Teaching Abstract statistics in Online Education

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2009 27: 271 originally published online 29 December 2008Social Science Computer ReviewMichael L. Vasu and Ali O. Ozturk

Computer SimulationTeaching Methodology to Distance Education Students Using Rich-Media and

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Teaching Methodology to DistanceEducation Students UsingRich-Media and ComputerSimulationMichael L. VasuAli O. OzturkNorth Carolina State University, Raleigh, North Carolina

This article addresses the two major issues involved in the teaching of an introductory

methodology course versus face-to-face instruction. The first issue incorporates a rich-

media solution, specifically streaming video, to precede traditional notes on any topic and

second the use of a computer simulation software created by the authors, which can be placed

on a matriculated distance education student’s desktop remotely, without the legal or logistical

problems of using commercial software, for example, Statistical Package for the Social

Sciences (SPSS) or Statistical Analysis Software (SAS). Technical and pedagogical dimen-

sions of these particular issues are discussed as well.

Keywords: distance education; computer simulation; software; pedagogy; research

methodology

Instructors involved in teaching introductory courses in public opinion and political sci-

ence methodology are confronted with the need to incorporate computer-based materials

into their courses. For face-to-face students, there are abundant alternatives.1 However, for

distance education (DE) courses where students are not required to come to the campus and

are located in time zones, literally, throughout the world, computer applications section of

the course creates a series of logistical, legal, and pedagogical problems for the methodol-

ogy instructor. In addition to the desire to provide the hands-on experience with computer

analysis, methodology instructors must accomplish the traditional objectives of imparting

various methodological concepts (e.g., the effects of a control variable on a bivariate rela-

tionship) as well as confront the problem of teaching various statistical and methodological

concepts to an audience whose mathematical maturity and preparation are usually very

heterogeneous.

Research emerging from a number of studies has recently uncovered insights with

respect to the financial parameters and the technological needs and desires of DE stake-

holders (faculty, students, and administrators). In their recent study including seven univer-

sity campuses around the nation, Jafari, McGee, and Carmean (2006) determine that DE

stakeholders are eagerly waiting for light, nimble, mobile, smart, helpful, and intuitive

Authors’ Note: Please address correspondence to Michael Vasu, NCSU Box 8102, Raleigh, NC 27695;

e-mail: [email protected]

Social Science Computer Review

Volume 27 Number 2

May 2009 271-283

# 2009 SAGE Publications

10.1177/0894439308327129

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hosted at

http://online.sagepub.com

271



teaching and learning technology systems (Howell, Williams, & Lindsay, 2003). To this list,

we would add the issue of the cost for DE delivery (Taylor, Vasu, Vasu, & Steelman, 2002).

We have found that our public opinion research methodology DE course is significantly

enhanced if traditional lecture notes can be augmented with rich media, in particular video

that can be updated at will (literally daily). This video allows the instructor to explain con-

cepts such as sampling, the null hypothesis, statistical significance, and so on in a video for-

mat, which is then reinforced in lecture online. This also allows the instructor to present the

material in a preview, view, and review formats thus giving instructors the light, nimble,

mobile, smart, helpful, and intuitive teaching and learning technology systems they desire.

In our view, any DE course is enhanced if traditional lecture notes can be augmented with

rich media, in particular video that can be updated at will (frequently in response to student’s

questions). However, in our view, this technology is particularly useful in methodology

courses that seek convey concepts such as control, sampling, the null hypothesis, statistical

significance, and so on for a constituency with whom you will only have contact online.

It has been, and in many ways still is, a significant challenge to develop and use a

technology that could satisfy all the needs and desires of the DE faculty and students.

This is particularly true for social science curriculums in terms of embracing the challenge

of applying technology to the teaching–learning process (Nufrio, 2007). Indeed, the search

for better strategies significantly depends on the available hands-on and user-friendly DE

technologies that will ensure simplicity in presentation creation and in delivery to DE

faculty while providing flexibility in managing students’ time. We believe MediaSite goes

a long way toward achieving these previously mentioned objectives.

Designing an Online Public Opinion Research Course

With the initiation of the University’s first entirely DE BA degree completion program

(Leadership in the Public Sector), we were approached to convert a survey research meth-

odology course—originally developed for on-campus political science major students—

into the DE format. As the literature suggests, there is a typical series of steps in course

development of this type (Care & Scanlan, 2001; Ko & Rossen, 2004). The first step of the

conversion process was very time-consuming in terms of creating electronic versions of

entire course notes, assignments, and other relevant materials including the course syllabus

and book review formats.

During the second stage of course design, we chose Blackboard Vista, an online course

delivery platform made available to our DE faculty. We started with the configuration of the

course materials (such as lecture notes, syllabus, supplemental written materials, discussion

board protocols, assignments, and final exam content) within Blackboard Vista. Blackboard

Vista offered significantly useful teaching and course management tools; these tools them-

selves are inadequate to elaborate relatively complex topics and assignments of our meth-

odology course. Therefore, we supplemented our course content with additional streaming

videos to build an online, media-rich learning platform using Blackboard Vista and

MediaSite tools and systems.

MediaSite is used to create course presentations incorporated in our course lecture mod-

ules located in the course homepage.2 Accordingly, students were asked to watch a lecture

272 Social Science Computer Review



presentation that introduces the subject of a particular lecture module before reading the

lecture notes or doing any lecture activities. DE students can watch and listen to their

instructor’s presentation live or on-demand while following any additional presentation

supplements. In our view, MediaSite goes a long way toward what distance educators want

in terms of learning technology systems (Jafari et al., 2006).

Figure 1 shows an example of MediaSite live presentation outlook for one of the lecture

presentations. In this case, one of the authors in this article presents the lecture along with

slides automatically provided by the MediaSite recorder via a document camera hooked up

to another computer showing our online course content (Vasu and Ozturk, 2008).

Using the Elaboration Model Simulation

At this point, we will turn to our use of a simulated data set that we used in teaching the

logic of causal analysis to undergraduate DE students called the Elaboration Model.3 We

agree with Earl Babbie (2006, p. 422) that the Elaboration Model is one of the best ways

of explaining causal analysis in the social sciences using contingency table analysis as a

precursor to more multivariate concepts. The software is called the Elaboration Model in

that it takes a great deal of its form from the model of the same name, which dates back

to the pioneering work of Paul Lazarsfeld (1982). The basic structure of the simulation/

Figure 1

Lecture Video Screen Produced Via MediaSite

Vasu, Ozturk / Teaching Methodology to Distance Education Students 273



software is to comprehend the relationship between the two variables after the introduction

of a ‘‘test’’ or ‘‘control’’ variable and then observe the effect on the original relationship.

Additionally, the simulation asks the student to identify whether the control variable is

intervening or antecedent to the two variables in the original bivariate relationship. Once

this is established, the student is asked to examine and define the outcome of the problem

posed. The program was designed to produce output that indicates whether the partial rela-

tionships (relationships in the control tables after the introduction of the control variable)

are statically significant. The partial relationships can remain the same, go away, or the par-

tial table can be split with one relationship remaining statistically significant and the other

becoming nonsignificant.

These outcomes in the Elaboration Model, which are the effect of controlling the third

variable, are defined as

1. Replication, which is the case whenever the partial relations remain essentially the same as the

original relationship, regardless of whether the control variable is antecedent or intervening.

2. Explanation is the terminology used to identify a spurious relationship that can be shown

to be false because the partial relationships are greatly reduced or go away when the con-

trol variable is introduced. Additionally, the control or test variable can be shown to be

antecedent, preceding in a time order, compared to the original bivariate relationship.

3. Interpretation is the relationship in which the introduction of the control variable results in

a reduced partial relationship but unlike a spurious relationship, the time order has a dif-

ferent effect in that the control variable can be defined as intervening between the two

variables in the original relationship.

4. The final outcome is specification in which the partial tables are different in that one par-

tial relationship is strong (statistically significant in our simulation) and other partial rela-

tionship becomes insignificant or greatly reduced. Additionally, the third or test or control

variable can be either antecedent or intervening.

Our simulation of the Elaboration Model is essentially a data set that is a numerical

matrix programmed so that a specific mathematical model underlies the actual construction

of the simulation.4 The simulated data set software (also called the Elaboration Model) we

discuss in this article has a variety of underlining mathematical models built into it, such

that each expresses specific statistical properties designed to address specific heuristic

objectives. The statistical topics we have chosen (statistical significance, control, linearity,

etc.) are designed to cover topics commonly covered in introductory methodology and sta-

tistics books. The entire simulation also includes examples of replication, interpretation,

specification, and elaboration. The student is asked to read and evaluate a problem. The

assignment comes at a point in the course where the student has been introduced to sam-

pling, statistical significant versus substantive importance, and various statistical tests

and measures.

Outline of Student Assignment

The assignment is essentially a research problem that involves a description of a set of

events as well as the questions and instructions necessary to generate a codebook and




various other materials. For the purpose of this article, we will present only a subset of

the simulation designed to express only one aspect of the Elaboration Model.5 As we have

indicated, North Carolina State University (NCSU) uses a remote data access developed,

under the leadership of one of the authors, for graduate courses that do significantly more

computer analysis than do undergraduate courses. Moreover, these students are typically

local students, Remote Computing Network (RCN) takes pressure off of already crowded

student labs and finally avoids logistical and legal issues—pertaining to commercial statis-

tical software—specific to DE as well. Pedagogically, our initial findings appear to us that

the simulation of data analysis designed to define and describe the policy implications for

the outcome works very well for undergraduates.6 Although each computer lab assignment

varies slightly, this example will provide the reader with a sense of how the simulated data

set is employed.

A Research Problem to Analyze: School Board Policy

Let us assume that you are employed as a policy analyst by the County School District,

which has taken a simple random survey of all high school seniors in the district. The

County School Board is interested in answering some questions about the high school

seniors to allocate resources for programs, equipment, teachers, and so on during the

upcoming year. The survey data, along with the data from school records, have been com-

bined into a large data set. Assume that this database is on the computer that you will be

using.

You have been assigned the task of analyzing some of the data that have been collected

according to following four scenarios:

A. The School Board is interested in learning more about delinquency of high school seniors.

B. The School Board wants to know whether money should be spent to provide some type of

after-hours supervision for students to reduce delinquency.

C. Some critics of sex-education classes in high school have said that sex-education classes

have led to more pregnancies and abortions and have changed the attitudes of high school

seniors. The Board is interested in knowing whether sex education makes any differences

in attitudes or whether there are other influences on attitudes.

D. The Board is interested in the changing attitudes and the role of women in society.

Assuming that the underlying distribution of the variables is normal, answer the

following questions:

1. Specify the independent variable (IV) and dependent variable (DV) in this data set and

specify the reasons that lead you to this conclusion.

2. Using your knowledge of �2, interpret the tables and indicate whether the bivariate rela-

tionship is statistically significant. In your final answer, relate your statistics back to the

original hypothesis put forth by the researchers.

3. Reexamine the original relationship; is the relationship between IV and DV the same

across both levels of the control variable?




4. How would you describe this relationship with respect to the four categories of the

Elaboration Model outlined in class?

Answers Given to Students After Assignment is Turned In

After collecting the students’ online submissions, instructors post the following

responses to the course locker. Because of space limitations, we include a complete set

of responses for the first scenario (A) only. However, for the other scenarios (B, C, and

D), the Elaboration Model outputs and brief descriptions are provided to the reader.

(A.) The School Board wants to know whether money should be spent to provide some

type of after-hours supervision for students to reduce delinquency. Run another crosstab of

10 and 9. Next, run a crosstab of 10 and 9 with 8 as control.

(A.1) The IV is the parents’ work status (yes, no). The DV is delinquency (yes, no).

Among the reasons that this is the case is that the IV is the variable that is isolated as influ-

encing or producing changes in the DV. (In our discussion of IVs whose values are contin-

uous, we have stated that changes in the values of the IV produce or result in changes in the

values of the DV.) Obviously, the IV must precede the DV in a time-order sense. In the

above example, the father and mother working outside the home is assumed to be associated

with delinquency in that a strong agent in the socialization process is not available in the

home.

(A.2) The relationship between parents’ working and delinquency is statistically

significant (p < .002). There is an association between the variables indicated by a variety

of measures of association provided at the bottom of the printout (such as g ¼ .40). The

relationship expressed in Figure 2 supports the research hypothesis that

the children raised in homes where both the parents work are more likely to be

delinquent than those whose parents do not work (35.8% of the former vs. 19.1% of the lat-

ter). Remember, however, that in our class discussion, we underscored the multivariate

nature of most real-world relationships.

(A.3) The effect of introducing supervision (a variable that measures ‘‘Was the child ade-

quately supervised?’’) can be expressed as follows:

Examining the first table in Figure 3, we notice that for both the parents who did work

(yes) and those who did not work (no) and whose children were not adequately supervised,

there is no statistically significant relationship between both parent working and the delin-

quency variable—in other words, the categories are statistically independent. Note, how-

ever, that the delinquency percentages have increased vis-a-vis the original bivariate table.

Examining the second table in Figure 3, we notice that again there is no statistically sig-

nificant relationship between parent working and delinquency for those whose children

were adequately supervised. In other words, the two categories are statistically independent.

Note also that across both levels of the control variable, the bivariate measure of associa-

tion, g, is low (.14425 and .06250, respectively).

(A.4) The relationship between the working parent and delinquency is the same at both

levels of the control variable. For both levels of the control variable, the relationship is

insignificant by both measures of statistical significance (�2) and the measures of associa-

tion presented at the bottom of the page. In other words, the original relationship between




Figure 2

The Elaboration Model Crosstabulation Output

Figure 3

The Elaboration Model Crosstabulation With a Control Variable




parent working and incidence of delinquency observed in the bivariate table appears to be

‘‘explained away’’ by the introduction of the control variable supervision occurring in time.

This is clearly an example of explanation. The control variable supervision is an intervening

variable between the IV and DV. Moreover, the introduction of the control variable (third

variable) results in the elimination of the originally observed bivariate relationship across

both levels of the control variable indicating that it is the relationship between working

mothers and lack of supervision that produced the original bivariate relationship.

Other Examples of the Elaboration Simulation

(B.) The School Board is interested in learning more about the delinquency of high

school seniors. First, run a crosstab of variables X9 (did both parents work?) and X10 (was

respondent delinquent?). Second, using the crosstab of X10 and X9, introduce variable X1

(sex of the respondent) as a control.

(B.1) The following results will be obtained through the Elaboration Model assuming

students follow the directions appropriately:

The Elaboration Model Output

Independent variable: 9 (Did both parents work?)

Dependent variable: 10 (Was respondent delinquent?)

Raw �2 ¼ 12.45

Contingency coefficient ¼ 0.19962

Control variable: 1 (Sex of the respondent)

Yes: Raw �2 ¼ 4.59, contingency coefficient ¼ 0.12276

No: Raw �2 ¼ 8.34, contingency coefficient ¼ 0.16446

This is an example for replication. As already discussed, replication occurs whenever the

partial relationships are the same (same is defined in terms of statistical significance) as the

original bivariate relationship regardless of whether the test or control variable is antecedent

or intervening. In the above example, when variable 1 (sex of respondent) is introduced as a

control variable, the original relationship remains the same for all levels of the control vari-

able (i.e., the control variable has no effect).

(C.) Some critics of sex-education classes in high school have said that sex-education

classes have led to more pregnancies and abortions and have changed the attitudes of high

school seniors. The Board is interested in knowing whether sex education makes any

differences in attitudes or whether there are other influences on attitudes. First, run a cross-

tab of X13 (attitude on abortion?) and X12 (sex educational class?). Next, run a second

crosstab of X13 and X12 with X11 (is respondent Catholic?) as control.

(C.1) The following results will be obtained through the Elaboration Model assuming



Independent variable: 12 (Sex educational class?)

Dependent variable: 13 (Attitude on abortion?)

Raw �2 ¼ 7.4





Control variable: 11 (Is respondent Catholic?)



This is an example for explanation category in the Elaboration Model. Explanation is a

spurious relationship that may be ‘‘explained away’’ by the introduction of a third variable.

The control or test variable must be antecedent to both the IV and DV. The partial relation-

ship must be zero or significantly less (measured in terms of statistical significance) than

was found in the original relationship. Variables 12 (sex-education class) and 13 (attitude

on abortion) are designed to produce a statistically significant relationship. However, when

variable 11 (is respondent Catholic) is introduced, the relationship disappears for both

levels of the control variable. If these variables are to be relabeled, variable 11 must be ante-

cedent to both the IV and DV (variables 12 and 13).

(D.) The Board is interested in the changing attitudes and the role of women in

society. Run a crosstab of X6 and X1. Run a second crosstab of 6 and 1 with 7 as control.

(D.1). The following results will be obtained through the Elaboration Model assuming



Independent variable: 1 (Sex of respondent)

Dependent variable: 6 (Attitude on women’s rights)

Raw �2 ¼ 15.51


Control variable: 7 (Does mother have a BA degree?)



Variables X1 (sex of respondent) and X6 (attitude on women’s rights) are designed to

produce a statistically significant relationship. When variable X7 (Does mother have a

BA degree?) is introduced as a control, one level of the original relationship remains statis-

tically significant while the other disappears completely. This is an example of specifica-

tion. When the partial relationships differ significantly from each other, that is, when one

remains statistically significant and the other does not, the result is called specification.

In other words, we have specified the conditions under which the original relationship

occurs. Specification occurs whether the test variable is antecedent or intervening.

Conclusion

This article addressed major issues in teaching methodology. First, it allowed rich-media

solutions that is nimble and user-friendly to precede traditional notes on any topic in the

syllabus and, second, it used computer simulation software created by the authors, which

can be placed on a matriculated DE student’s desktop remotely, without the legal or logis-

tical problems of using more commercial software, for example, SPSS or SAS. In our view,

MediaSite is a product that works very well on Blackboard Vista online learning




management platform. The software offers the instructor flexibility to modify DE courses at

a much quicker pace and allows a feedback process as well. In the case of teaching concep-

tual analysis using a computer program to online students, the example we chose to present

is a subset of our software, a simulated data set designed to illustrate the conceptual

approach to contingent table analysis also called the Elaboration Model. The advantage

of our simulated data set, we believe, is that it contains a research context that enables the

instructor to instill the relationship of substance to method while using computer-based

materials to students online. The model that underlies the data is known to the instructor

and can be manipulated by the instructor for a variety of purposes—not the least of which

is producing a common standard for grading the computer-generated output. The exercise

itself, we believe, provides a real-world analog to the research process that involves all its

major components. Moreover, our impressionistic feedback of the process of using the data

set and student exercises has been very favorable. Students report enjoying the search for

the right answer and the feedback we provided in our answer sheets. Those who have made

the transition to real-world data report it less traumatic after having been exposed to our

data set. Finally, our data set provides a mechanism for integrating computer-based mate-

rials into the classroom that is both pedagogically sound and logistically manageable.

(Rosenberg, M. 1968)

Appendix

Codebook for Data Set

Description Values

1. Sex of respondent (0) Male

(1) Female

(99) Missing

2. Hair length in centimeters Exact value

(99) Missing

3. District math exam score exact value (99) Missing

4. Test anxiety score exact value (99) Missing

5. Final exam score math II exact value (99) Missing

6. Attitude on women’s rights (0) Disapprove

(1) Approve

(99) Missing

7. Does mother have a BA degree? (0) No BA degree

(1) Yes BA degree

(99) Missing

8. Was respondent supervised? (0) No

(1) Yes

(99) Missing

9. Did both parents work? (0) No

(1) Yes

(99) Missing

(continued)




Description Values

10. Was respondent delinquent? (0) No

(1) Yes

(99) Missing

11. Is respondent Catholic? (0) No

(1) Yes

(99) Missing

12. Sex-education class? (0) No

(1) Yes

(99) Missing

13. Attitude on abortion? (0) Disapprove

(1) Approve

(99) Missing

Notes

1. Our university has created a Remote Computing Network (RCN), which is a system through which

students, faculty, and staff can access lab computers remotely from their home, work, or laptops and use any

of the software that is available in walk-in classroom including, but not restricted to, SAS and SPSS.

However, for pedagogical and logistical reasons, we needed to find a solution for computer analysis that was

under our control and that would allow us to address IT problems by the end of the business day. The authors

of this article have used the RCN system to provide flexibility, access, and exposure to a wider range of applied

data sets for our face-to-face graduate students in methodology classes. However, our DE students who include

military stationed all over the world, we believed, needed a unique solution that had a high reliability, that was

under our control, and that did not require third party intervention to fix. In other words, that was highly nimble.

In addition, questions relating to our site licenses for the software for DE were also a consideration in not using

RCN. We address the pedagogical reasons for developing the Elaboration Model in the body of the article. For

more information on RCN, see http://www.chass.ncsu.edu/it/page.php?name¼index.

2. MediaSite is a media system designed to capture video of the instructions along with presentation slides

and automatically distribute the integrated rich-media presentations live over the Internet or archive them to

servers for later use. In other words, MediaSite allows professors to simply teach and stream their courses to

online students using a rack-mounted unit for permanent in-room installation or a portable unit for multilocation

(including classrooms, labs, and conferences) presentations. MediaSite recorder has capability to integrate

multiple inputs for video, audio, and digital signals—such images as output from a computer to show slides,

documents, and course content. The MediaSite system relies on Windows Media Server and captures the record-

ing as Windows Media file that could be watched through standard web browsers. The recorder configuration

submenus also include options to configure image compression for various quality levels with different

templates for dial-up or broadband users. The presentation sessions can be saved on CDs, DVDs, or USBs using

‘‘Publish-to-Go’’ function, which is significantly useful to distribute the course contents to DE students pursuing

their higher education. Overall, MediaSite is not only a user-friendly, time-saving, and low-cost rich-media

creation system but also gives DE students more flexibility in managing their time (Lipschutz, 2004), provides

interactive features of navigating, and replays particular lectures or presentations.

3. Actual software is available via the authors, which includes instructions pertaining to software installation

on students’ computers. If interested, contact Dr. Michael L. Vasu or Dr. Ali O. Oztrurk at [email protected]

or [email protected]. The total cost of the Elaboration software will be determined in the future.

4. An example will help illustrate this point. Our data set contains a correlation and linear regression module

in which the student is asked to use the Elaboration Model software to examine the linear relationship between

an IV and a DV. The original relationship has a very high positive correlation (r ¼ .89). The student is then




instructed to control for a third variable and to run separate scatterplots for two subgroups that comprise the third

variable. The examination of these separate scatterplots reveals that, in fact, no real linear relationship exists

between the IV and DV and that the observed relationship was a simple artifact of failing to control for this third

variable. The example exposes students to scattergrams, linearity, the concept of controlling a variable as well as

requiring them to use the computer. The data set currently has an N of 300 and variables that provide the basis

for a conceptual context or setting.

5. What the model is written in and how we post it to the end user. In addition to the issues raised previously

about the logistics and possible legal constraints, one specific question that any potential reader of this article

may ask is—why would one simulate a data set with all the real data available? The answer is that outside var-

ious natural science domains, and particularly in fields such as political science, sociology, and education and

psychology, real world of data often do not meet the heuristic needs of the instructor. (For example, a real-world

data set may contain an excellent example of second-order control variable, yet no examples of outliers.

Moreover, the use of a simulated data set allows the instructor to construct a very specific example of a statistical

concept—such as the effect of controlling for a third variable on an existing bivariate relationship—in a fashion

that may appear very unlikely when compared to a similar result using real-world data. One example is the abil-

ity to produce an outcome in which the introduction of a third variable clearly—and in a highly statistically sig-

nificant sense—eliminates the original bivariate relationship. We believe this approach instills a solid

conceptual framework for data analysis.

6. In addition, our data set has been designed to be incorporated into the chapters of a commonly used meth-

odology text and possesses the flexibility to be modified (e.g., the variable labels) to use examples from other

textbooks. We have found that students initially exposed to our data sets, in which the variables and values

express heuristically interesting (although simulated) relationships, find the transition to real data less traumatic.

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Michael L. Vasu, PhD, is a Lifetime Edelman Laureate, awarded by the Institute for Operations Research and

Management Sciences (INFORMS). He is a member of the Graduate Faculty at the School of Public and

International Affairs. He is the author of Politics and Planning and the co-author of Organizational

Behavior and Public Management and Effective Program Practices For At-Risk Youth: A Continuum of

Community – Based Programs. He has written 20 technical paper and monographs, and over 30 peer-

reviewed articles. He is the former President of the Southern Association for Public Opinion Research.

Ali O. Ozturk, PhD, is the Director of Leadership in the Public Sector Online B.A. degree completion program

at the School of Public and International Affairs, North Carolina State University. He is also teaching leadership

and methodology courses as an assistant teaching professor in LPS program. He received his PhD in public

administration from School of Public and International Affairs in 2005. His research and teaching interests

include contemporary public management, public sector leadership, public sector organizational culture,

e-government, and survey research. His e-mail is [email protected]




Teaching Abstract statistics in Online Education

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