den32mf 0.1609 0.7388X 0.2965 men23mf 0.1905 0.7200X 0.08826 men24ff 0.5849X 0.2318 0.13987 men24ff 0.4313X -0.0302 0.39478 den27fu 0.7022X 0.4709 -0.07119 men24ff 0.7724X 0.1678 0.086610 blc2exp 0.7854X 0.1438 -0.047211 men23mu -0.3709 0.4872 0.369912 men47mf 0.8464X 0.2891 0.067413 men24mf 0.1147 0.084 0.7859X14 den40mf 0.7279X 0.1966 0.032815 den30mf 0.5182X 0.1834 0.246216 blc1new 0.136 -0.1778 0.5292X17 dnn30mi 0.9171X 0.0002 0.049618 dnn27mf 0.2338 0.5255X -0.170519 dnn22fi 0.7844X 0.1507 -0.026720 dnn25mf 0.0652 0.7488X -0.059521 dny30ff 0.4365X 0.2257 0.13622 bfmexp 0.0395 0.5303X 0.110123 bfmnew -0.4248 0.2074 0.426624 mnn29fi 0.2372 0.0944 0.6970X25 mnn25mu 0.6360X 0.2844 0.278826 mnn22fi 0.263 0.2558 0.3808X27 mnn25fi 0.2914 0.4605X 0.282828 dnn22mu 0.3577 0.6426X 0.112329 mnn23fi 0.7135X 0.393 -0.054330 mnn23fi -0.0384 -0.0769 0.4103X31 mnn24ff 0.8596X 0.2199 -0.095332 mnn22mf 0.8530X 0.1194 0.1228

160

QSORT 1 2 3

33 dnn23mf 0.3345 0.6050X -0.025834 dnn23mi -0.0841 0.3697 0.6504X35 mny24fi 0.5793X 0.0828 0.316636 blc1exp 0.2042 0.4141X -0.218

% expl.Var. 27% 15% 9%# per factor 18 10 6% per factor 50.0% 27.7% 17.6%

Note: The coding for the Q Sort ID includes the program (M = Masters, D = Doctoral, BLC = Biology Lab Coordinator, LFM = Lead Faculty Member), experience (n = new, e = experience, international status (yes = international, no = American), age inyears, sex (M = male, F = female), and teaching experience (f = formal, I = informal, u = unanswered).

As indicated in Table 7 above, there were three factors that

emerged. There were 18 sorts that were represented by Factor 1,

and their loadings ranged from 0.43 to 0.91. Marked with an X,

these are sorts 1, 3, 6, 7, 8, 8, 9, 10, 12, 14, 15, 17, 19, 21,

25, 29, 31, 32, and 35. This factor was named "The Emerging

Teacher" by the researcher. The participants in the group

included ten females and eight males. There was an even split

with nine experienced and nine new GTAs. Seventeen out of the 18

sorts included participants with teaching experience, 12 with

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formal experience, four with informal experience, and two who

provided no answer.

Ten sorts were represented by Factor 2. Their loadings

ranged from 0.41 to 0.75. Marked with an X, these are sorts 2, 4,

5, 18, 20, 22, 27, 28, 33, and 36. This factor was named “The

Preferred Researcher” by the researcher. The participants in the

group included three females, and seven males. There was an even

split with five experienced and five new GTAs. Every participant

had taught before, but one only had informal teaching experience.

Six sorts were represented by Factor 3. Their loadings

ranged from 0.38 to 0.79. Marked with an X, these are sorts13,

16, 24, 26, 30, and 34. This factor was named “The Anxious GTA”

by the researcher. The participants in this group included four

females and two males. All of these sorters were new GTAs except

for one doctoral student. Every GTA had taught before, but mostly

(four sorts) in an informal setting, with two teaching in a

formal setting.

Finally, there were two sorters who did not load on any

factor. These two sorters were sort 11 and 23. One no-loading

sort was from a male Master’s degree, experienced GTA who was 23,

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and born and educated in The United States. The other was from

The Lead Biology Faculty Member, theoretically sorting as a new

GTA. The factors will be more fully described later in Chapter

IV.

After calculating factor scores, two of the tables that are

developed for analysis in Q Methodology are consensus and

distinguishing factor statements, which allow the researcher to

explore what is common among and different between the factors

(Brown, 1980, 1993, 2009; McKeown & Thomas, 1988, Newman & Ramlo,

2010). In order to determine the distinguishing statements, a

difference score is calculated within PQMethod. A statement’s

factor score is the normalized weighted average statement score

(Z-score) of respondents that define that factor. Van Exel (2005,

p.9) describes difference scores as follows: “The difference

score is the magnitude of difference between a statement’s z-

score on any two factors that is required for it to be

statistically significant. When a statement’s score on two

factors exceeds this difference score, it is called a

distinguishing statement.” When a statement is not distinguishing

between any of the factors it becomes a consensus statement (van

163

Exel, 2005). Consensus statements are those statements that are

common among pairs of factors.

Table 8 below shows the factor scores, or where those

statements would appear in the ideal sort each factor, with the

distinguishing statements for each factor marked by an asterisk

(*), and the consensus statements marked by a cross (†).

Table 8 - Factor Values for Each Statement

Factor

Arrays

No

.Statement 1 2 3

1All my students are capable of understanding

Biology †2 1 3

2Being a good teacher is as important as

being a good researcher3

-

1*4

3Being a TA helps me ask better questions in

my research1 0 0

4Being a TA will help me to be a good

professor someday5* 2 2

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Factor

Arrays

No

.Statement 1 2 3

5I am good at creating a respectful classroom

environment4* 1*

-

1*

6I believe I know what it takes to be a good

researcher0 3* 0

7I came to grad school mainly so I could do

research

-

2*5* 3*

8I can balance being a good teacher with

being a good student4 4 0*

9 I dislike teaching, and wish I could spend more time on my research

-5 -1*

-3*

10I don't think teaching requires a lot of

emotion †-2 -2 -3

11 I feel like an outsider, and that people at grad school won't accept me

-4 -4 -1*

12I feel like I could go into teaching as a

profession5* 0* 3*

13 I feel like I need to constantly monitor my -1 -2 -

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Factor

Arrays

No

.Statement 1 2 3

students for cheating 4*

14 I feel like I'm a good teacher because I am closer in age to my students

-1 -3 -2

15I feel like it will be easy to manage my

class1 1

-

3*

16I feel like my fellow TAs will help me to

teach better1 1 5*

17I feel like students look at me weird when I

tell them I'm a TA-4 -5 -5

18I feel overwhelmed with work my advisor

gives me-2

-

1*-3

19I feel pretty comfortable using technology

in my class †2 5 1

20I feel pretty confident that I'm a good

teacher5* 4*

-

5*

21 I feel self-confident when I teach 5* 4* -

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Factor

Arrays

No

.Statement 1 2 3

5*

22 I have a lot of anxiety about teaching, because I don't know what to expect

-5 -5 2*

23I have had dreams about my teaching or

research-1 0 0

24I have lost sleep because I'm worried about

teaching †-4 -4

-

2*

25 I have no idea what students think about me,and that makes me uncomfortable

-4 -4 4*

26 I have no idea what the level of understanding is with these students

-2 -3 3*

27 I have to repeat myself over and over to getthese students to understand me †

-1 0 -1

28 I know the university policies that relate to my research 0 -1 -1

29 I know what attributes make a good teacher 3 3 0*

30I know what the Biology department expects

from my teaching0 1

-

5*

31 I know what the department expects from my 1* 3* -

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Factor

Arrays

No

.Statement 1 2 3

research 4*

32 I learned best by actively doing labs 1 4 2

33I learned best by listening to professors

teach0*

-

2*1*

34 I like doing research over teaching-

3*5* 1*

35 I like doing teaching over research 3*-

5*

-

4*

36I think all this teaching gets in the way of

my research

-

5*2*

-

2*

37 I think most of my students learn in a way that's similar to the way I learn †

-1 -2 -1

38 I think one of the most important things about being a TA is being ethical †

3 1* 3

39 I think research is very challenging 1* 3 4

40 I think some people are natural teachers † 3 5 5

41 I think teaching is very challenging - - 5*

168

Factor

Arrays

No

.Statement 1 2 3

2* 3*

42I think that I give good teaching

presentations4* 2*

-

2*

43 I think you can be "taught to teach"-

1*3 1

44I want all students to actively participate

in my class †4 2* 4

45I want to teach the same way my favorite

professor taught2* 0* 5*

46 I worry that certain students in my class might know more about Biology than I do

-3 -5*

-2

47If I teach well, I will get good student

evaluations †0 -1 0

48I'm worried that the students won't be able

to understand me-3 -3 2*

49 I've had family problems because of the pressures of graduate school†

-5 -4 -4

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Factor

Arrays

No

.Statement 1 2 3

50Most of my students will do just enough to

get by0* 2 1

51My students will like Biology because I can

make it interesting2

-

1*1

52 My students will respect me because I'm fair 2 0* 2

53Sometimes I worry that I might have chosen

the wrong career †-3 -2 -1

54 Using social media (like Twitter or Facebook) helps me to feel like I’m not alone†

-3 -3 -3

Note: Distinguishing Statements marked by *, and consensus

statements marked by †

A table of the distinguishing statements specifically for

each factor will be presented in the analyses for that factor,

within the next section, to aid the reader within the

interpretation of each factor. The consensus statements are also

170

discussed in detail within the next section, while their

interpretation will be revisited in the recommendations section

of Chapter V. A representative sort is created from the sorts of

those who are represented by a particular factor. This

representative sort is one sort that represents that

factor/viewpoint (Brown, 1980; Brown, 2008; McKeown & Thomas,

1988, Newman & Ramlo, 2010).

Brown & Good (2010, p.7) describe how the factor scores are

calculated, “The factor scores are then calculated by multiplying

each statement’s Q Sort score by the weight and then summing each

statement across the weighted Q Sorts comprising the factor, with

weighted statement sums then being converted into a factor array

presented in the form of the original metric.” An example of a

representative sort from Factor 1 is show in Figure 7 below and

contains the same information about Factor 1 contained in Table

8.

171

Generally, the statements ranked at the extreme ends of a

representative sort are called the characterizing statements. In

other words, the statements that ranked as most like my view and

least like my view are used to provide a starting point to

describe the view represented by that factor. These statements

demonstrate to the researcher the statements that sorters who

load on this factor feel the most strongly about. The consensus 172

Least

likemyview

neutral

Mostlikemy

view-5 -4 -3 -2 -1 0 1 2 3 4 5

22 17 53 7 27 30 39 52 40 8 12

49 25 54 41 23 50 32 51 35 44 20

36 24 34 26 43 6 15 19 2 5 4

9 11 46 10 14 33 3 45 38 42 21

48 18 13 47 16 1 29

37 28 31

Figure 6 – Representative Sort for Factor 1

and distinguishing statements are used to illuminate the

similarities and differences between the factors respectively. To

further understand an individual participant’s sort, it is

generally advisable to conduct a post-sort interview to have the

participant explain to the researcher why the sort was arranged

the way it was. This can allow the researcher to gain

confirmation of the analysis and/or further insight into the

factor’s meaning (McKeown & Thomas, 1988; van Exel, 2005; Brown,

2009). For this study, the researcher asked post-sort questions

on the sorting grid sheet and allowed the participant to self-

report about the decision making process used during the sorting

process. The questions were:

1. Tell us why you selected the four statements you placed

under +5 (most like my view)?

2. Tell us why you selected the four statements you placed

under -5 (least like my view)?

3. Please describe your decision-making process during the

sort. Did you gain insight about your views as you sorted

the statements? If so, please describe.

4. What are you planning to do after graduate school?

173

5. Briefly (a few sentences) describe what you would

like to get out of a TA training program:

The responses to these questions allowed the researcher to

further identify the commonalities among those sorters who were

represented by the same factor as well as to help define and

articulate the factor itself. Comments that help clarify the

interpretation of each of the factors will be included in the

next section, along with the description of each factor.

Factor 1

In this study, the researcher asked the GTAs to sort 54

statements based upon their views about graduate school and being

a GTA. The analysis resulted in three views or factors. Of the 36

Q Sorts, 18 were represented by Factor 1, or what the researcher

has named "The Emerging Teacher". These GTAs are those who feel

confident that they are good teachers, and that they could go

into teaching as a profession. Table 9 and Table 10 contain the

top eight most-like and least-like statements for this factor,

174

respectively. Table 11 indicates the distinguishing statements

for Factor 1.

Table 9 - Eight Most-Like My View Statements for Factor 1 "The Emerging Teacher" with a † indicating a Distinguishing Statement.

No. StatementGrid Pos.

12I feel like I could go into teaching as a profession † 5

20I feel pretty confident that I'm a good teacher† 5

4Being a TA will help me to be a good professor someday † 5

21 I feel self-confident when I teach † 5

8 I can balance being a good teacher with being agood student

4

44I want all students to actively participate in my class 4

5I am good at creating a respectful classroom environment † 4

42 I think that I give good teaching presentations 4

The top four “most like my view” statements (Table 9) (12, I

feel like I could go into teaching as a profession; 20, I feel

pretty confident that I'm a good teacher; 4, Being a TA will help

175

me to be a good professor someday; 21, I feel self-confident when

I teach) indicate confidence in teaching abilities. This is

further elucidated in that 12 of the 18 GTAs who were represented

by this factor specifically listed professor or teaching as their

desired career path (See Table 12). They also provided

preferences for skills they would like to acquire from The

“Effective Teaching” course. Most of the GTAs requested advice

about obtaining teaching skills, ranging from teaching strategies

and classroom management, to inspiring students. Over half of

these GTAs were experienced, and 13 of the 18 participants had

formal teaching experience. Multiple GTAs expressed that they

were confident people in general, and participant MNN23FI

indicated that teaching was a “natural extension” for her, and

that she enjoyed it.

Table 10 - Eight Least-Like My View Statements for Factor 1 "The Emerging Teacher" with a † indicating a Distinguishing Statement.

No. StatementGrid Pos.

17 I feel like students look at me weird when I tell them I'm a TA

-4

25 I have no idea what students think about me, -4176

and that makes me uncomfortable24 I have lost sleep because I'm worried about

teaching-4

11 I feel like an outsider, and that people at grad school won't accept me

-4

22 I have a lot of anxiety about teaching, becauseI don't know what to expect

-5

49 I've had family problems because of the pressures of graduate school

-5

36 I think all this teaching gets in the way of myresearch †

-5

9 I dislike teaching, and wish I could spend moretime on my research †

-5

GTAs loading on Factor 1 also indicated a preference for

teaching over research, as demonstrated by the distinguishing

statements for this factor (Table 11). Two statements (35, I like

doing teaching over research; 39, I think research is very

challenging), indicate that this group of GTAs expresses less

confident about research than they do teaching. Statement 31, I

know what the department expects from my research, and statement

7, I came to grad school mainly so I could do research, were

neutral to negative in a representative sort by these GTAs.

Participant MNN24FF said, “I love TAing, so I really don’t get

anxiety about it. I can always seek help from [the biology lab

177

coordinator]…. my research, however….” Participant MNN25MU said,

“Teaching labs keeps me immersed in conducting and setting up

experiments.” He went on, “Research for me has been a bit

strenuous for my first semester, but I believe teaching will be

beneficial for research.”

Table 11 - Distinguishing Statements for Factor 1 " The Emerging

Teacher ".

Factors

1 2 3

No. Statement

Rank Score

Rank Score

Rank Score

12 I feel like I could go into teaching as a profession

5 0 3

20 I feel pretty confident that I'ma good teacher

5 4 -5

4 Being a TA will help me to be a good professor someday

5 2 2

21 I feel self-confident when I teach

5 4 -5

5 I am good at creating a respectful classroom environment

4 1 -1

42 I think that I give good teaching presentations

4 2 -2

35 I like doing teaching over research

3 -5 -4

45 I want to teach the same way my favorite professor taught

2 0 5

39 I think research is very 1 3 4178

Factors

1 2 3

No. Statement

Rank Score

Rank Score

Rank Score

challenging31 I know what the department

expects from my research1 3 -4

50 Most of my students will do justenough to get by

0 2 1

33 I learned best by listening to professors teach

0 -2 1

43 I think you can be "taught to teach"

-1 3 1

7 I came to grad school mainly so I could do research

-2 5 3

41 I think teaching is very challenging

-2 -3 5

34 I like doing research over teaching

-3 5 1

36 I think all this teaching gets in the way of my research

-5 2 -2

9 I dislike teaching, and wish I could spend more time on my research

-5 -1 -3

Table 12 - Post-Sort Interview Responses for Factor 1

Code Specific Teaching Experience

Career plan GTA program Preferences

BLC2EXP 4 years Teach N/A (Biology Lab Coordinator)

DEN27FU 7 years Post Doc to Professor

Teaching strategies,delivery, classroom management

DEN29MF 8 years TA, Field

NGO (Non-Governmental

Add to Teaching

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Code Specific Teaching Experience

Career plan GTA program Preferences

instructor, lecture 1 semester

Organization)/Conservation Advocacy

Portfolio

DEN30MF undergrad TA, ESL

Post Doc to Professor

clear expectations, classroom management, teachingportfolio prep, how to design courses

DEN32MF 5 years TA, high school, camp, Jr. high

Professor Help students excel

DEN40MF 10 years, youth group, church

Unsure clear expectations from courses, bettermatching of TAs to abilities (less favorite playing)

DNN22FI tutoring, research lab

Professor looking to find her teaching style

DNN30MI school in China

Professor mentorship program, individual lab prep

DNY30FF 4 semesters Academia or research

teaching skills

MEN24FF 5 semesters asa grad studentIntro, Micro, Genetics

Lab Tech, or get a teaching degree

How to break down procedures, work through processes, up to date with principles, diverse learners

MEN24FF 4 semesters A+P

Med school Gain confidence, be more comfortable in front of students

MEN24FF 4 semesters Vet school or PA school

public speaking training

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Code Specific Teaching Experience

Career plan GTA program Preferences

MEN47MF 20 years high school, TA 2 years

PhD (could teach a TA training program)

MNN22MF 5 semesters Ph.D. to professor inspire studentsMNN23FI coaching,

horseback riding

Vet school or PA (Physicians’ Assistant) school

situational knowledge, diverse students, handling unique circumstances

MNN24FF 3 semesters PA (Physicians’ Assistant) program

how to motivate students, inspire people to want to learn

MNN25MU Unanswered PhD, consulting how to prepare, communication, refreshing protocols, procedures

MNY24FI 3 semesters informal

Pharmaceutical or teach

classroom management

Note: The coding for the Q Sort ID includes the program (M = Masters, D = Doctoral, BLC = Biology Lab Coordinator, LFM = Lead Faculty Member), experience (n = new, e = experience, international status (yes = international, no = American), age inyears, sex (M = male, F = female), and teaching experience (f = formal, I = informal, u = unanswered).

Five GTAs who were represented by Factor 1 described, in

response to post-sort questions, how it was easy to pick

statements that were least-like their views, but it was difficult to

prioritize what was like their views. Participant MNN22MF

described how he could “instantly discredit anything that 181

suggested teaching is so unimportant.” He also described how he

felt he was “inherently a teacher” because he “possessed factors

like patience and good communication skills.” This sentiment is

expressed by the eight least-like-my-view statements, which are

noted in Table 10.

Not only do the most-like statements in Factor 1 indicate

teaching confidence, the least-like statements indicate that this

GTA group has not encountered many of the negative sentiments

that are often expressed in the literature, such as anxiety about

teaching, family problems from pressures of graduate school, or

wishing they could spend less time teaching and more time

researching. These GTAs expressed in their post-sort interview

questions that “I knew what I was getting into (DNN22FI),” and “I

know I chose the right career path (DNY30FF).” Another

experienced Factor 1 GTA noted, “I do not rely on teaching or

classes for my social life (DEN40MF).”

As Factor 1 GTAs described their decision-making processes

during the sort, some expressed that the activity gave them

insight into who they were as teachers, what other GTAs may be

feeling, or that it reinforced things they already knew about

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themselves. Participant DEN30MF described his insight, after the

sort, “It made me think more clearly about how I teach and how I

feel, rather than how I WANT to feel or think I should, about

teaching.” Participant MNN24FF said, “I definitely learned things

about myself, and this forced me to weigh situations out in my

head.” Participant DEN40MF described how he “was not comfortable

in being constrained to equalized piles, and limited to ranges. I

would have much more in the -5, -4, -3 piles.”

Participant MNN23FI described what she would like to get out

of a TA program, “I would like to get answers to certain

situations and possibly insight into different types of

students/circumstances I may be unaware of, because I was a

different type of student than what I teach.” Multiple GTAs

suggested they would like classroom management skills, teaching

strategies, and how to better work with students. Participant

MNN24FF suggested that learning about how different types of

students learn would help her “to become a better student

myself.” She indicated that learning how to help students want to

learn, this “might stimulate and inspire me to become a better

student myself.”

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“The Emerging Teachers” are GTAs in this group who feel

confident in their teaching abilities, and express interest in

teaching as a profession. This factor is characterized by

expressions of confidence in their teaching abilities, self-

confidence as teachers, and that their position as GTAs will help

them to become better teachers, as statements that are most-like

their views. GTAs who were represented by this factor ranked

statements about anxiety about teaching, family problems, lack of

confidence in their abilities, and feeling like an outsider as

least-like their views. The majority of GTAs who were represented

by this factor specifically listed professor or teaching as their

desired career path (See Table 12). This factor also included the

second Biology Lab Coordinator.

Factor 2

Of the 36 Sorts, 10 were represented by Factor 2, or what

the researcher has named "The Preferred Researcher.” These GTAs

are those who came to graduate school mainly so they could do

research, and prefer research over teaching. This is further

detailed in the post-sort interview questions that these GTAs

completed. Table 16 demonstrates that almost all of the GTAs who

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were represented by Factor 2 described their career aspirations

as “academia or research.” These career aspirations further

supported the researcher’s naming of Factor 2. The Lead Biology

Faculty Member and the Biology Lab Coordinator, theoretically

sorting as experienced GTAs were both included in this factor.

Table 16 helped substantiate this view, which was further

supported by the Q Sorts. Table 13 and Table 14 contain the top

eight most-like and least-like statements for this factor,

respectively.

Table 13 - Eight Most-Like My View Statements for Factor 2 "The Preferred Researcher” with a † indicating a Distinguishing Statement.

No. StatementGrid Pos.

7 I came to grad school mainly so I could do research †

5

34 I like doing research over teaching † 540 I think some people are natural teachers 519 I feel pretty comfortable using technology in

my class5

32 I learned best by actively doing labs 48 I can balance being a good teacher with being a

good student4

21 I feel self-confident when I teach † 420 I feel pretty confident that I'm a good teacher 4

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No. StatementGrid Pos.

†

Table 14 - Eight Least-Like My View Statements for Factor 2 "The Preferred Researcher” with a † indicating a Distinguishing Statement.

No. Statement

Grid

Pos.

25 I have no idea what students think about me, and that makes me uncomfortable

-4

24 I have lost sleep because I'm worried about teaching

-4

11 I feel like an outsider, and that people at grad school won't accept me

-4

49 I've had family problems because of the pressures of graduate school

-4

22 I have a lot of anxiety about teaching, becauseI don't know what to expect

-5

17 I feel like students look at me weird when I tell them I'm a TA

-5

35 I like doing teaching over research † -546 I worry that certain students in my class might

know more about Biology than I do †-5

Table 15 - Distinguishing Statements for Factor 2 "The Preferred

Researcher".

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Factors

1 2 3

No

. Statement

Rank

Score

Rank

Score

Rank

Score

7 I came to grad school mainly so I could do research

-2 5 3

34 I like doing research over teaching

-3 5 1

21 I feel self-confident when I teach

5 4 -5

20 I feel pretty confident that I'ma good teacher

5 4 -5

6 I believe I know what it takes to be a good researcher

0 3 0

31 I know what the department expects from my research

1 3 -4

44 I want all students to actively participate in my class

4 2 4

36

I think all this teaching gets in the way of my research

-5 2 -2

42 I think that I give good teaching presentations

4 2 -2

38 I think one of the most important things about being a TA is being ethical

3 1 3

5 I am good at creating a respectful classroom environment

4 1 -1

45 I want to teach the same way my favorite professor taught

2 0 5

52 My students will respect me because I'm fair

2 0 2

12 I feel like I could go into teaching as a profession

5 0 3

18 I feel overwhelmed with work my advisor gives me

-2 -1 -3

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Factors

1 2 3

No

. Statement

Rank

Score

Rank

Score

Rank

Score

2 Being a good teacher is as important as being a good research

3 -1 4

9 I dislike teaching, and wish I could spend more time on my research

-5 -1 -3

51 My students will like Biology because I can make it interesting

2 -1 1

33 I learned best by listening to professors teach

0 -2 1

41 I think teaching is very challenging

-2 -3 5

35 I like doing teaching over research

3 -5 -4

46 I worry that certain students inmy class might know more about Biology than I do

-3 -5 -2

The top four “most like my view” statements (7, I came to

grad school mainly so I could do research; 34, I like doing

research over teaching; 40, I think some people are natural

teachers; 19, I feel pretty comfortable using technology in my

class) indicate preference for research activities. This factor

is dominated by six doctoral GTAs, with only two master’s GTAs

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loading on this factor. Also, both the Biology Lead Faculty

Member and the Biology Lab Coordinator theoretically sorted on

this factor (See Table 16). Participant MEN23MF explained that

his most-like items were regarding “research, and how it consumes

my life.” The Biology Lead Faculty Member expressed that GTAs

“care more about research than when they started, and may have

more fears about research – finding it to be harder than they

thought.”

GTAs represented by Factor 2 also indicated a preference for

research over teaching, as demonstrated by the distinguishing

statements for this factor (See Table 15). Two statements (35, I

like doing teaching over research; 46, I worry that certain

students in my class might know more about Biology than I do),

were ranked the lowest by GTAs who were represented by this

factor. These statements indicate that this group of GTAs

perceives that there is no way their students know more about

Biology than they do. This prioritization of the statements

suggests the GTAs are highly confident in their Biology content

knowledge. Participant DEN32MF suggested that “I like research

because it is challenging. Because of this, I decided to go to

189

grad school. Although I don’t mind teaching, research is my

passion. I came to grad school for research, not to teach.”

GTAs who were represented by Factor 2 were highly

analytical, based on their written responses to the post-sort

questions, in making their choices about statements that were

most-like or most-unlike their views. Participant DNN25MF

detailed how he used a combination of facts, logic, experiential

knowledge, inference, and speculation in his sorting process.

Participant DEN32MF said, “Most of the decisions were provided by

the sorting mechanism itself, otherwise I tried to use my gut

reactions or emotional responses. It was difficult to sort some

of the statements because I had mixed feelings, or there was a

negative tone. I don’t agree or disagree with the

Table 16 - Post-Sort Interview Responses for Factor 2 “The Preferred Researchers”

Code Specific Teaching Experience

Career plan GTA program Preferences

BFMEXP 20+ years Faculty N/A Biology Faculty Member

BLC1EXP 15 years teach N/A Biology Lab Coordinator

DEN30FF 8 semesters Post Doc to Effective 190

Code Specific Teaching Experience

Career plan GTA program Preferences

TA, ballet and dance

Professor Teaching Techniques, Conduct, How to prepare a courseof her own, How to teach a lecture

DEN32MF 4 years Principles, guest taught evolutionary biology

Prefer research or industry

Prepare for teaching, lecture, work with students, diverse but not intensive, strike a balance, would be nice to have a more intense program for those TAs planning to teach

DNN22MU Unanswered Academia or research

student motivation, confidence, foundations

DNN23MF 3 semesters Academia or research

educational foundations, troubleshooting

191

Code Specific Teaching Experience

Career plan GTA program Preferences

DNN25MF 2 semester TA Post Doc to Professor

new ways of teaching, understanding diverse students, puzzle-based learning

DNN27MF 1 semester Business mentorship

MEN23MF 2 years Biotech, Pharmaceutical, or Industry

Preparation, group work

MNN25FI English abroad, nature center

NGO/Nonprofit motivation, peermentoring

Note: The coding for the Q Sort ID includes the program (M = Masters, D = Doctoral, BLC = Biology Lab Coordinator, LFM = Lead Faculty Member), experience (n = new, e = experience, international status (yes = international, no = American), age inyears, sex (M = male, F = female), and teaching experience (f = formal, I = informal, u = unanswered).

statement based on the tone, but rather the content. It was an

interesting process.” The “Preferred Researchers” were highly

analytical in their processes, and detailed more about the

sorting process than GTAs who were represented by the other

factors. They took a very scientific approach to the process, 192

which is interesting because they are very scientifically-minded

students, and approached sorting like doing research.

Factor 2 GTAs did not dislike teaching; they preferred

research. Participant MNN25FI indicated that “some people were

born to teach, and have a natural gift. I don’t have much anxiety

about teaching, because I have experience.” The Lead Biology

Faculty Member indicated that as GTAs gained experience, they

wouldn’t be scared of teaching anymore. Participant DEN30FF

further explained that “I am very clear about my goals and had

thought out my career path prior to grad school.” She indicated

that she wanted to do a post-doc after graduating, and then go on

to become a professor. “I don’t hate teaching; I just prefer to

do research. My advisor expects a lot of me, and I tend to

overwhelm myself with my research.” She also detailed that she

felt like she had changed dramatically as she progressed through

her grad school/TA career.

This group of GTAs acknowledges that they have some natural

teaching abilities (statement 40, I think some people are natural

teachers), and that they can be good teachers (8, I can balance

being a good teacher with being a good student; 21, I feel self-

193

confident when I teach; and 20, I feel pretty confident that I'm

a good teacher), despite their preference for research. “The

Preferred Researchers” further emphasized this point in their

most-unlike statements, as well. They were confident that the

students looked at them positively (25, I have no idea what

students think about me, and that makes uncomfortable; and 17, I

feel like students look at me weird when I tell them I'm a TA),

that they fit in, in grad school (11, I feel like an outsider,

and that people at grad school won't accept me), and that they

were comfortable in their GTA position in grad school (22, I

have a lot of anxiety about teaching, because I don't know what

to expect).

In the distinguishing statements for “The Preferred

Researchers,” statement 6 (I believe I know what it takes to be a

good researcher) and 31 (I know what the department expects from

my research) were indicators that these GTAs, more so than those

who were represented by the other factors, were research

oriented. Many of the GTAs who were represented by this factor

indicated that teaching was easy, or at least not challenging

compared to their research (negatively placed statements 41, I

194

think teaching is very challenging and 35, I like doing teaching

over research).

The post-sort interview questions (See Table 16) were

revealing, in that most of the Factor 2 GTAs planned to pursue a

career in academia. Even though the sorters represented by Factor

2 preferred research, they asked for professional development in

the “Effective Teaching” course that would enhance their teaching

skills. They wanted to be assisted in developing their teaching

skills, working with diverse students, and motivating their

students. Professional development for these GTAs, who are

planning to continue into academia and will be future professors,

may have the biggest impact on their success as there are

potential long term effects – beyond these GTAs’ time as GTAs at

this university – that could influence the learning of future

students at other institutions as well as tenure and promotion of

these potential future academics.

"The Preferred Researcher” are GTAs who came to graduate

school mainly so they could do research, and prefer research over

teaching. This factor also included the Lead Biology Faculty

Member and the Biology Lab Coordinator, theoretically sorting as

195

experienced GTAs. Almost all of the GTAs who were represented by

Factor 2 described their career aspirations as academia or

research. They felt like they were good teachers, and that they

had some natural teaching abilities, but that they just preferred

research. They did not display anxiety about teaching, or lack

confidence in their abilities, and they were highly confident

that they knew their content matter. The majority of GTAs who

were represented by Factor 2 were doctoral students.

Factor 3

Of the 36 Q Sorts, six were represented by Factor 3, or what

the researcher has named "The Anxious GTA". This included The

Biology Lab Coordinator, sorting as a new GTA. These GTAs do not

show a preference for teaching or research, but instead look to

juggle the two, along with being a student. The top eight most-

like my view statements are in Table 17. These GTAs rated

statement 41 (I think teaching is very challenging) and statement

39 (I think research is very challenging) as most-like their

view. They asserted a need to be good at both teaching and

research activities (statement 2, Being a good teacher is as

important as being a good researcher), while expressing that they

196

may not be good at either (statement 25, I have no idea what

students think about me, and that makes me uncomfortable;

statement 41, I think teaching is very challenging; statement 39,

I think research is very challenging). They want to teach like

their favorite professors (statement 45) yet also believe they

will learn a lot from their peers (statement 16, I feel like my

fellow TAs will help me to teach better). This was the only

factor that indicated they believed they would learn from their

peers.

Table 17 - Eight Most-Like My View Statements for Factor 3 “The Anxious GTA” with a † indicating a Distinguishing Statement.

No. Statement Grid

Pos.

40 I think some people are natural teachers 541 I think teaching is very challenging † 545 I want to teach the same way my favorite

professor taught †5

16 I feel like my fellow TAs will help me to teach better †

5

25 I have no idea what students think about me, andthat makes me uncomfortable †

4

44 I want all students to actively participate in my class

4

39 I think research is very challenging 42 Being a good teacher is as important as being a

good researcher4

197

Table 18 - Eight Least-Like My View Statements for Factor 3 “The Anxious GTA” with a † indicating a Distinguishing Statement.

No. Statement

Grid

Pos.

31I know what the department expects from my research † -4

13I feel like I need to constantly monitor my students for cheating † -4

35 I like doing teaching over research † -4

49I've had family problems because of the pressures of graduate school -4

30I know what the Biology department expects frommy teaching † -5

17I feel like students look at me weird when I tell them I'm a TA -5

21 I feel self-confident when I teach † -5

20I feel pretty confident that I'm a good teacher† -5

198

Table 19 - Distinguishing Statements for Factor 3 “The Anxious

GTA.”

Factors

1 2 3

No

. Statement

Rank

Score

Rank

Score

Rank

Score

41 I think teaching is very challenging

-2 -3 5

45 I want to teach the same way my favorite professor taught

2 0 5

16 I feel like my fellow TAs will help me to teach better

1 1 5

25 I have no idea what students think about me, and that makes me uncomfortable

-4 -4 4

7 I came to grad school mainly so I could do research

-2 5 3

26 I have no idea what the level ofunderstanding is with these students

-2 -3 3

12 I feel like I could go into teaching as a profession

5 0 3

48 I'm worried that the students won't be able to understand me

-3 -3 2

22 I have a lot of anxiety about teaching, because I don't know what to expect

-5 -5 2

33 I learned best by listening to professors teach

0 -2 1

34 I like doing research over teaching

-3 5 1

8 I can balance being a good teacher with being a good

4 4 0

199

Factors

1 2 3

No

. Statement

Rank

Score

Rank

Score

Rank

Score

student29 I know what attributes make a

good teacher3 3 0

11 I feel like an outsider, and that people at grad school won'taccept me

-4 -4 -1

5 I am good at creating a respectful classroom environment

4 1 -1

24 I have lost sleep because I'm worried about teaching

-4 -4 -2

36 I think all this teaching gets in the way of my research

-5 2 -2

42 I think that I give good teaching presentations

4 2 -2

9 I dislike teaching, and wish I could spend more time on my research

-5 -1 -3

15 I feel like it will be easy to manage my class

1 1 -3

31 I know what the department expects from my research

1 3 -4

13 I feel like I need to constantlymonitor my students for cheating

-1 -2 -4

35 I like doing teaching over research

3 -5 -4

30 I know what the Biology department expects from my teaching

0 1 -5

21 I feel self-confident when I teach

5 4 -5

20 I feel pretty confident that I'm 5 4 -5

200

Factors

1 2 3

No

. Statement

Rank

Score

Rank

Score

Rank

Score

a good teacher

GTAs who were represented by Factor 3 demonstrated a lack of

confidence (25, I have no idea what students think about me, and

that makes me uncomfortable; and 48, I'm worried that the

students won't be able to understand me), which is not mentioned

by GTAs loading on the other two factors. In the distinguishing

statements for this factor (See), statement 25 (I have no idea

what students think about me, and that makes me uncomfortable),

statement 26 (I have no idea what the level of understanding is

with these students), statement 48 (I'm worried that the students

won't be able to understand me), helped distinguish this factor

from the other two factors. The post-sort interview questions

which detailed the “GTA Program Preferences” are found in Table

20, and further provided differences between this factor and the

others.

201

Two statements made by sorters who were represented by

Factor 3 appeared to be at odds with one another. Statement 12 (I

feel like I could go into teaching as a profession) was ranked as

a +3, and statement 20 was ranked as a -5 (I feel pretty

confident that I'm a good teacher). These two statements are at

ranked on opposite ends of the grid. The GTAs feel that they are

not good teachers, but they feel they could go into teaching as a

profession needs further clarification. Do these GTAs feel they

could go into teaching as a career, because it is easier than

research, or that they will eventually “get the hang of”

teaching? These GTAs are obviously uncomfortable and anxious, and

it would be good to clarify what they mean by these statements.

The bottom four “least like my view” statements (20, I feel

pretty confident that I'm a good teacher; 21, I feel self-

confident when I teach; 17, I feel like students look at me weird

when I tell them I'm a TA; 30, I know what the Biology department

expects from my teaching) for GTAs who were represented by this

factor indicated not that they didn’t like teaching, but that they

were both unsure of their abilities to teach, and didn’t know how

to teach yet (See Table 18). They also seemed to lack a sense of

202

expectation from the department in regards to their teaching

(statement 30, I know what the Biology department expects from my

teaching). They repeated that they were not confident in their

teaching (statement 20, I feel pretty confident that I'm a good

teacher; statement 21, I feel self-confident when I teach).

Five out of the six GTAs who were represented by this factor

were new GTAs. Four of the six were Master’s degree students.

Participant DNN23MI indicated, “I have witnessed natural

teachers, and I am sure I am not one. I always feel more

comfortable after seeing someone else do a similar talk. I know I

suck at teaching, I have a lack of self-esteem, whatever….” He

continued to describe his process, “I did however realize I want

to be a lot like my past professors. Perhaps that’s part of the

pressure. I am very aware of my lack of confidence and

nervousness. I just want to survive.” Another participant,

MNN23FI described how “I feel like I can only excel at one, if I

put a lot of effort into both, each becomes mediocre.”

Factor 3 was characterized by displays of anxiety and lack

of confidence, further evidenced from the post-sort interview

questions (See Table 20). Participant DNN23MI expressed, “I know

203

I suck at teaching…I lack self-esteem.” Participant MNN23FI

stated, “I have no experience in public speaking, I have social

anxiety.” Participant MNN29FI said, “Public speaking can terrify

me, even if I’m good at hiding it, so I never feel confident when

I’m lecturing.” Participant MEN24MF explained, “I don’t feel I’ve

been briefed on what is

Table 20 - Post-Sort Interview Responses for Factor 3 “The Anxious GTA.”

Code Specific Teaching Experience

Career plan GTA program Preferences

BLC1NEW 15 years Teach N/A Biology Lab Coordinator

DNN23MI tutoring elementary

Academia orresearch

surviving teaching, feelsvery uncomfortable, insecure

MEN24MF TA 5 semesters,Nature program

PhD, then research/teach

basic managing and leadership skills, individual styles should be nourished, how to self-motivate, promote scholarship

MNN22FI tutoring Phd, research

motivating students

MNN23FI tutoring, helping friends

industry reduce anxiety, confidence, teaching portfolio, how to teach

204

clearly and speak in public

MNN29FI tutoring, life skills to developmentallydisabled

Phd, research

speaking to groups, explain using multiple methods

Note: The coding for the Q Sort ID includes the program (M = Masters, D = Doctoral, BLC = Biology Lab Coordinator, LFM = Lead Faculty Member), experience (n = new, e = experience, international status (yes = international, no = American), age inyears, sex (M = male, F = female), and teaching experience (f = formal, I = informal, u = unanswered).

expected from my TA and I’m not sure what students expect and

feel about my classes. This is OK. Trial by fire is very

effective. HA! I’m not confident that my managerial practices are

received well AT ALL.” The Biology Lab Coordinator also was

represented by this factor, when theoretically sorting as a new

GTA. She wrote, “I thought about what it means to be a new GTA,

why they came here, and what they want to do – and how their

expectations and frustrations develop.”

“The Anxious GTA” are those GTAs who do not show a

preference for teaching or research, but instead look to juggle

the two, along with being a student. They asserted a need to good

at both teaching and research activities, while expressing that

205

they may not be good at either. These GTAs expressed insecurity

in their abilities, being uncomfortable in their position, and

feeling anxious. They also stated that they were frustrated

because they didn’t know the department’s expectations of them.

GTAs who were represented by this factor were mostly master’s

degree students, and the Biology Lab Coordinator.

Consensus Statements

Q Methodology is a powerful tool for determining consensus

and perspectives of a group (Ramlo, 2011). The consensus

statements can highlight the similarities between factors (See

Table 21). These would be views shared by all the GTAs. This

study uncovered 13 consensus statements, which allow the

commonality of the GTAs’ sorting to be expressed.

Perceptions of GTA status were indicated by statements 14 (I

feel like I'm a good teacher because I am closer in age to my

students) and 17 (I feel like students look at me weird when I

tell them I'm a GTA). This is note-worthy because all GTAs sorted

statement 14 between the -2 and 0 columns. They also sorted

statement 17 between the -3 and -4 columns. The literature often

suggests that GTAs find the opposite is true – being closer in

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age to their students is a problem, rather than a positive

(Austin, 2002). Because many new GTAs lack teaching experience,

have not had adequate training to deal with power issues in the

classroom, and are often close in age to (or younger than) the

students they teach, they may feel that their credibility is

called into question by their students (Golish, 1999). GTA

training research reveals that one of GTAs' fears or

uncertainties is establishing credibility with their students

(Hendrix, 1995; Simonds, Jones, & Bedore, 1994; Worthen, 1992).

Graduate teaching assistants may feel like they need to work

harder to establish their credibility in the classroom because

they lack the initial credibility or status of full-time faculty

(Hendrix, 1995), along with their young age.

Table 21 - Consensus Statements – Statements in Common Amongst Factors

Factor Arrays

1 2 3

No. Statement

14*

I feel like I'm a good teacher because I am closer in age to my students

-2 1 0

17 I feel like students look at -4 -3 -3

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* me weird when I tell them I'ma GTA

19*

I feel pretty comfortable using technology in my class 3 4 4

23*

I have had dreams about my teaching or research -1 0 -2

27 I have to repeat myself over and over to get these students to understand me

-1 0 -2

28 I know the university policies that relate to my research

0 0 -1

33*

I learned best by listening to professors teach 0 1 0

37 I think most of my students learn in a way that's similarto the way I learn

-1 -3 0

38 I think one of the most important things about being a GTA is being ethical

2 2 3

40*

I think some people are natural teachers 4 5 4

47 If I teach well, I will get good student evaluations 0 -2 0

49*

I've had family problems because of the pressures of graduate school

-5 -5 -5

54*

Using social media (like Twitter or Facebook) helps meto feel like I'm not alone

-3 -1 -2

All of the GTAs ranked statement 19 (I feel pretty

comfortable using technology in my class) as most-like their

view. Because Biology is a technology-rich STEM discipline, GTAs 208

have developed their conception of their content knowledge using

technology (Shulman, 1986). GTAs across all the factors also

agreed that statement 38 (I think one of the most important

things about being a GTA is being ethical) was most-like their

view.

GTAs were in agreement across all factors that statements 49

(I've had family problems because of the pressures of graduate

school) and 54 (Using social media (like Twitter or Facebook)

helps me to feel like I'm not alone) were unlike their view.

Graduate school is often highly competitive, and emotionally

exhausting (Jacobs & Dodd, 2003); however, this cohort of GTAs

did not seem to share this sentiment. The balance of

school/personal life does not appear to have affected this group

of GTAs the way it is described in the literature (Ward & Wolf-

Wendel, 2004; Ward, 1998).

There were many statements that the three factors agreed

were neutral to their viewpoint (statement 23, I have had dreams

about my teaching or research; statement 27, I have to repeat

myself over and over to get these students to understand me;

statement 28, I know the university policies that relate to my

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research; statement 33, I learned best by listening to professors

teach; and statement 47, If I teach well, I will get good student

evaluations). These statements did not hold particular

significance for the sorters, but were represented in the

literature. The importance of the needs assessment stage of

program evaluation becomes apparent, as this particular cohort of

GTAs share differing viewpoints from the GTA literature.

Consensus statements in a Q Methodology study may reveal

similarly shared perspectives that are important to working with

groups. These consensus views may facilitate dialogue and

collaboration among the groups’ membership (Ramlo, 2005). This

study uncovered 13 consensus statements, which allow the

commonality of the GTAs’ sorting to be expressed. GTAs across all

the factors ranked items about being similar in age to their

students, using technology, and the pressures of graduate school,

similarly. Many of the consensus items from this study indicated

neutral viewpoints.

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Results of Testing the Research Hypotheses

General Research Hypothesis 1

The first research hypothesis stated that, “A variety of

viewpoints about graduate school will exist among biology GTAs.”

Three GTA factors, or viewpoints about graduate school, emerged

as a result of analyzing the Q Sorts. These viewpoints were, “The

Emerging Teacher,” “The Preferred Researcher,” and “The Anxious

GTA.” Each represents a distinctive view about being a GTA. “The

Emerging Teachers” are GTAs who feel confident that they are good

teachers, have a preference for teaching, and feel that they

could go into teaching as a profession. “The Preferred

Researchers” are GTAs are those who came to graduate school

mainly so they could do research, and prefer research over

teaching.” The Anxious GTAs” are GTAs who do not show a

preference for teaching or research, but instead expressed

anxiety about both; they lack confidence in teaching and

research, unlike the other two views. The variety of viewpoints

was further elucidated by post-sort interview questions.

Therefore, we reject the null research hypothesis.

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General Research Hypothesis 2

The second research hypothesis stated that “Experienced

biology graduate GTAs will have different views of graduate

school than new biology graduate GTAs.” Of the three factors

revealed through analysis of the Q Sorts, Factor 1 had nine

experienced GTAs and nine new GTAs, Factor 2 had five experienced

GTAs and five new GTAs, and Factor 3 had one

Table 22 – Number of Q-Sorts Included in Each Factor

Factor1 2 3

Q-SortsNew GTA 9 5 5Experienced GTA 9 5 1Biology Lab Coordinator 1 0 1 1Biology Lab Coordinator 2 1 0 0Biology Lead Faculty Member

0 1 0

experienced GTAs and five new GTAs (See Table 22). Each of the

three viewpoints contained both new and experienced GTAs.

Therefore, being an experienced GTA or a new GTA was not

necessarily a predictor of holding a certain viewpoint. The null

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research hypothesis (Experienced biology graduate TAs will have

the same views of graduate school as new biology graduate GTAs)

cannot be rejected, because experienced and new GTAs do populate

similar factors. Some new GTAs share similar views with

experienced GTAs, but some do not. Thus, status (new or

experienced) does not determine GTA views. Therefore, the

researcher fails to reject the null hypothesis.

General Research Hypothesis 3

Specific research hypothesis 3A.

The first specific research hypothesis states that “The Q

Sorts will reveal differences between the viewpoints of the GTAs

and the Biology Lab Coordinator.” There were two Biology Lab

Coordinators who sorted, the first Biology Lab Coordinator

theoretically sorting once as a new GTA, and the second time as

an experienced GTA. These sorts revealed that first The Biology

Lab Coordinator theoretically sorted similar to a new GTA loading

on Factor 3 (The Anxious GTA), and similar to an experienced GTA

loading on Factor 2 (The Preferred Researcher). The second

Biology Lab Coordinator theoretically sorted as an experienced

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GTA, and were represented by Factor 1. The null hypothesis, “The

Q Sorts will reveal no differences between the viewpoints of the

GTAs and the Biology Lab Coordinator,” is rejected. The Biology

Lab Coordinator was represented by Factor 3 when she

theoretically sorted as a new GTA, and was represented by Factor

2 when she theoretically sorted as an experienced GTA. The second

Biology Lab Coordinator was represented by Factor 1 when she

sorted as an experienced GTA. The Biology Lab Coordinators’ views

aligned with Factors that were populated with both new and

experienced GTAs, but did not end up on views that were not

populated by any GTAs. Therefore, we fail to reject the null

hypothesis.

Specific research hypothesis 3B.

The second part of the third research hypothesis, “The Q

Sorts will reveal differences between the viewpoints of the GTAs

and the Lead Biology Faculty Member” revealed that the Lead

Biology Faculty Member theoretically sorted similar to a new GTA

did not load significantly on any factor (his loading was mixed,

and not significant on any one factor), and the Lead Biology

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Faculty Member theoretically sorted as an experienced GTA,

loading on Factor 2 (The Preferred Researcher). The null

hypothesis, “The Q Sorts will not reveal differences between the

viewpoints of the GTAs and the Lead Biology Faculty Member” is

failed to be rejected. The Lead Biology Faculty Member

demonstrated he believed the new and experienced GTAs would have

different viewpoints, as evidenced by his sorts. His sorts were

also different than both the new and experienced GTAs who were

represented by Factors 1 and 3.

General Research Hypothesis 4

The fourth hypothesis is that “Consensus statements

concerning GTA viewpoints will emerge during factor analysis.”

The analyses revealed 13 consensus statements. The null

hypothesis states that “Consensus statements concerning GTA

viewpoints will not emerge during factor analysis” is rejected.

When a statement is not distinguishing between any of the factors

it becomes a consensus statement (van Exel, 2005). Consensus

statements are those statements that are common between any pair

of factors. Therefore, the researcher rejected the null

hypothesis.

215

Summary

The participants in this study included 17 new GTAs

participating in an “Effective Teaching” course in the Fall of

2012, the two supervisors of the “Effective Teaching” course, 14

experienced GTAs, and an additional Biology Lab Coordinator.

There were a total of 36 sorts collected. The Q Sort process

revealed three factors or views on graduate school. The factors

were named “The Emerging Teacher,” “The Preferred Researcher,”

and “The Anxious GTA.” The names for the factors were determined

using the eight most-like (+5 and +4) and least-like my view (-5

and -4) statements, distinguishing statements, consensus

statements, and answers to post-sort questionnaire.

Factor 1 GTAs, or “The Emerging Teacher,” are those who feel

confident that they are good teachers, and that they could go

into teaching as a profession. Factor 2 GTAs, or “The Preferred

Researcher,” are those who came to graduate school mainly so they

could do research, and prefer research over teaching. Factor 3

GTAs, or “The Anxious GTA,” do not show a preference for teaching

or research, but instead look to juggle the two, along with being

216

a student. This study uncovered 13 consensus statements, which

allow the commonality of the GTAs’ sorting to be expressed.

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CHAPTER V

SUMMARY, CONCLUSIONS, AND IMPLICATIONS

The purpose of this chapter is threefold; first, a summary

of the study, second, conclusions, and third, implications and

further research. The first major section, the summary of the

study, includes a brief restatement of the problem, a brief

review of the procedures employed in conducting the research, and

the specific research hypotheses tested. The second section,

conclusions, is drawn from Chapter IV analyses. These include

highlights of the major findings. The final section discusses the

implications of the findings. The emphasis is on interpretation

of the significance of the research findings and what they imply.

The suggested further research section includes possible ways of

extending the current study, expanding the study to include

different participants, or additional variables that could be

added to the current study to glean additional insight.

Summary of the Study

Graduate Teaching Assistants (GTAs) are frequently utilized

as instructors in undergraduate classrooms and science

laboratories (Kendall & Schussler, 2012; Luft et al., 2004;

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Nyquist & et al., 1991). GTAs provide universities a cost-

effective form of instructor, while the GTAs themselves are being

simultaneously socialized into the roles of teacher, researcher,

and scholar (Carroll, 1980; Garland, 1983). GTAs represent a

diverse population of masters and doctoral-level students with

varying amounts of pedagogical preparation, research abilities,

and motivation to complete their graduate study (Boyle & Boice,

1998). GTAs who are not adequately prepared to engage in teaching

activities may display a wide range of behaviors, from an

overblown confidence in their abilities (Golde & Dore, 2001), to

frustration and insecurity (Eison & Vanderford, 1993).

Instructional training programs for professionally

developing GTAs vary from institution to institution, and even

between departments at the same institution (Nyquist & Woodford,

2000; Parrett, 1987; Stockdale & Wochok, 1974). Calls for

instructional training programs for teaching assistants in the

sciences (Carroll, 1980; Luft et al., 2004), and more

specifically in biology (Rushin et al., 1997; Tanner & Allen,

2006) have created a continual demand for pedagogical training,

in addition to content area mastery. Locally developed GTA

219

instructional training programs are much more common in graduate

schools or disciplinary departments than large-scale, externally-

funded, national programs. These local programs are led by

graduate school or disciplinary faculty, or GTA supervisors, and

vary in programmatic elements and effectiveness (Carroll, 1980;

Parrett, 1987; Thornburg et al., 2000).

The lack of uniformity among instructional training programs

may be another reason for varying preparedness among GTAs

(Mountain & Pleck, 2000). Programs might range from half a day

before the semester begins, to a week-long campus-wide

orientation, to a department-specific semester long course in

teaching methods, to a university-wide graduate school

certification (Golde & Dore, 2001). There has been little

agreement on “the best way to train GTAs,” although the Council

of Graduate Schools with the “Preparing Future Faculty”

initiative and the Association of American Colleges and

Universities have provided some guidelines that can be referenced

(DeNeef, 2002; Gaff, 2002a). There has also been no consensus on

who should be doing the training, and for what purpose (Shannon

et al., 1998). The amount and type of professional development

220

made available to GTAs remains highly variable among higher

education institutions.

Whether the GTA instructional training program is

implemented by the state, the university, or the individual

disciplinary department, program evaluation is complex and

necessary (Garet et al., 2001; Guskey, 1994). Such evaluation

should be an intrinsic part of any program or project because it

is used both to measure the effectiveness of that program or

project as well as to investigate ways to increase that

effectiveness (Newman & Ramlo, 2011). The literature surrounding

GTA training programs describes GTAs as having varying

programmatic needs based on numerous factors – prior formal or

informal teaching experience, familiarity with content, exposure

to prior instructional training, demographic variables, career

aspirations, international status, etc. GTA programs often group

cohorts of GTAs together for training (Muzaka, 2009) regardless

of experience, assignment, career focus, or degree plan. However

GTAs are grouped, the first steps in effectively evaluating any

professional development program is assessing participants’

needs. This study used Q Methodology to assess the professional

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development needs of GTAs in one Department of Biology at a

large, public, urban university in the Midwest.

Q Methodology provides a foundation for the systematic study

of subjectivity, a person’s viewpoint, opinion, beliefs,

attitude, etc. (Brown, 1993; Van Exel & de Graaf, 2005). By

Q Sorting, people assign their subjective meaning to the

statements and reveal their subjective viewpoints (Smith, 2001)

or personal profiles (Brouwer, 1999). Within this study,

Q Methodology was used to provide a needs assessment for an

instructional program such that the study allowed the researcher

to identify and interpret the various viewpoints that GTAs hold

in regard to graduate school.

The factors that emerged as a result of analyzing the Q

Sorts were named using information provided by GTAs. In post-sort

interview questions, GTAs answered questions about their graduate

school program and degree track. They described both formal and

informal teaching experiences. Their Q Sorts provided information

that distinguished the most-like and most-unlike my view

statements. GTAs also described their decision making process

about the sorting procedure, and made clarifications about why

222

they placed the statements into the grid. Three distinct GTA

viewpoints about graduate school emerged from the analysis of the

data: “The Emerging Teacher,” “The Preferred Researcher,” and

“The Anxious GTA.” These three factors provided the researcher

with GTA typologies that may be more useful in designing

meaningful GTA professional development for this group of GTAs

and differentiating the training program than simple demographics

or answers to survey questions. Distinguishing and consensus

statements provide the supervisors of the “Effective Teaching”

course areas of agreement and disagreement between GTAs, which

allows for reinforcement, enhancement, or supplementation of

skills possessed by these GTAs.

Statement of the Problem

The purpose of this study is to demonstrate that Q

Methodology can be used as a needs assessment tool for a Biology

graduate teaching assistant (GTA) instructional training program.

Despite the wealth of literature concerning elements of

instructional training programs for GTAs at the national,

institutional, or departmental level, there is little consistency

among training programs. Literature about faculty perceptions of

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GTAs suggests that faculty perceive GTAs as preferring research

over teaching (Austin, 2002; Boyle & Boice, 1998; Kurdziel,

Turner, Luft, & Roehrig, 2003; Levinson-Rose & Menges, 1981;

Trice, 2003), which was supported by GTAs loading on Factor 2

(“The Preferred Researcher”), but not the other factors.

Literature about GTA professional development also suggests that

GTAs are timid and self-conscious when teaching (Gibbs & Coffey,

2004; Park, 2002, 2004; Prieto & Altmaier, 1994; Salinas, Kozuh,

& Seraphine, 1999), which was supported by GTAs loading on Factor

3 (“The Anxious GTA”), but not the other factors.

Because typically a program does not have the same level of

effectiveness for the entire population it serves (McNeil et al.,

2005), a needs assessment to identify important and significant

issues that can be addressed during professional development with

the unique cohort of GTAs should be, but often is not, completed.

Q Methodology can be useful as a needs assessment tool, providing

predictor typologies that are more useful than simple variables

and demographic information for the classification of people,

especially within program evaluation (Newman & Ramlo, 2011).

224

If a program is to be useful to its stakeholders, in this

case the Biology GTAs and The Department of Biology, it is

important to keep GTA views and program preferences in mind. To

assist graduate students to become as proficient in both their

teaching and their research, they must be given opportunities to

develop their teaching skills, abilities, and knowledge with the

same guidance and practice that is afforded to the development of

a quality researcher (Golde & Dore, 2001).Because stakeholder

needs vary at different stages in the program (Chen, 2005),

identifying GTA needs at the various stages in their graduate

school program allows for program supervisors to identify and

modify program elements relative to GTA needs. The resulting

factors/views that emerged in this study have implications for

improving this training program by improving GTA instruction to

undergraduates and improving GTA success in graduate school and

their future careers.

Statement of the Procedures

The researcher used Q Methodology to investigate new and

experienced biology GTAs’ views of their GTA experiences.

Multiple survey instruments were used to gather initial

225

information about the participants and their views on their

biology graduate school program, which were used to populate the

concourse. The concourse, discussed extensively in Chapter III,

for this study included a collection of statements made by GTAs

in a Self-Reflection Questionnaire, a “Perceptions of Graduate

School Survey,” a graduate student discussion forum (“Grad School

Life,” 2012), and everyday conversations and emails made between

Biology GTAs and their supervisors.

The Q Sample (see Appendix 2) was derived from the concourse

by selecting nine representative statements from each of six

categories of interest to the researcher – teaching, learning,

students, research, challenges in graduate school, and GTA

persistence in their program – for a total of 54 statements in

the Q Sample. This represented a Fisherian design (Brown & Ungs,

1970).The statements were sorted by the participants into a three

piles, based upon the conditions of instruction, “Read each

statement, and then based on your views of graduate school, place

the statements into three equal piles; most unlike your view,

neutral, and most like your view.” Then, the sorter placed each

statement onto the sorting grid. Rather than simply indicating

226

agreement or disagreement with statements, as in Likert-style

surveys, participants in this study completed the “GTA

Perceptions of Graduate School Q Sort,” where they were asked to

sort statements in relation to other statements in the Q Sample.

After the participants completed their Q Sorts, the researcher

factor analyzed the sorts. The descriptive tables that result

from the factor analysis, along with post-sort interview

questions, led to an understanding of the various viewpoints held

by GTAs and their supervisors, in a GTA instructional training

program.

The Q Methodology instrument was pilot tested during the

“Effective Teaching” course with new GTAs and their supervisors,

in The Department of Biology during the Fall semester of 2012.

The pilot study demonstrated the viability of the research design

and instrument and led to three GTA viewpoints (“The Confident

Teachers,” “The Preferred Researchers,” and “GTA to Professor”)

that provided greater insight about new GTAs, the “Effective

Teaching” course, and programmatic needs. The research study

expanded the pilot with experienced GTAs and another Biology Lab

Coordinator, sorting under the same conditions of instruction.

227

After the 36 Q Sorts were collected, the sorts were entered

into PQMethod, where they could be factor analyzed and used to

create detailed tables describing the different views and the

consensus. The factor analysis uncovered three factors (“The

Emerging Teacher,” “The Preferred Researcher,” and “The Anxious

GTA”), a list of distinguishing statements, and a list of

consensus statements. These factors represent three distinct

viewpoints that exist amongst GTAs and their supervisors about

being a GTA in The Department of Biology, at this institution, at

the time of the study. The results of this can be used to

consider potential changes or updates to the existing training

program in The Department of Biology.

The Research Hypotheses

General Research Hypothesis 1

The first research hypothesis stated that, “A variety of

viewpoints about graduate school will exist among biology GTAs.”

Three GTA factors, or viewpoints about graduate school, emerged

as a result of analyzing the Q Sorts. These viewpoints were, “The

Emerging Teacher,” “The Preferred Researcher,” and “The Anxious

GTA.” Therefore, we reject the null research hypothesis.

228

General Research Hypothesis 2

The second research hypothesis stated that “Experienced

biology graduate GTAs will have different views of graduate

school than new biology graduate GTAs.” Of the three factors

revealed through analysis of the Q Sorts, Factor 1 had nine

experienced GTAs and nine new GTAs, Factor 2 had five experienced

GTAs and five new GTAs, and Factor 3 had one

Table 23 – Breakdown of Number of Q-Sorts Included in Each Factor

Factor1 2 3

Q-SortsNew GTA 9 5 5Experienced GTA 9 5 1Biology Lab Coordinator 1 0 1 1Biology Lab Coordinator 2 1 0 0Biology Lead Faculty Member

0 1 0

experienced GTAs and five new GTAs (See Table 23). Each of the

three viewpoints contained both new and experienced GTAs.

Therefore, being an experienced GTA or a new GTA was not

necessarily a predictor of holding a certain viewpoint. The null

229

research hypothesis (Experienced biology graduate TAs will have

the same views of graduate school as new biology graduate GTAs)

cannot be rejected, because experienced and new GTAs do populate

similar factors. Some new GTAs share similar views with

experienced GTAs, but some do not. Thus, status (new or

experienced) does not determine GTA views. Therefore, the

researcher fails to reject the null hypothesis.

General Research Hypothesis 3

Specific research hypothesis 3A.

The first part of the third research hypothesis states that

“The Q Sorts will reveal differences between the viewpoints of

the GTAs and the Biology Lab Coordinator.” The null hypothesis,

“The Q Sorts will reveal no differences between the viewpoints of

the GTAs and the Biology Lab Coordinator,” is rejected. The

Biology Lab Coordinators’ views aligned with Factors that were

populated with both new and experienced GTAs, but did not end up

on views that were not populated by any GTAs. Therefore, the

researcher rejected the null hypothesis.

230

Specific research hypothesis 3B.

The second part of the third research hypothesis states that

“The Q Sorts will reveal differences between the viewpoints of

the GTAs and the Lead Biology Faculty Member.” The null

hypothesis, “The Q Sorts will not reveal differences between the

viewpoints of the GTAs and the Lead Biology Faculty Member” is

rejected. The Lead Biology Faculty Member demonstrated he

believed the new and experienced GTAs would have different

viewpoints, as evidenced by his sorts. His sorts were also

different than both the new and experienced GTAs who were

represented by Factors 1 and 3. Therefore, the researcher

rejected the null hypothesis.

General Research Hypothesis 4

The fourth hypothesis is that “Consensus statements

concerning GTA viewpoints will emerge during factor analysis.”

The analyses revealed 13 consensus statements. The null

hypothesis states “Consensus statements concerning GTA viewpoints

will not emerge during factor analysis.” Therefore, the

researcher rejected the null hypothesis.

231

Conclusions

This part of the chapter discusses the conclusions drawn

from the results of this study. First, statements related to the

general research questions, followed by the specific research

questions, and concluded with a general discussion of the

research questions.

General Research Questions

What are the various viewpoints that exist among Biology GTAs about their graduate school experiences?

In this research study, three viewpoints, or factors,

emerged among Biology GTAs about their graduate school

experience. The viewpoints were named by the researcher; Factor

1, or “The Emerging Teacher,” Factor 2, or “The Preferred

Researcher,” and Factor 3, or “The Anxious GTA.”

Factor 1, or “The Emerging Teachers” view, represents the

GTAs who feel confident that they are good teachers, that their

time as a GTA was preparing them to be a good professor, and that

they could go into teaching as a profession. The majority of GTAs

who were represented by this factor specifically listed professor

or teaching as their desired career path. They indicated a

232

preference for teaching over research. They did not indicate

anxiety about teaching, that they were experiencing family

problems because of grad school, or that they felt that they were

an outsider in grad school. Of the 36 Q Sorts, 18 were

represented by Factor 1 including the second Biology Lab

Coordinator.

Factor 2, or “The Preferred Researchers” view, represents

the GTAs who came to graduate school mainly so they could do

research, and prefer research over teaching. Almost all of the

GTAs who were represented by Factor 2 described their career

aspirations as academia or research. They felt like they were

good teachers, and that they had some natural teaching abilities,

but that they just preferred research. They did not display

anxiety about teaching, or lack confidence in their abilities,

and they were highly confident that they knew their content

matter. The majority of GTAs who were represented by Factor 2

were doctoral students, and this factor also included the Lead

Biology Faculty Member and the first Biology Lab Coordinator

theoretically sorting as a new GTA. Of the 36 Q Sorts, 10 were

represented by Factor 2.

233

Factor 3, or “The Anxious GTA” view, represents the GTAs who

do not show a preference for teaching or research, but instead

look to juggle the two, along with being a student. They asserted

a need to be good at both teaching and research activities, while

expressing that they may not be good at either. These GTAs

expressed insecurity in their abilities, being uncomfortable in

their position, and feeling anxious. They also stated that they

were frustrated because they didn’t know the department’s

expectations of them. GTAs who were represented by this factor

were mostly master’s degree students, and the first Biology Lab

Coordinator theoretically sorting as a new GTA. Of the 36 Q

Sorts, six were represented by Factor 3.

What are the various viewpoints of the supervisors of graduate GTAs in The Department

of Biology relative to those of the GTAs?

One supervisor of GTAs, the first Biology Lab Coordinator,

theoretically sorted once as a new GTA, and once as an

experienced GTA. For the new GTAs, she was represented by Factor

3 (“The Anxious GTA”), and for the experienced GTAs, she was

represented by Factor 2 (“The Preferred Researcher”). The two 234

different factor loadings support the Biology Lab Coordinator’s

perception that new and experienced GTAs would have different

viewpoints. In theoretically sorting as a new GTA, one of her

most-like my view statements was statement 22 (I have a lot of

anxiety about teaching, because I don't know what to expect) in

the +5 position. This was one of the distinguishing statements

for Factor 3, and correlates closely to GTAs who were represented

by Factor 3 (“The Anxious GTAs”). The literature describes how

GTAs may perceive teaching as a highly demanding career having a

heavy workload, high emotional demand (Hendrix, 1995), anxiety-

provoking, and generally requiring hard work (Deiro, 1996;

Rhodes, 1997). At the same time, GTAs may also perceive teaching

as relatively low in social status, paying a low salary, and

reported experiences of quite strong social dissuasion from a

teaching career (Rhodes, 1997; Watt & Richardson, 2008). The

Biology Lab Coordinator’s theoretical sort as a new GTA supported

these views.

When theoretically sorting as an experienced GTAs, The

Biology Lab Coordinator was represented by Factor 2 (“The

Preferred Researcher”). Her sort suggests that she believes that 235

experienced GTAs will have more confidence in themselves, and

their teaching, after gaining experience, which was supported by

the GTAs sorting statements 20 (I feel pretty confident that I'm

a good teacher) and 21 (I feel self-confident when I teach) in

the +4 column as “most like” their view. These GTAs also sorted

statements 7 (I came to grad school mainly so I could do

research) and 34 (I like doing research over teaching) in the +5

column, as “most like” their views. The first Biology Lab

Coordinator sorted into the least-like my view columns statement

25 (I have no idea what students think about me, and that makes

me uncomfortable), statement 22 (I have a lot of anxiety about

teaching, because I don't know what to expect), and statement 46

(I worry that certain students in my class might know more about

Biology than I do), which corresponds to the rankings by Factor 2

GTAs. The Biology Lab Coordinator’s view may have been

represented by Factor 2, or “The Preferred Researcher,” but she

has not been trained as a Biology researcher. She may or may not

possess the skills to prepare the GTAs who are represented by

this factor, which were 10 of the 36 sorts. This is one of the

reasons that having both the Lead Biology Faculty Member and The

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Biology Lab Coordinator, with their differing backgrounds, lead

the “Effective Teaching” course is of benefit to the GTAs.

The second Biology Lab Coordinator theoretically sorted as

an experienced GTA, and was represented by Factor 1 (“The

Emerging Teachers”). Three of her “most like” my view statements,

statement 12 (I feel like I could go into teaching as a

profession), statement 20 (I feel pretty confident that I'm a

good teacher), and statement 21 (I feel self-confident when I

teach), were also in the top eight statements of the GTAs loading

on Factor 1. She noted in her post-sort interview questions, “I

feel that teaching is as important as doing research and that

professionals at the university should invest in the two

equally.” She also stated that “the statements that were most-

like her view were the easiest to identify.” This Biology Lab

Coordinator, who teaches a majors Biology lab, and wants to

continue teaching, loaded highly on Factor 1.

The Biology Lead Faculty Member theoretically sorted once as

a new GTA, and once as an experienced GTA. For the new GTAs, the

Biology Lead Faculty Member displayed a mixture of all three

factors, not loading significantly on any one factor. He loaded -237

0.4248 for Factor 1, 0.2074 for Factor 2, and 0.4266 for Factor

3. The negative loading for Factor 1 indicates that he

represented an opposing viewpoint to Factor 1, or “The Emerging

Teachers.” He ranked statement 9 (I dislike teaching, and wish I

could spend more time on my research) as a +4, or most-like his

view, whereas GTAs loading on Factor 1 ranked this statement as

their -5, or least-like their view. He also ranked statement 7 (I

came to grad school mainly so I could do research) in the +5

column, where the Factor 1 GTAs ranked it in their -2, least like

their view column. He rated statement 34 (I like doing research

over teaching) in his +4 column, while the GTAs ranked it in

their -3 column. He ranked statement 36 (I think all this

teaching gets in the way of my research) in the +5 column, while

the Factor 1 GTAs ranked it in the -5 column. He ranked statement

20 (I feel pretty confident that I'm a good teacher) in the -5

column, while the Factor 1 GTAs ranked it in their +5 column. He

ranked statement 12 (I feel like I could go into teaching as a

profession) in the -4 column, while the Factor 1 GTAs ranked it

in the +5 column. The Biology Lead Faculty Member demonstrates

that his viewpoint is the opposite of the GTAs who were

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represented by Factor 1, which were 18 out of the 36 Q Sorts.

This reinforces the need for a needs assessment in the

instructional training program, because the supervisor of the

program has an opposite view of the program than half the

participants. Many of the national training programs, such as The

Preparing Future Faculty program, are designed entirely by

faculty, using faculty perceptions of what GTAs should know

(Anderson et al., 1997). While their institutions may articulate

messages about the importance of the teaching mission, their

advisors, particularly in STEM fields, may urge them to avoid

spending too much time on anything besides research-related

activities (Austin et al., 2009).

For the experienced GTAs, the Biology Lead Faculty Member

was represented by Factor 2 (“The Preferred Researcher”) He

ranked statement 7 (I came to grad school mainly so I could do

research) in the +5 column, as did the GTAs who were represented

by Factor 2. He also ranked statement 19 (I feel pretty

comfortable using technology in my class) in the +5 column, as

well as statement 32 (I learned best by actively doing labs)

which correlated with the Factor 2 GTAs. He ranked statement 25

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(I have no idea what students think about me, and that makes me

uncomfortable), statement 11 (I feel like an outsider, and that

people at grad school won't accept me), statement 22 (I have a

lot of anxiety about teaching, because I don't know what to

expect), and statement 17 (I feel like students look at me weird

when I tell them I'm a TA) as least-like his view, which

correlated with Factor 2 GTAs. The Biology Lead Faculty Member

theoretically sorted as a “Preferred Researcher,” which

correlates to his chosen profession. He may be best suited to

understanding the viewpoints of GTAs who were represented by

Factor 2.

What consensus exists among the GTAs in The Department of Biology about their

graduate school experiences?

Q Methodology is a powerful tool for determining consensus

and perspectives of a group (Ramlo, 2011). A statement that is

not distinguishing between any of the identified factors is

called a consensus statement (Van Exel & de Graaf, 2005). The

consensus statements can highlight the similarities between

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factors (See Table 21). It may be just as enlightening to

discover the statements that people have agreement on as where

their views diverge. Participants may agree positively,

negatively or be neutral about the issue (Coogan & Herrington,

2011). These would be statements that ranked similarly between

all the typologies. This study uncovered 13 consensus statements,

which allow the commonality of the GTAs’ sorting to be expressed.

There were three statements that the GTAs felt strongly were

most-like their viewpoints. These include statement 19 (I feel

pretty comfortable using technology in my class), statement 38 (I

think one of the most important things about being a GTA is being

ethical), and statement 40 (I think some people are natural

teachers). These statements indicate areas where additional

training may not be necessary, like using technology in the

classroom, or where GTAs already feel strongly, like conducting

oneself in an ethical manner. Thinking that some people are

natural teachers may require further exploration, as this

statement could be interpreted several ways. GTAs may feel like

some people are natural teachers, and they are a natural teacher,

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or they may feel that some people are natural teachers, so they

will never be a natural at teaching.

There were four statements that the GTAs felt strongly were

most-unlike their viewpoints. These include statement 17 (I feel

like students look at me weird when I tell them I'm a GTA),

statement 49 (I've had family problems because of the pressures

of graduate school), statement 27 (I have to repeat myself over

and over to get these students to understand me), and statement

54 (Using social media (like Twitter or Facebook) helps me to

feel like I'm not alone). These indicate that GTAs are

comfortable with their “label” as teaching assistants, and that

they are not feeling some of the pressures related to their

families or feelings of loneliness that are often indicated in

the literature. These indicate areas where GTAs may not need

added professional development.

Finally, there were six statements that GTAs placed in the

neutral categories. These include statement 14, (I feel like I'm

a good teacher because I am closer in age to my students),

statement 23 (I have had dreams about my teaching or research),

statement 28 (I know the university policies that relate to my

242

research), statement 33 (I learned best by listening to

professors teach), statement 37 (I think most of my students

learn in a way that's similar to the way I learn), and statement

47 (If I teach well, I will get good student evaluations).

Neutral columns should be evaluated as items the GTAs do not have

strong view are like or unlike their views, in relation to other

statements in the Q Sample. All the study factors have ranked the

items in pretty much the same way, Consensus statements might be

used to highlight a possible need for improvement in a program,

or further training in a particular area (Watts & Stenner, 2005).

In this study, the consensus statements did not provide as much

support for scaffolding the instructional training program as the

top-eight and bottom eight statements, as well as the

distinguishing statements.

How do the views differ between new GTAs versus experienced GTAs?

There were GTAs from both the new and experienced groups who

were represented by each factor. There were nine new GTAs and

nine experienced GTAs who were represented by Factor 1 (“The

243

Emerging Teachers”). There were five new GTAS and five

experienced GTAs who were represented by Factor 2 (“The Preferred

Researcher”). There were five new GTAs and one experienced GTA

who was represented by Factor 3 (“The Anxious GTA”). For the

first two factors, there was an even amount of new and

experienced GTAs populating each factor. For the last factor,

Factor 3, there was a five-to-one ratio of new to experienced

GTAs who were represented by the factor. This distribution of new

and experienced GTAs across all three factors indicates that none

of these distinct views is the result of the “status” of a GTA as

new or experienced. Instead, these views appear to have other

origins and are not directly associated with being a new or

experienced GTA.

Just as students in a classroom interact in different ways

with the curriculum, bringing prior experiences, ways of

thinking, and motivation to the class, GTAs have different

experiences with graduate school. Rather than focusing on the

narrative of specific individuals (i.e. Determining whether an

individual is a new or experienced GTA, or identifying whether an

individual has formal or informal teaching experience), Q

244

methodology typically focuses on the range of viewpoints that are

favored (or which are otherwise ‘shared’) by specific groups of

participants (Watts & Stenner, 2005). In other words, the typical

Q methodological study very deliberately pursues constructions

and representations of a social kind (Moscovici, 1988). “New” or

“Experienced” are labels that are placed on individuals.

Typologies demonstrated shared views between groups. These

results are discussed further within the response to the next

research question.

Do the varying views and consensus of GTAs about their graduate school experiences

provide sufficient information for a needs assessment that informs the existing training

program?

One of the first steps in effective program evaluation is

assessing the needs of the particular set of participants in that

program (Chen, 2005; McNeil et al., 2005). A needs assessment is

a “systematic set of procedures for the purpose of setting

priorities and making decisions about a program or organizational

improvement and allocation of resources. The priorities are based

245

on identified needs (Witkin, 1995).” The literature revealed that

GTA needs in a program are often collected using modified teacher

inventories (Angelo & Cross, 1993; Gibson & Dembo, 1984; Kohn et

al., 1990; Prieto & Altmaier, 1994; Renzulli & Smith, 1978),

Likert-style surveys (Cho et al., 2010; Gorsuch, 2003), using

simple demographic variables – or are not assessed at all

(Shannon et al., 1998; Worthen, 1992).The data analysis of the

“Perceptions of Graduate School Q Sort” provided three distinct

viewpoints about graduate school. Because of the rich qualitative

data provided by Q Methodology, there is sufficient information

about the cohort of GTAs who participated in the study to use the

“Perceptions of Graduate School Q Sort” as a needs assessment to

inform the existing training program.

Q Methodology offers a number of potential advantages for

assessing needs of GTAs throughout their graduate school careers

(Peritore, 1989; Prasad, 2001), which were demonstrated by this

study. Q Methodology does not demand the large number of

participants that a Likert-style survey requires (Cummins &

Gullone, 2000). This study involved 36 Q Sorts, in one

department, at one university. Because the literature about GTAs

246

frequently refers to GTAs in different disciplines or different

types of schools, the needs of GTAs in other disciplines are not

necessarily the needs of this specific group of Biology GTAs. Q

Methodology allows the researcher to determine the various

perspectives and consensus within the group (Ramlo, 2008). This

study uncovered three viewpoints within the group of study.

The only specific needs assessment for GTAs in the

literature was provided by Sohoni et. al. (2013) and was for

engineering GTAs. GTAs, faculty, and students rated the

importance of each of 24 GTA roles and responsibilities on a 5-

point Likert scale, and the perceived competence of GTAs on these

24 items. This 5-point Likert scale was used with 1 representing

“Not at all important‟ and 5 representing “Critically important‟

on the roles and responsibilities questionnaire. A similar scale

was used for competence, with 1 representing “Lack of competence‟

and 5 representing “Very competent.‟ The problem with this type

of survey is that participants could mark every item “Critically

Important.” This type of instrument may not provide useful or

adequate understandings of the various viewpoints that exist

among GTAs about their needs in an instructional training

247

program. Prasad (2001) argues that use of the forced choice

method (forced matrix) in Q Methodology means that the

respondents have to consider their attitudes more carefully,

which can bring out true feelings in response. This study led to

three factors that provided the researcher with GTA typologies

that may be more useful in designing meaningful GTA professional

development and training programs than simple demographics or

answers to survey questions. Classification of GTAs based on

typologies, or predictor profiles, may be more useful for program

evaluation, because typically a program does not have the same

level of effectiveness for the entire population it serves

(McNeil et al., 2005).

Program evaluation ought to be an intrinsic part of any

program or project because it is used to both measure the

effectiveness of that program or project, as well as investigate

ways to increase that effectiveness (Newman & Ramlo, 2011). This

study uncovered GTA programmatic needs that were similar to, and

differed from, those described in the literature. Carroll (1980,

p. 179) notes in his review of the research surrounding GTA

training programs, that “programs should be structured to 248

encourage the participation of experienced, senior GTAs who can

share their insights and experiences with the novice GTAs.” This

study found that experienced GTAs populated all three different

factors/viewpoints. The viewpoint of one experienced GTA may not

necessarily be the same as another experienced GTA who was

represented by a different factor. Therefore, it may be a

limitation of the “Effective Teaching” course to be limited to

new GTAs, or to limit peer mentoring to pairing one new GTA and

one experienced GTA. This study has demonstrated that differing

viewpoints exist between the participants in the study, not just

because of simple demographic traits or experience, but because

they have different perceptions of graduate school. This is one

of the advantages of using Q Methodology as a needs assessment

tool. The perspectives and consensus of the GTAs and their

supervisors can be uncovered.

Since the benefits to the department or institution could

vary from one cohort of GTAs to another, it is important that

program evaluation be conducted regularly (Carroll, 1980). GTAs

enter school with varying degrees of experience, prior teaching,

experiences with students, approaches to diversity, and

249

motivation to persist in their programs. There were 18 sorts that

were represented by Factor 1 ("The Emerging Teacher"). The

participants in the group included ten females and eight males.

There was an even split with nine experienced and nine new GTAs.

Seventeen out of the 18 sorts included participants with teaching

experience, 12 with formal experience, four with informal

experience, and two who provided no answer. Ten sorts were

represented by Factor 2 (“The Preferred Researcher”). The

participants in the group included three females, and seven

males. There was an even split with five experienced and five new

GTAs. Every participant had taught before, but one only had

informal teaching experience. Six sorts were represented by

Factor 3 (“The Anxious GTA”). The participants in this group

included four females and two males. All of these sorters were

new GTAs except for one doctoral student. Every GTA had taught

before, but mostly (four sorts) in an informal setting, with two

teaching in a formal setting. The varying experiences of the

cohort of GTAs completing this Q Study may or may not be similar

to the cohort of GTAs in The Department of Biology the next year.

250

Only by repeating the needs assessment can the supervisors of the

program determine the viewpoints of the next cohort.

In addition to uncovering three distinct GTA viewpoints,

this study also unearthed 13 consensus statements which allow the

common voices of GTAs to be expressed. Q Methodology is a

powerful tool for determining consensus and perspectives of a

group (Ramlo, 2011). The consensus statements can highlight the

similarities between factors. These would be views shared by all

the GTAs. In this study, the consensus statements were mostly

items that the GTAs felt neutral about, such as dreaming about

teaching or research, knowing university policies, repeating

themselves while teaching, their own learning styles, or student

evaluations. The consensus statement that GTAs felt very strongly

was unlike their view (statement 49, I've had family problems

because of the pressures of graduate school) indicated that this

cohort of GTAs was not experiencing the emotional exhaustion that

often occurs in the academic community (Repak, 2012). Consensus

statements can be used to identify common ground within the

population of study, but in this case, were not as useful as the

most-like, most-unlike, and distinguishing statements.

251

Finally, it is also important to do a needs assessment that

includes the supervisors of the GTAs, so that they can be aware

of how their viewpoints differ from the viewpoints of the GTAs in

their course. Just as GTAs must be ready to work with diverse

students who may be completely unlike them as students, so must

the supervisors of GTAs. This study demonstrated that the

supervisors of the GTA instructional training program shared some

consensus with the GTAs, but also displayed different viewpoints.

The Biology Lead Faculty Member demonstrated a viewpoint that was

the opposite of GTAs loading on Factor 1. Q Methodology allows

the supervisors of the course to uncover their own viewpoints,

or perceptions of both new and experienced GTAs, which may be

helpful in designing effective professional development for the

GTAs.

Implications

Differentiating the Instructional Training Program

Once the typologies of GTAs are known through conducting a

needs assessment, professional development should be designed

around their needs through differentiation. Differentiation means

252

tailoring instruction to meet individual needs (Tomlinson, 2012).

Supervisors of GTA instructional training programs may

differentiate content, process, products, or the learning

environment, and use ongoing assessment and flexible grouping to

make this a successful approach to instruction. Differentiating

GTA instructional training does not have to mean teaching three

different instructional training courses for the three different

types of GTAs. Q Methodology can provide the supervisors of the

instructional training program the chance to adjust their

curriculum and instruction to maximize learning for all GTAs,

depending on their needs and ability levels (The IRIS Center,

n.d.).

The department should encourage GTAs to explore workshops,

courses, or seminars offered outside the department by the

graduate school or the faculty professional development

department on the campus. There may even be specialized

certifications that the GTAs can acquire such a certification for

online teaching or working with students with disabilities.

Factor 1 GTAs specifically asked for further development of

classroom management techniques and working with diverse 253

students. Factor 2 GTAs asked for advice on how to motivate

students and deliver better classroom lectures. Factor 3 GTAs

asked for public speaking advice and how to work with groups.

Each of these GTA preferences are often addressed by the

Institute for Teaching and Learning on campus, but only faculty

are sent notices of these workshops. Giving GTAs options to

expand on their instructional skills outside their instructional

training course, in meaningful and specific ways that the GTA

chooses, is vital to differentiating their professional

development. This is why it is also important for GTAs to

understand their own typologies, and what they mean as far as a

needs assessment.

Q Methodology as a Self-Diagnostic Tool

Sharing the results of the study (i.e. which factor the GTA

was represented by, what the factors uncover about differing

viewpoints, what differentiating or consensus statements were

uncovered by the study) with the GTAs could provide a “self-

diagnostic tool.” GTAs could adapt their own professional

development by adaptively scaffolding their own self-directed

learning (Ley, Kump, & Gerdenitsch, 2010). Self-directed learning254

is a self-initiated action that involves goal setting and

regulating one’s efforts to reach the goal, and can be seen as a

continuous engagement in acquiring, applying and creating

knowledge and skills in the context of an individual learner’s

unique problems (Fischer & Scharff, 1998).

Upon completion of the Q Sort and the subsequent analysis,

when a GTA learns their typology, or predicator profile, they

might be given a preparatory list of common needs for their

typology. GTAs could then be paired with a peer mentor, faculty

mentor, or supervisor who subscribes to a different typology, and

can provide additive scaffolding for the GTA. In self-directed

learning, the starting point is the perception of a knowledge

need of the learners arising in their actions. Based on this,

GTAs determine the goals of learning, initiate purposive

information seeking behavior by identifying and choosing possible

sources, and interact with the sources to obtain the desired

information (Choo, 1996). When approaching graduate school as a

“problem-based learning” environment, GTAs taking control of many

facets of their own acquisition of knowledge, facilitated by

their supervisors and under the parameters of their typology, 255

would be more relevant and purposeful to their personal

development.

Typically, all the new GTAs enrolled in the “Effective

Teaching” course would be provided a syllabus containing topics

to be discussed each week, such as “Preparing Your Teaching

Portfolio,” “Conducting a Mid-Term Evaluation,” or “What to do

About Plagiarism.” These are topics that the Lead Biology Faculty

Member and the Biology Lab Coordinator have determined to be

essential to the instructional training program for the GTAs.

Besides the traditional fixed scaffolding where a fixed list of

learning goals for the task (provided by the disciplinary

experts) is given to GTAs, GTAs could be introduced to an

adaptive scaffolding condition where they seek out advice on

several self-development strategies (i.e. Ask Factor 1 GTAs to

collaborate with their faculty advisor about policies that

concern their research. Ask Factor 2 GTAs to explore teaching

strategies that could improve their teaching presentation. Ask

Factor 3 GTAs to research strategies that facilitate a respectful

teaching environment.), and then journal their findings. They

could share these findings in class or with their “Effective 256

Teaching” instructors. Recommendations would then be provided to

the GTA on both their personal approach to meeting their learning

goals, and adaptations the GTAs could make in their behaviors

that would help them meet their goals by addressing their own

needs.

By personalizing the GTAs’ learning, layering both a common

set of topics with an adaptive, self-directed set of tasks or

topics based on their needs assessment, this would make their

professional development more meaningful and personally relevant.

Rather than a “one-size-fits-all” instructional training course

that is the same for all the new GTAs, their professional

development becomes highly personalized, built on their personal

preferences, based on what the typologies in the course are, and

not what the instructors perceive them to be. The course would be

adapted to account for the GTAs’ prior experiences, and their

preferences for a GTA program. Along with meeting the needs of

the new GTAs who were represented by each factor, identified

through the Q Sort factor analysis, the supervisors can also

account for the future needs that have been identified by

experienced GTAs. 257

Collective Mentoring

By adding a formal “collective mentoring” aspect to the

“Effective Teaching” course, the experienced GTAs, faculty, and

staff could provide the kind of collaboration and expertise on

the way GTAs develop over the span of teaching multiple semesters

at the university, that new GTAs asked for in their post-sort

interview questions. Collective mentoring is an evolution of the

multiple mentor/single mentee model whereby senior colleagues and

the department take responsibility for constructing and

maintaining a mentoring team (Chesler & Chesler, 2002). Tierney

and Bensimon (1996) have argued that “The notion of a single

experienced faculty member being willing and able to play the

all-inclusive role of mentor to a protégé is wishful thinking.”

Asking experienced GTAs, faculty, and staff to return to the

course, as a “panel of experts,” provides the new GTAs with

various points of view from people who have experienced what

these new GTAs are preparing to do. In truth, a variety of

individuals are required to help meet a mentee’s diverse needs

(Chesler & Chesler, 2002). This is currently done in a very

informal fashion, mostly occurring outside the realm on the

258

“Effective Teaching” course. Increasing the presence of other

members of The Department of Biology in the “Effective Teaching”

course, whose differing perspectives may provide collegiality to

the new graduate students, may help the GTAs to see that there

are other perspectives than their immediate faculty advisor,

supervisor, or lab mates.

Tierney and Bensimon (1996) point out that collective

mentoring is a formal and collective organizational task, part of

the organization’s responsibility to orient and socialize its new

members. As such, “mentoring need not take place only in a senior

faculty member’s office or an orientation session at the

beginning of the school year. The mail room, the faculty lounge,

and any number of other institutional locations have potential

for socializing individuals to the culture of the department and

organization.” Included in that list, for GTAs in The Department

of Biology, are places such as laboratories, lab meetings, course

meetings for the classes they teach, colloquium, and the

“Effective Teaching” course. Ginorio (1995) argues that students

need to find a meaningful community in science and engineering,

one that “would not include…outdated ideas of what a successful 259

culture of science is: competitive, all engrossing, demanding to

the exclusion of any other interest, and open only to the handful

of individuals who can pass all the tests.” Organizational change

that creates more egalitarian and caring communities will benefit

all students. In addition, promoting collegiality and civility

between not only faculty members, but GTAs, where they can

passionately share ideas, and then work together as a department,

sets a positive example for socializing future faculty.

Promises and Challenges of Q Methodology

In using Q Methodology as a needs assessment tool for GTAs

in an instructional training class, there are a number of

promises that Q Methodology holds, as well as some challenges. As

the typologies of the GTAs emerged, the profiles, or viewpoints,

lacked characteristics of “the needs of GTAs” that were described

in the literature. Some had a few of the characteristics. The

three typologies of GTAs were based upon the factor analysis of

the Q Sorts, not through asking GTAs simply “What type of GTA are

you?” The operant categories are functional, not just logical

distinctions (Brown, 1991). Because the typologies were uncovered

using factor analysis, they may be more meaningful than a Likert-260

style survey, which leads to the loss of meaning, or a case

study, which only examines a few views and is time-consuming, for

the professional development of GTAs.

Biology GTAs are situated in laboratory settings, with a PI

or faculty member heading the lab (Golde & Dore, 2001). The GTAs

join established labs, and then being a GTA provides a fee

remission for their tuition as well as a stipend for living

expenses. The GTAs in this Department of Biology may have

different viewpoints about their graduate school program than

GTAs in other disciplines, such as other STEM disciplines, the

social sciences, or the humanities. The reasons these GTAs

described for entering graduate school were complex, from one

Factor 1 GTAs describing that “I took the job of being a GTA

because I knew I was already a good teacher,” and one Factor 2

GTA saying “Although I don’t mind teaching, research is my

passion. I came to grad school for research, not to teach.” While

the literature does briefly mention going to graduate school for

“teaching opportunities (Malaney, 1987),” the Biology GTAs in

this study who were represented by Factor 1, “The Emerging

Teacher,’ described being a GTA as more than just a “teaching 261

opportunity.” Participant MNN24FF stated “I love TAing.” MNY24FI

stated in the post-sort discussion, “I knew I could do this. I

knew I’d love teaching, and I’m happy to have the opportunity to

do it while learning and conducting my research, which is more

challenging to me. Teaching is like the bright spot in my week.”

This group of GTAs did not need coaxing to embrace teaching as

part of their job in graduate school, and didn’t express

trepidation about this part of the job. This group did not

require a lecture about “why teaching is important.” They already

came to the department with this perspective. Through using Q

Methodology as a needs assessment tool, the post-sort interviews

uncovered views of which the supervisors of the “Effective

Teaching” course were unaware. Rather than spending time in the

course persuading GTAs to embrace teaching, time could be spent

on the professional development of skills that GTAs indicated in

their “program preferences” post-sort question.

Q Methodology provides a more accurate needs assessment for

an instructional training program than a Likert-style survey or

questionnaire. Although Q Methodology is similar to the Likert-

style survey scale in that the distribution on the grid typically262

ranges from least like my view to most like my view (Ramlo,

2008), Q differs from Likert-style surveys in that Q involves

participants physically sorting items relative to each other into a

normalized or Gaussian distribution (Brown, 1993; Brown, 1980;

McKeown & Thomas, 1988; Ramlo, 2008; Ramlo & Nicholas, 2009).

Likert (1967) assumed that every statement is equally important

to the overall attitude. Likert scales do not consider the weight

that sorters attach to individual items (ten Klooster, Visser, &

de Jong, 2008) which can therefore result in the loss of meaning

(McKeown, 2001; Ramlo & McConnell, 2008). As was demonstrated in

Cho et al. (2010), GTAs could mark every statement as “critically

important,” which does not help supervisors of the course

determine what is actually ranked the highest, in relation to the

other statements. Cho, Sohoni, and French’s (2010) needs

assessment for GTAs missed data that is critical to understanding

GTA needs. Surveys are common methods for collecting feedback;

however, they allow responders to give similar or identical

ratings to many or all items (Dennis, 1986). Though Q Methodology

is gaining recognition in education research, it is not as wide-

spread or commonly used as surveys or questionnaires.

263

Qualitative methods may generate transcripts of discussions

which reveal much about attitudes as they are expressed in the

normal social context of a discussion. However, these methods are

often criticized on the grounds that they lack statistical rigor

(Addams, 2000). There have been numerous case-studies of GTAs

(Darling & Staton, 1989; Muzaka, 2009; Park, 2002, 2004), but the

time it would take to do an in depth interview with each GTA

could prove onerous. Interviews and other purely qualitative

techniques are time consuming (Ramlo, 2006). Q Methodology

provided better results for guiding this instructional training

program because of Q’s efficiency. The qualitative data that is

collected, along with the statistical analysis, provided a rich

understanding of the types of viewpoints that exist among the

GTAs, without the time it takes to do in-depth interviews.

One of several benefits of using Q methodology in this

study, opposed to Likert-style surveys or case studies, is that

it allowed the researcher to examine how GTA viewpoints compared

to how their supervisor perceived their views (both as new GTAs

and experienced GTAs). It is interesting here to note that the

first Biology Lab Coordinator’s theoretical sort as a new GTA was264

represented by “The Anxious GTA,” and her theoretical sort as an

experienced GTA was represented by “The Preferred Researcher.”

The second Biology Lab Coordinator’s theoretical sort as an

experienced GTA was represented by “The Emerging Teacher.” The

Lead Biology Faculty Member’s theoretical sort as a new GTA

displayed a mixture of all three factors, not loading

significantly on any one factor, and his theoretical sort as an

experienced GTA was represented by “The Preferred Researcher.”

The supervisors did not have one single perception of new or

experienced GTAs, and in the case of The Biology Lead Faculty

Member’s theoretical sort of new GTAs, did not load on a

viewpoint at all. This may indicate supervisor’s misconceptions

about GTA needs that may have been missed if the supervisors’

perspectives had not been included in the needs assessment. This

study might be hand rotated in the future to force loading on a

factor by The Lead Biology Faculty Member, relative to the

viewpoints of the GTAs. But in this study, the three supervisors

were represented by three different factors. This reinforces the

need for collaboration by faculty and staff in the Department of

Biology, The Department of Education, and The Graduate School

265

about professional development. Collaboration, especially in the

form of team teaching, is not easy, however. It takes time and

energy to work together in planning, teaching, and evaluating.

Questions of teaching loads may come into play. And, of course,

the egos of many academics often make collaborative teaching

difficult, or even impossible (George & Davis-Wiley, 2000).

One of the reasons Q Methodology may help in the

collaboration of faculty and staff in an instructional training

program is that Q allows the supervisors of such programs to

scaffold the professional development of the GTAs according to

both supervisor and GTA typology. Results of the study could be

used to start a dialogue between faculty members, supervisors of

GTAs, and mentors about what types of supports each person can

provide. Scaffolds are temporary supports that help a learner

bridge the gap between what he or she can do and what he or she

needs to do to succeed at a learning task (Graves & Braaten,

1996). To guarantee that each GTA can internalize complex

concepts, supervisors of GTAs should consistently provide

scaffolding, often inventing supports on the spot as a GTA asks

for advice about a specific situation. Supervisors of GTAs should266

be able to draw on a rich mental database of examples, metaphors,

and enrichment ideas. The typology of the supervisor may reveal

clues as to the strengths or weaknesses of their viewpoints in

relation to the GTAs, which allows for more meaningful

professional development than one supervisor being in charge of

pedagogy, and the other being in charge of research, or however

the duties are divided. Because the supervisors of such programs

have vast experiences in higher education, and have a repertoire

of experiences, they should be able to offer GTAs insights into

how to successfully deal with students, teaching, or research

situations.

One of the challenges of using Q Methodology as a needs

assessment tool is that it requires a researcher that is able to

administer, analyze, and interpret the study; this requires a

researcher knowledgeable about Q Methodology. Because the factors

that emerge during a Q Study are not generalizable to larger

populations of GTAs, and instead produce an in-depth portrait of

the typologies of perspectives that prevail in a given situation

(Steelman & Maguire, 1999), the Q Sort may need to be repeated

with each cohort of GTAs. The researcher may find that, after 267

repeating the Q Sort over several semesters, the ratios of

typologies are relatively stable, meaning they occur in

approximately the same proportions each semester. If the

typologies are stable, the researcher could then repeat the Q

Sort every two to three years to demonstrate the stability, and

ascertain any changes. Because Q Studies are dependent on local

cultural conditions and context specific factors, as in this

case, the specific university climate and the specific discipline

(Biology), the Q Sort may need to be repeated (Baker et al.,

2006). The same three typologies may, or may not, emerge in each

repetition of the Q Sort, as the climate at the university or

within the department changes. Interpreting new or additional

viewpoints that emerge from the data takes a researcher who is

skilled at this type of data analysis. A Q Methodology expert may

not be available each semester the “Effective Teaching” course is

offered.

Suggested Further Research

The current research study revealed three factors that GTAs

had about their graduate school experience (“The Emerging

Teacher,” “The Preferred Researcher,” and “The Anxious GTA”). 268

Expanding the study to include GTAs teaching in a variety of

departments at the same institution, or in Biology Departments at

different institutions, may yield different factors. Other

departments at the same institution may have different pressures,

attitudes towards teaching, instructional training programs, or

research programs that would affect the viewpoints of their GTAs.

Other Biology Departments at similar institutions, or at low

versus high-research institutions may yield different viewpoints

among their GTAs. GTAs in the same Biology Department sorting at

different times (beginning of the semester, end of the semester,

mid-semester, summer, end of program) may affect their

perceptions of the statements.

This Q Sort could also be expanded to include faculty

members and staff in The Department of Biology. It would be

particularly useful if the faculty could recall their experiences

as a GTA, or to theoretically sort as they believe a new or

experienced GTA would sort. Faculty members have almost always

had experience as a GTA themselves, in that they have completed

graduate school, and are now teaching in The Department of

Biology. It would be useful to know whether faculty aligned with

269

a single factor, or whether faculty viewpoints are distributed in

a similar fashion to the GTAs’ viewpoints. This may allow for a

faculty modeling or peer mentoring program that better addresses

GTA needs. This type of modeling or mentoring could be formally

developed by faculty in the department. Because faculty serve as

mentors or role models for GTAs, they help to shape the GTAs

personal and professional development over time. Knowing GTA

viewpoints could help faculty convey both academic knowledge and

the “hidden curriculum” of academia. The increased awareness

about GTA views may benefit mentors as well, through greater

productivity, career satisfaction, and personal gratification

(Rose, Rukstalis, & Schuckit, 2005). It is possible that

supervisor views are out of date or out of sync with today’s

GTAs. Making faculty aware of how the needs of GTAs may have

changes since their time as a GTA would have implications for

faculty/GTA relationships.

Repeating the Q Sort with GTAs who have sorted at the

beginning of their program, and then completed a year of graduate

school, would yield potentially useful pretest/posttest results.

One statement made by supervisors about GTAs is “they don’t know

270

what they don’t know.” Sorting may take on different meaning

after undertaking the graduate school experience. The emergence

of Factor 3, “The Anxious GTA,” would be interesting to revisit,

to see if their viewpoints change after formally teaching, or if

they persist in their graduate school program. New or improved

interventions for GTAs who load of Factor 3 could improve

retention rates among this segment of GTAs.

Undergraduate learning outcomes are not part of the current

study, but could be incorporated into future studies. The end

result of an improved professional development program should be

increased student outcomes – increased scores on Biology Concept

Inventories, grades, motivation, content knowledge, retention

rates, etc. Biology GTAs also teach a variety of courses, from

introductory, non-majors laboratories, to upper-level content

specific courses. The improvement of the different courses that

GTAs teach in would be of interest.

Researchers could also pick individual GTAs who were

represented by a certain factors, and do a more in-depth, case-

study approach to their viewpoints. This would provide a more

qualitative data about how the GTA came to their point of view

271

than through limited, post-sort interview questions.

Alternatively, researchers could enter the details of the study

into a statistical analysis software package and look for

correlations or ANOVAs.

Only one study has attempted to identify graduate students

who are at risk for failure (defined as non-degree completion),

using such factors as Graduate Record Examination (GRE) scores,

graduate grade point average (GGPA) in the first nine hours of

graduate study, undergraduate grade point average (UGPA), age,

gender, academic area of study, and type of institution from

which the baccalaureate degree was earned. When all records were

analyzed, the GRE verbal score combined with either UGPA or GGPA

were significant predictors of degree completion. The highest

graduation rate occurred among students who earned their

undergraduate degrees from master's level institutions; students

from bachelor's institutions had the lowest graduation rate. The

results varied, however, when individual academic areas were

assessed (Nelson et al., 2000).

Lindle and Rinehart (1998) state “the GRE was designed for

‘traditional’ graduate students, those who pursue advanced

272

studies full time immediately or shortly after attaining their

baccalaureates” (p.1). Other studies have found that older

students score significantly lower particularly on quantitative

measures associated with the GRE (M. J. Clark, 1984; Hartle,

1983). If the GRE is designed for “traditional” graduate

students, and an estimated 48.6% of the 2,637,000 students

entering graduate school in 2003 were over the age of 30 (Digest

of Educational Statistics, 2004), predicting success in graduate

school needs a new tool for measurement. Q Methodology could

provide that needs assessment tool that indicates which students

are in need of additional support in their graduate school

program. Because one group of GTAs who were represented by Factor

3 demonstrated excessive frustration and anxiety, they may need

counseling, peer mentoring, or advising that GTAs displaying the

other viewpoints do not need. In the distinguishing statements

for Factor 3, statement 25 (I have no idea what students think

about me, and that makes me uncomfortable), statement 26 (I have

no idea what the level of understanding is with these students),

and statement 48 (I'm worried that the students won't be able to

understand me), helped define this factor. These GTAs may be more

273

comfortable in their passive position as a student than in their

active position as a teacher or researcher. They also demonstrate

that they are worried about many aspects of graduate school and

may require additional support to persist in their programs. An

area of future research may be focusing on how many of these

Factor 3 students complete their degrees, or how to additionally

support them.

Summary

Chapter V began with a summary of the study, a statement of

the problem, a statement of the procedures, and the general and

specific research hypotheses. The conclusions of the study are

drawn from Chapter IV analyses. Three factors, or viewpoints,

emerged from the sorting process. The researcher names these

three viewpoints, “The Emerging Teacher,” “The Preferred

Researcher,” and “The Anxious GTA.” Q Methodology can provide

predictor typologies that are more useful than simple variables

and demographic information for the classification of people,

especially within program evaluation (Newman & Ramlo, 2011). The

implications of the study are discussed, along with possible

future research.

274

Differentiating the GTAs’ professional development by using

their Q Sort data may lead to more meaningful and relevant

professional development. Scaffolding instruction, using self-

directed learning, and peer or faculty mentoring may strengthen

the skills of GTAs. GTA training programs would be much more

significant if it used the viewpoints of the different typologies

of GTAs to reinforce positive behaviors, enhance GTAs’ strengths,

and supplemented their skills where needed. Further studies that

use these factors/viewpoints to modify GTA professional

development, or modify their graduate school program to encourage

program completion are needed. Future studies may use larger sets

of GTAs, GTAs from different departments, or GTAs from different

institutions to further explore GTA viewpoints. Other data, such

as student learning outcomes, field observations, and case

studies, may provide greater detail for developing meaningful GTA

professional development.

275

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330

APPENDICES

331

Appendix 1: Concourse Development

ConcourseStatement Source Concourse

theme

Usedin Q

Sample?

Q Sample Q Sampletheme

I feeloverwhelmedwith workmy advisorgives me

SRQ Advisor1/3 yes

I feeloverwhelme

d withwork myadvisorgives me

Advisor1/1

I feel likemy advisorwill help

me learn toteachbetter

SRQ Advisor2/3 no

If I didn'tknow whatto do in alab, I feellike I knowwho to askfor help

SRQ Advisor3/3 no

I feel likean

outsider,and thatpeople at

grad schoolwon't

accept me

PGSS Anxiety1/10 yes

I feellike an

outsider,and thatpeople at

gradschoolwon't

accept me

Anxiety1/4

I have lostsleep

because I'mworriedabout

(Tierney &Bensimon,1996)

Anxiety2/10

yes I havelost sleepbecauseI'm

worried

Anxiety2/4

332

teaching aboutteaching

I've hadfamilyproblemsbecause of

thepressures

of graduateschool

PGSS Anxiety3/10 yes

I've hadfamily

problemsbecause of

thepressures

ofgraduateschool

Anxiety3/4

Usingsocial

media (likeTwitter orFacebook)

helps me tofeel likeI'm notalone

(BatesHolland,2008)

Anxiety4/10 yes

Usingsocialmedia(like

Twitter orFacebook)helps meto feellike I'mnot alone

Anxiety4/4

I am soworriedabouttakingtests in

school thatI feel sick

(Darling &Staton,1989)

Anxiety5/10 no

The amountof debt I

have,because ofschool,makes meupset

(Golde &Dore,2001)

Anxiety6/10 no

My studentsnitpick

about everysingle

SRQ Anxiety7/10

no

333

point Itake off onassignments

I willnever beable to

learn everystudent's

name

SRQ Anxiety8/10 no

Severaltimes, I'vefelt likeI'm goingto have anervousbreakdown

SRQ Anxiety9/10 no

Everyone ingrad schoolis reallystressedout and

unfriendly

SRQ Anxiety10/10 no

I canbalancebeing agood

teacherwith beinga goodstudent

SRQ Balance1/4 yes

I canbalancebeing agood

teacherwith being

a goodstudent

Balance1/2

I disliketeaching,and wish Icould spendmore time

on myresearch.

SRQ Balance2/4 yes

I disliketeaching,and wish I

couldspend moretime on myresearch.

Balance1/2

I want togo to

(Boyle &Boice,

Balance3/4 no

334

socialactivitieson campus,but I don'thave time

1998)

I believeI'm a verycompetentresearcher

PGSS Balance4/4 no

Being a TAwill helpme to be a

goodprofessorsomeday

SRQ Career1/2 yes

Being a TAwill helpme to be a

goodprofessorsomeday

Career1/2

Sometimes Iworry thatI might

have chosenthe wrong

career path

(Boyle &Boice,1998)

Career2/2 yes

SometimesI worrythat I

might havechosen the

wrongcareerpath

Career2/2

I feel likemy fellowTAs willhelp me to

teachbetter

SRQ Collaboration 1/1 yes

I feellike my

fellow TAswill helpme toteachbetter

Collaboration 1/1

Being a TAhelps meask betterquestions

(Feldon etal., 2011)

Confidence 1/10 yes

Being a TAhelps me

ask betterquestionsin my

research

Confidence 1/7

I feel likeit will beeasy to

(Luo,Bellows, &

Grady,

Confidence 2/10 yes

I feellike itwill be

Confidence 2/7

335

manage myclass 2000)

easy tomanage myclass

I feel likestudentslook at meweird whenI tell themI'm a TA,like I'mnot goodenough to

be teachingat the

university.

SRQ Confidence 3/10 yes

I feellike

studentslook at meweird when

I tellthem I'm aTA, likeI'm notgood

enough tobe

teachingat the

university.

Confidence 3/7

I feelpretty

comfortableusing

technologyin my class

(Marincovich,

Prostko, &Stout,1998)

Confidence 4/10 yes

I feelpretty

comfortable using

technologyin myclass

Confidence 4/7

I feelpretty

confidentthat I'm a

goodteacher

(Boyle &Boice,1998)

(Prieto &Altmaier,1994)

Confidence 5/10 yes

I feelpretty

confidentthat I'm a

goodteacher

Confidence 5/7

I feelself-

confidentwhen Iteach

(Prieto &Altmaier,1994)

Confidence 6/10 yes

I feelself-

confidentwhen Iteach

Confidence 6/7

I have alot of SRQ Confidenc

e 7/10 yes I have alot of

Confidence 7/7

336

anxietyabout

teaching,because Idon't knowwhat toexpect

anxietyabout

teaching,because Idon't knowwhat toexpect

My studentsthink I'm

interestingSRQ Confidenc

e 8/10 no

I feelconfident Iwill be a

goodteacher

SRQ Confidence 9/10 no

I amapprehensiv

e aboutteachingBiology

SRQ Confidence 10/10 no

I have noidea whatthe level

ofunderstanding is with

thesestudents

SRQ Diversity1/4 yes

I have noidea whatthe level

ofunderstand

ing iswith thesestudents

Diversity1/2

I'm worriedthat thestudentswon't beable to

understandme

SRQ Diversity2/4 yes

I'mworriedthat thestudentswon't beable to

understandme

Diversity2/2

I think allof my

students

(Nyquist &et al.,1991)

Diversity3/4 no

337

will be onpretty muchthe samelevel

My studentscome fromvarious

backgrounds

(Nyquist &et al.,1991;

Loreto R.Prieto &Meyers,2001)

Diversity4/4 no

I feel likeI need toconstantlymonitor mystudents

forcheating

SRQ Effort1/4 yes

I feellike Ineed to

constantlymonitor mystudents

forcheating

Effort1/4

I have torepeat

myself overand over toget these

students tounderstand

me

SRQ Effort2/4 yes

I have torepeatmyself

over andover to

get thesestudents

tounderstand

me

Effort2/4

If I teachwell, Iwill get

goodstudent

evaluations

(Marsh,1984;

Wachtel,1998)

Effort3/4 yes

If I teachwell, Iwill getgood

studentevaluation

s

Effort3/4

Moststudentswill do

SRQ Effort4/4 yes

Most of mystudentswill do

Effort4/4

338

just enoughto get by

justenough toget by

I think oneof the mostimportantthings

about beinga TA isbeing

ethical

(Branstetter &

Handelsman, 2000)

Ethical1/2 yes

I thinkone of the

mostimportantthingsabout

being a TAis beingethical

Ethical1/1

I wouldlike to getto know mystudentsoutside of

class

(Cotten &Wilson,2006)

Ethical2/2 no

My studentswill

respect mebecause I'm

fair

(Burrowes,2003)

personalcorrespond

ance

Fair 1/2 yes

Mystudentswill

respect mebecauseI'm fair

Fair 1/1

I will tryto treatall mystudentsthe same,becausethat iswhat'sfair.

SRQ Fair 2/2 no

I worrythat

certainstudents inmy classmight know

(Rushin etal., 1997)

Intelligence 1/2

yes I worrythat

certainstudentsin myclass

Intelligence 1/1

339

more aboutBiologythan I do

might knowmore aboutBiology

than I doI'm afraidthat mystudentswill thinkI'm notsmart

SRQ Intelligence 2/2 no

All mystudents

are capableof

understanding Biology

SRQlearningstyles1/11

yes

All mystudents

arecapable ofunderstand

ingBiology

Learningstyles1/5

I learnedbest byactivelydoing labs

(Kugel,1993)

learningstyles2/11

yes

I learnedbest byactively

doing labs

Learningstyles2/5

I learnedbest bylistening

toprofessors

teach

(Kugel,1993)

learningstyles3/11

yes

I learnedbest by

listeningto

professorsteach

Learningstyles3/5

I thinkmost of mystudentslearn in away that'ssimilar tothe way I

learn

(Golish,1999)

learningstyles4/11

yes

I thinkmost of mystudents

learn in away that'ssimilar tothe way Ilearn

Learningstyles4/5

My studentswill likeBiologybecause I

SRQ learningstyles5/11

yes Mystudentswill likeBiology

Learningstyles5/5

340

can make itinteresting

because Ican make

itinterestin

gI want my

students todo higher

orderthinking

(Burrowes,2003)

learningstyles6/11

no

I know howto get

groups towork

together

(Knight &Wood,2005)

learningstyles7/11

no

Most of mystudents in

my lablearn justlike me

(Luft etal., 2004)

learningstyles8/11

no

I will befriendly tomy students

while Ishare myknowledgewith them

(Luft etal., 2004)

learningstyles9/11

no

I want mystudents tobe able tomemorize

facts abouta subject

PClearningstyles10/11

no

My studentswill

appreciatesciencewhen theyare done

SRQ learningstyles11/11

no

341

with thiscourse

Being a TAhas helped

me toafford grad

school

(Girves &Wemmerus,1988)

Practical1/2 yes

Being a TAhas helped

me toaffordgradschool

Practical1/1

I think Icould

create asyllabusfor a

course Imight teach

(Davis,2009)

Practical2/2 no

Being agood

teacher isas

importantas being a

goodresearcher

(Hattie &Marsh,1996;

Nyquist etal., 1999)

Preparation 1/8 yes

Being agood

teacher isas

importantas being a

goodresearcher

Preparation 1/4

I believe Iknow what

it takes tobe a goodresearcher

(Nyquist &Woodford,2000)

Preparation 2/8 yes

I believeI knowwhat ittakes tobe a goodresearcher

Preparation 2/4

I know whatthe Biologydepartmentexpectsfrom myteaching

SRQ Preparation 3/8 yes

I knowwhat theBiology

departmentexpectsfrom myteaching

Preparation 3/4

I know whatthe

department

(Nyquist &Woodford,2000)

Preparation 4/8 yes

I knowwhat the

department

Preparation 4/4

342

expectsfrom myresearch

expectsfrom myresearch

I know whatthe

departmentexpectsfrom gradstudents

(Nyquist &Woodford,2000)

Preparation 5/8 no

I feel likeI'm

prepared tohandle

challengingstudents

(Young &Bippus,2008)

Preparation 6/8 no

How am Isupposed to

help mystudentswhen I

don't evenknow whatto do?

PC Preparation 7/8 no

My to-dolists are amile long

PC Preparation 8/8 no

I came tograd schoolmainly so Icould doresearch

(Nyquist &Woodford,2000)

Research1/8 yes

I came togradschool

mainly soI could doresearch

Research1/6

I know theuniversitypolicies

that relateto my

research

(Luft,Kurdziel,Roehrig, &Turner,2004)

Research2/8 yes

I know theuniversitypoliciesthat

relate tomy

research

Research2/6

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I likedoing

researchover

teaching

(Colbeck,1998;

Levinson-Rose &Menges,1981)

Research3/8 yes

I likedoing

researchover

teaching

Research3/6

I likedoing

teachingover

research

(Colbeck,1998;

Levinson-Rose &Menges,1981)

Research4/8 yes

I likedoing

teachingover

research

Research4/6

I think allthis

teachinggets in theway of myresearch

(Boyle &Boice,1998)

Research5/8 yes

I thinkall thisteachinggets in

the way ofmy

research

Research5/6

I thinkresearch is

verychallenging

SRQ Research6/8 yes

I thinkresearchis very

challenging

Research6/6

My researchis making

animportant

contribution to

science

(Marsh,1980)

Research7/8 no

I think mystudentswould findmy research

to becompletelyboring

SRQ Research8/8 no

I am good (Golish, Respect yes I am good Respect344

at creatinga

respectfulclassroom

environment

1999) 1/3

atcreating arespectfulclassroomenvironmen

t

1/3

I feel likeI'm a goodteacherbecause Iam closerin age to

my students

SRQ Respect2/3 yes

I feellike I'm a

goodteacher

because Iam closerin age to

mystudents

Respect2/3

I have noidea whatstudents

think aboutme, and

that makesme

uncomfortable

(Rubin &Smith,1990)

Respect3/3 yes

I have noidea whatstudentsthink

about me,and thatmakes me

uncomfortable

Respect3/3

I don'tthink

teachingrequires alot ofemotion

SRQ Teaching1/16 yes

I don'tthink

teachingrequires a

lot ofemotion

Teaching1/9

I feel likeI could go

intoteaching as

aprofession

SRQ Teaching2/16 yes

I feellike I

could gointo

teachingas a

profession

Teaching2/9

I know whatattributes SRQ Teaching

3/16 yes I knowwhat

Teaching3/9

345

make a goodteacher

attributesmake agood

teacher

I thinksome peopleare naturalteachers

SRQ Teaching4/16 yes

I thinksome

people arenaturalteachers

Teaching4/9

I thinkteaching is

verychallenging

SRQ Teaching5/16 yes

I thinkteachingis very

challenging

Teaching5/9

I thinkthat I give

goodteaching

presentations

SRQ Teaching6/16 yes

I thinkthat I

give goodteaching

presentations

Teaching6/9

I think youcan be

"taught toteach"

(Hardré,2003)

Teaching7/16 yes

I thinkyou can be"taught to

teach"

Teaching7/9

I want allstudents toactively

participatein my class

(Smith etal., 2005)

Teaching8/16 yes

I want allstudents

toactively

participate in myclass

Teaching8/9

I want toteach the

same way myfavoriteprofessortaught

SRQ Teaching9/16 yes

I want toteach thesame way

myfavoriteprofessortaught

Teaching9/9

I will (Hardré, Teaching no346

teachdifferentlythan I wastaught

2003) 10/16

I know howto dealwith

disruptiveor

inappropriate students

(Luo etal., 2000)

Teaching11/16 no

I know howto handle

an in-classdiscussion

(Prieto &Altmaier,1994)

Teaching12/17 no

I feelconfidentthat Icouldhandle

problems inmy

classroom

PGSS Teaching13/17 no

I know theuniversitypolicies

that relateto my

teaching

PGSS Teaching14/16 no

As I becomea better

teacher, mystudents

willreceivequality

instruction

SRQ Teaching15/16 no

I havedeveloped a SRQ Teaching

16/16 no

347

pretty goodgeneralteachingphilosophy

I have haddreamsabout my

teaching orresearch

(Kerry,2005;

Nyquist etal., 1999)

(PC)

Unknown1/1 yes

I have haddreams

about myteaching

orresearch

Unknown1/1

Note: SRQ – Self-Reflection Questionnaire, PGS – Perceptions of Graduate School Survey, GLF – Grad Life Forum, PC – Personal Correspondence

348

Appendix 2: Q Sample

1 All my students are capable of understanding Biology

2 Being a good teacher is as important as being a good researcher

3 Being a TA helps me ask better questions in my research

4 Being a TA will help me to be a good professor someday

5 I am good at creating a respectful classroom environment

6 I believe I know what it takes to be a good researcher

7 I came to grad school mainly so I could do research

8 I can balance being a good teacher with being a good student

9 I dislike teaching, and wish I could spend more time on my research.

10 I don't think teaching requires a lot of emotion

11

I feel like an outsider, and that people at grad school won'taccept me

12 I feel like I could go into teaching as a profession

13

I feel like I need to constantly monitor my students for cheating

14

I feel like I'm a good teacher because I am closer in age to my students

15 I feel like it will be easy to manage my class

16 I feel like my fellow TAs will help me to teach better

17

I feel like students look at me weird when I tell them I'm a TA, like I'm not good enough to be teaching at the university.

18 I feel overwhelmed with work my advisor gives me

1 I feel pretty comfortable using technology in my class

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920 I feel pretty confident that I'm a good teacher

21 I feel self-confident when I teach

22

I have a lot of anxiety about teaching, because I don't know what to expect

23 I have had dreams about my teaching or research

24 I have lost sleep because I'm worried about teaching

25

I have no idea what students think about me, and that makes me uncomfortable

26

I have no idea what the level of understanding is with these students

27

I have to repeat myself over and over to get these students to understand me

28 I know the university policies that relate to my research

29 I know what attributes make a good teacher

30 I know what the Biology department expects from my teaching

31 I know what the department expects from my research

32 I learned best by actively doing labs

33 I learned best by listening to professors teach

34 I like doing research over teaching

35 I like doing teaching over research

36 I think all this teaching gets in the way of my research

37

I think most of my students learn in a way that's similar to the way I learn

3 I think one of the most important things about being a TA is

350

8 being ethical39 I think research is very challenging

40 I think some people are natural teachers

41 I think teaching is very challenging

42 I think that I give good teaching presentations

43 I think you can be "taught to teach"

44 I want all students to actively participate in my class

45 I want to teach the same way my favorite professor taught

46

I worry that certain students in my class might know more about Biology than I do

47 If I teach well, I will get good student evaluations

48 I'm worried that the students won't be able to understand me

49

I've had family problems because of the pressures of graduateschool

50 Most of my students will do just enough to get by

51

My students will like Biology because I can make it interesting

52 My students will respect me because I'm fair

53

Sometimes I worry that I might have chosen the wrong career path

54

Using social media (like Twitter or Facebook) helps me to feel like I'm not alone

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Appendix 3: Conditions of Instruction

TA Perceptions of Graduate School Q Sort

Thank you for taking time to help us better understand the views related to this topic. Please follow the instructions below. If you have a question, just ask!

Instructions 1. Please read and consider each statement in the envelope carefully as it relates to your view of graduate school and as a Biology Teaching Assistant.. 2. Remove the pieces of paper from the attached envelope. Each ofthe 54 pieces of paper contains one statement. 3. Read each statement and then, based upon your views of graduate school place each statement into one of three piles while attempting to make these piles of EQUAL size (about 18 statementsin each pile) HERE:

MOST UNlike my

view

(~18 statements

here)

Neutralview about

thisstatement

(~18

statements

here)

Most like myview

(~18 statements

here)

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4. Now take the MOST LIKE pile and distribute it on the distribution sheet (last page - BLUE) putting your top FOUR (4) MOST LIKE statements in position +5, and then working toward the 0-column. Repeat with the UNLIKE pile and finally with the NEUTRAL pile. 5. You may move your statements around until you are satisfied with their placement. 6. Each square on the grid should have only one statement number;each number is only used once. 7. Write the statement-number in their appropriate location on the grid on the next page (PAGE 2). 8. Finally, answer the questions underneath the grid.

*********************************************************************************

Please answer the following questions:

Are you in a Master’s or Doctoral degree program (circle)? Master’s Doctoral

Are you an international student? Yes No

What is your age, in years? ________________

What is your gender? _____________

Do you have any experience teaching (either formal or informal)? ______ If yes, in what setting, and how many semesters or how many years?

What are you planning to do after graduate school?

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Briefly (a few sentences) describe what you would like to get outof a TA training program:

354

TA Perceptions of Graduate School Q Sort

Each box must contain ONE (1) statement number; each number must be used only once.

Sort based on your view of being a Biology TA in graduate school

4 4 5 5 6 6 6 5 5 4 4

Most

unli

ke

my

view

s

neut

ral

Most

like

my

view

s

-5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5

Please answer the following questions regarding your sort:

355

Tell us why you selected the four statements you placed under +5 (most like my view)?

Tell us why you selected the four statements you placed under -5 (most unlike my view)?

Please describe your decision-making process during the sort. Didyou gain insight about your views as you sorted the statements? If so, please describe. You may continue writing on the back of this sheet.

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Appendix 4: IRB Informed Consent Letter

Amy HollingsworthNatural Science Biology Lab CoordinatorThe University of Akron

302 Buchtel CommonsAkron, OH 44312

You are invited to participate in a study being conducted byAmy Hollingsworth, the Natural Science Biology Lab Coordinator and a doctoral student in the Department of Curricular and Instructional Studies at The University of Akron and Susan E. Ramlo, PhD, STEM Initiatives and The College of Education at The University of Akron. The project investigates the views of graduate biology teaching assistants on teaching, learning, students, and research. The project includes a Q Sort which you will be asked to conduct. Analysis of views will be done using the Q Methodology analysis technique.

Should you agree to participate you can expect the time to perform the Q Sort to be about 15-30 minutes. You may be contacted after the Q Sort has been completed if further clarification about your sort is needed.

The Q Sort will be conducted during the “Effective Teaching”class in the Fall semester of 2012. If you agree to participate, you may refuse to answer any questions and may withdraw from the study at any time.

Completion of the Q Sort will serve as your consent to participate in this study. You may keep this form for your records. Participants’ answers on the questionnaires will be recorded, without any identifying information, and used only by the researchers for organizational and analytical purposes. Your confidentiality will be protected throughout the study.  Any data

357

obtained from you (Q Sort) will be kept confidential and will notbe viewed by anyone but the researchers. All identifying information will be retained in a locked cabinet or other locked storage area. The data will be kept for no more than two (2) years and will be destroyed upon completion of the project. There are no anticipated benefits or risks to you as a participant.

If you have any questions about the research project, you can call either Dr. Ramlo at 330-972-7057 (sramlo@uakron.edu) or Amy Hollingsworth at 330-972-5268 (amyholl@uakron.edu). This research project has been reviewed and approved by The Universityof Akron Institutional Review Board for the Protection of Human Subjects. Questions about your rights as a research participant can be directed to Ms. Sharon McWhorter, Associate Director, Research Services, at 1-330-972-7666.

Thank you for your participation!

Sincerely, 

 

Amy HollingsworthNatural Science Biology Lab CoordinatorThe University of Akron

358

Appendix 5: IRB Exemption Request

359

360

361

362

Appendix 6: IRB Exemption

1a. Provide a brief description of the purpose of the proposed project and the procedures to be used. What will research subjects be asked to do? How long will it take?

The “Teaching Assistant Perceptions of Graduate School Q Survey” will be used to survey the viewpoints of current Teaching Assistants in the Biology Department as to their perceptions of teaching, students, research, and graduate school life. The participants will rank order 54 statements into a grid for least like their viewpoints, to most like their viewpoints. The survey will take between approximately 30 minutes to complete.

1b. Provide the process by which individuals will be recruited. Describe any qualifying characteristics of the subject populationsuch as gender, age ranges, ethnic background and health status. Indicate any special classes of subjects that might be included in the study population (e.g., socially or economically disadvantaged, minors, mentally disabled.) Estimate the number ofsubjects to be recruited.

The Q Survey will take place during the Biology TAs’ “Effective Teaching” class. Most teaching assistants are between the ages of22 and 25. There are no special classes included in the study population. There are 11 graduate biology students enrolled in the “Effective Teaching” course.

1c. Where will data collection take place (e.g. university, outside agency, school district, hospital, etc) and who will collect the data? Attach letter(s) of authorization to perform the research from all off-campus sites.

Data collection will take place in the classroom, which is located in the Auburrn Science and Engineering Building at The University of Akron. The data will be collected by the

363

researcher, and results will be stored in the researcher’s secureand locked office.

1d. Describe any potential risks - physical, psychological, economic, social, legal or other. Indicate how you will eliminateor reduce any potential risks to subjects. Only minimum risk research is eligible for exemption.

There is no potential risk to the participants. The Q Survey asks Teaching Assistants to reflect on their viewpoints ofthe Biology Laboratory and Teaching Assistant experience, which will be done anonymously. The data will not be reported individually, but will be summarized for each of the sections. TAs may discontinue their participation at any time. There is little likelihood that a 30-minute survey done in class would trigger psychological stress.

1e. Describe any potential benefits of the research to subjects or to society.

There are no known benefits to completion of the survey. Survey results will guide the creation of future teaching assistant courses in the Biology Department.

1f. Explain how individual privacy will be protected. For example, if interviewing, where will that be conducted?

Q Surveys will be completed individually. They will be asked to reflect on their sorting experience anonymously.

1g. Explain how individual confidentiality or anonymity will be protected. What kind of information will be recorded and how willit be protected? Who will have access to the data and where will it be kept? Will any identifying information be included in publications or presentations of the research?

There is limited demographic information on the survey, so

364

individual participants cannot be identified. The instrument will be administered anonymously. There will not be enough identifying information on the instrument to trace answers back to participants. The primary and co-investigator will bethe only individuals who have access to the data. The data files will be shared electronically between Dr. Susan Ramlo and Ms. Hollingsworth. Both Dr. Ramlo and Ms. Hollingsworthhave computers that are password protected. No identifying information will be included in any publication or presentation.

Questionnaires will be coded in order to match them with the Q Sorts. Only these codes will be used in the recording of datain an PQ Method file. Only the PI and CI will have access to the original data and it will remain secured in a locked filing cabinet. Once the data for a participant is complete and recorded, the original documents will be shredded.

No identifying information will be included in any publication orpresentation of the data.

1h. Describe your consent procedures. Provide justification if you do not plan to collect a signed consent from each participant. (Provide a copy of the consent form or information sheet you will provide to participants.)

We seek a waiver of signed informed consent for the following reason:

“That the research presents no more than minimal risk of harm to subjects and involves no procedures for which written consent is normally required outside of the research context.”

Please see attached informed consent instructions.

365