Involving Online Doctoral Students in the Journal Publication Process - A Qualitative Research Study...

SAGE Research Methods Cases

Involving Online Doctoral Studentsin the Journal Publication Process:

A Qualitative Research Study

Contributors: Claretha HughesBook Title: SAGE Research Methods CasesChapter Title: "Involving Online Doctoral Students in the Journal Publication Process: AQualitative Research Study"Pub. Date: 2014Access Date: August 04, 2014Publishing Company: SAGE Publications, Ltd.City: LondonPrint ISBN:Online ISBN: 9781446273050

DOI: http://dx.doi.org/10.4135/978144627305014534156©2014 SAGE Publications, Inc. All Rights Reserved.

This PDF has been generated from SAGE Research Methods. Please note that thepagination of the online version will vary from the pagination of the print book.

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©2014 SAGE Publications, Inc. All Rights Reserved. SAGE Research Methods

of 22 SAGE Research Methods Cases: Involving OnlineDoctoral Students in the Journal Publication

Process: A Qualitative Research Study

http://dx.doi.org/10.4135/978144627305014534156

Involving Online Doctoral Students in theJournal Publication Process: A QualitativeResearch Study

This case study provides an account of a 3-year research project that examinedexpectations of online graduate students. The two goals of this case are to exploreopportunities and challenges of engaging online doctoral students as student co-researchers in the journal publication process and to describe how to conductqualitative research using transcript analysis. The case sheds light on the particularcomplexities of conducting and completing research with doctoral students whocomplete all their coursework online. Situational and communication problems wereexposed. The student co-researchers were asked to focus on the study's literaturereview because this is an area with which doctoral students are known to struggleand where they can contribute within their research experience and study and timeconstraints. Four key practical lessons were learned as a result of this project: (1) Readthe literature and be proficient in the method that you are using to write a literaturereview and/or analyze data, (2) Be patient with doctoral students even if they aresupposed to be the best students, (3) Distance does not have to be a problem withonline students, and (4) Learning occurs through participation.

Learning Outcomes

By the end of the case study, you should

• Understand the role of relationship building between doctoral students andfaculty conducting a research study

• Be able to examine the role of communication and accuracy of referencesprovided during the research process

• Be able to assess the pros and cons of conducting a research study withonline doctoral students

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One of the key considerations before beginning a research study is to determinethe method that will be used. Although I wanted to determine the before and afterconsiderations of students' expectations, the design of the study did not warrant aquantitative analysis. Typically, quantitative studies examine pre- and post-studyresults. In this case, the study was designed to determine before and after perceptionsof students from open-ended, self-reported responses. However, I intended to use theinformation obtained to develop the foundation for analysis using quantitative analysis ofa larger population of students.

In 2010, I embarked on a research study to examine, for the first time, what theexpectations and expected contributions were for online graduate students uponentry to the online classroom conducted through the Blackboard virtual learningenvironment. Online learning is relatively new to many students and faculty, and thereis no developed knowledge base that examines the expectations of students uponentry to an online course. Previous studies have examined the resources needed bystudents and faculty, but no study had directly examined the expectations that studentsbring within them to the course environment. My study looked at the responses thatwere captured within an Expectations Discussion Board forum upon students' entryto the course. Graduate students were asked to respond to questions regarding theirexpectations of courses in which they were enrolled.

When I embarked on this study in 2010, I had no intention of involving doctoral studentsin this research project as student co-researchers. The theoretical framework was on atopic that I was already familiar with and was essentially a follow-up study to results thatemerged from my dissertation work in 2002. Our online doctoral students usually workfull-time and live remotely from the Midwestern United States institution, and many arefrom other states. In this project, I worked with two doctoral students as co-researchers:one who worked on campus and one who was out of state. The student co-researcherswere in courses where there was a research paper requirement and their paper topicinterests were close to the topic of my research study.

I asked these student co-researchers whether they were interested in writing literaturereviews on the theoretical framework topic for my research study. The student co-researchers were not in contact with each other during the process. The student co-researchers' participation offered the following benefits to them:



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• 1. They participated in an official research project.• 2. They learned how to write a literature review using correct American

Psychological Association (APA) guidelines.• 3. They learned about theoretical frameworks and how foundational the

theoretical framework is to a research project.• 4. They learned to write an Institutional Review Board (IRB) protocol.• 5. They gained recognition through an official journal publication.

Project Overview and Context

This research project evolved from my experiences teaching online graduate studentsand a finding from my dissertation related to the met-expectations hypothesis. Thisfinding showed that regardless of how much communication was provided to potentialparticipants in a training workshop, the participants still came to the workshops withtheir own expectations. I wanted to see whether graduate students in my courses cameto online classes with their own expectations of the courses, and if so, what were thoseexpectations? I also wanted to know whether the students' expectations change afterhaving been in the class for 6–8 weeks. In addition, I wanted to know what the onlinegraduate students expected to contribute to the course.

Faculty typically have expectations for students as expressed within the syllabus forthe course, but faculty usually do not know what students' expectations of the courseis or what students expect to contribute during the course. The data were collected inBlackboard discussion boards on expectations. The nature of the data collection did notallow for the two student co-researchers to engage in the data collection process, andthey did not participate in the data analysis portion of the project because of locationand time constraints.

Research Practicalities

This journal publication research project was conducted between fall semester 2010and fall semester 2013. The online graduate student responses were captured inBlackboard discussion board forums titled Expectations Discussion Board. It was



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optional for students in my courses to participate in the discussion board, and no pointswere awarded for participation. The participant responses were captured from 13different sections of seven different master's- and doctoral-level asynchronous onlinecourses. The Expectations Discussion Board for each course section was availableto students upon entry into the course, and I did not ask the students whether or notthey had read the syllabus, the assignment summary, or the course description prior toresponding to the questions.

The literature review section was essential to this project because it had been 11 yearssince I had thoroughly examined the literature related to expectancy theory and met-expectations hypothesis. When I met with the first doctoral student to participate inthis project as a student researcher, we spent over 1 hour simply discussing whetheror not the doctoral student was interested in working on this project as a researchpracticum. This student was a second-year doctoral student who had not participated inany prior research activities. After an extensive discussion of the project, we discussedthe literature review topic for another hour. During this time, I did a simple GoogleScholar search for met-expectations hypothesis and expectancy theory. Articles thatwere familiar to me came up in the search. I printed some of them for the student and e-mailed several of the articles to the student, and I asked the student to read them. Afterreading the initial articles, I asked the student to find similar articles related to usingexpectancy theory in online learning and write a literature review. The student's initialarticle count was five potential articles upon which the student could begin writing thereview. During this meeting, we also talked about writing the IRB application for thestudy. After the meeting, I completed the application for IRB approval and sent it to thestudent through e-mail. The student was to sign the document and deliver it to the IRBoffice on campus for review.

While awaiting approval of the IRB, I spoke with another potential student co-researcher, an online doctoral student who was taking a course from me in which thestudent was to write a research paper. When the student sent his topic proposal, it wassimilar to the current study's topic, so I offered the student an opportunity to work onthis project as a student co-researcher. I called the IRB office and was told that thestudent's name could be added to the IRB protocol since the protocol had not yet beenprocessed. I sent the signature page to the student to be completed and then sent italong to the IRB. The second student co-researcher was a third-year doctoral student



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who also had no prior research experience. The student was provided with the samefive potential articles and the same topic, expectancy theory in online learning, andwas asked to write a literature review. Both reviews were completed during the springsemester of 2013. The literature reviews were concise but not comprehensive. I did notexpect perfection of the reviews from these two student co-researchers because of theirinexperience with the research process. However, I did want to see how their reviewsrelated to each other and whether they had found additional articles that were related tothe theoretical framework.

After we received the IRB approval of exempt for our study, I began to extract thetranscript data from the Blackboard discussion boards and place the data into aMicrosoft Excel spreadsheet. Exemption from IRB approval meant that the research didnot pose risk to potential participants, so I could proceed with the study without havingthe IRB board review the study. During this process, all identification related to thecourse students who submitted the responses was removed to protect the anonymity ofparticipants. Removing the data from Blackboard and placing it in Excel was completedbefore the end of spring semester 2013. No analysis or specific ordering of the datawas done until the 2013 fall semester. At the beginning of the fall semester, I combinedthe literature documents from the two student co-researchers into one literature reviewfile and printed it to be read. I had read them independently during the 2013 springsemester, but I wanted to see how much overlap in information there was. Prior toreading their work, I learned of an opportunity to submit a proposal for a TeachingResearch Grant offered by the teaching center on our campus. While writing the grantproposal, I essentially had written the introduction to our journal article, so I includedthat into the combined literature review and the formation of the article began.

As I read the student co-researchers' work, I began organizing and reorganizing wherethe content they provided actually fit or not did not fit within the document. I did notthrow out the content that was not usable for this particular article; rather, I placed it ina separate Microsoft Word file for possible future use. Some of the content providedby the student co-researchers could be included in the introduction, the literaturereview, the discussion, and the conclusion sections of the article. I did not segregatewhich student co-researcher provided what specific content as I was organizing thearticle. I organized based on relevance to the study. While writing the document, Idiscovered that one of the student co-researchers had several citations listed with no



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references. Although I should have caught this during the first read during the springsemester, at that time, I was examining more for content than for listing of references,and I knew I would read the work again. There was also misspelling of authors' namesin the citations and/or the reference listing, which is difficult to check without pullingall the articles and rereading them. At this point, I sent the work back to the studentco-researcher and asked the student co-researcher to verify and update all of thereferences in the work. The student co-researcher did update the document with thecorrections. At this point, because the article was in progress, I did not remerge thecontent but updated the references and the specific citations that were of relevance tothe article.

At the discovery of this level of errors within the student's work, I asked another facultymember whose research area was related to the article to join as a fourth author on thearticle by proofreading and providing technical content analysis. This provided additionalexpertise to speed up the process for completion of the article.

One critical point here for doctoral student learning is that all doctoral students, whetheror not they are serving as student co-researchers on a project, need to be as thoroughas possible when submitting research work, especially when it comes to citing andreferencing other researchers' work. Misspelling an author's name and not referencingor citing works properly is a major weakness and is not tolerated in the academiccommunity of scholars to which all academic researchers belong. Once a facultymember cannot trust the work that a student submits, it is difficult for the faculty memberto continue working with the student on research projects. In this case, the studentco-researcher did not plagiarize, but proper care was not exhibited in how the work ofothers was presented. Once given the opportunity to correct the errors, the studentdid a much better job, but faculty members expect graduate students' best work ontheir first attempt, especially when feedback has been provided during the initial writingprocess.

Organization of the data and preparing it for analysis in NVivo 10 software took themost time because I had never used NVivo before. In the past, I had used a similarproduct from QSR (the company that makes NVivo) called QSR NUD*IST and havea substantial amount of software experience including Crystal Reports, which is thereport writing software that is used within NVivo 10. This experience made the learning



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curve less steep, but it took time to get up to speed. I did reduce the time by sorting andorganizing the data in Excel spreadsheets so that they could be easily imported intoNVivo.

The Research Process

In addition to the practicalities of involving graduate student co-researchers in thejournal publication process of my research on graduate students' course expectations,the process of gathering and analyzing data for this project also presented some issues.Overall, the main issues with this qualitative research project are summarized in thefollowing subsections:

The Population and Sampling

Finding the representative group of participants who had credible and meaningfulinformation to contribute to this study was not difficult; the concern was to remainobjective and allow the course students to participate willingly in the process. Whenthe idea for the Expectations Discussion Board was initially developed, it was based onprevious research and my own curiosity about the students in my courses. At the startof this study, I had been teaching online for 8 years and 4 years at this institution. I hadnot ever thought about using data captured in my courses as part of a research studyand did not know how to do it. I spoke with the IRB director and asked whether it werepossible and was told that I could use data collected during normal class procedures if Iprotected the identity of participants.

Discussion Board Design

The discussion board for each course, titled Expectations Discussion Board, wasdesigned as a forum. The students in each of my courses were asked to respond to twoquestions upon initial entry to the course:



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• 1. What are your expectations of this course? (i.e. Why did you enroll in thiscourse and what do you expect to learn?)

• 2. What do you expect to contribute during this course?

The following two questions were asked about mid-way through the course to reassessthe students' course expectations within the same Expectations Discussion Boardforum:

• 1. Has your expectations changed since you wrote your initial expectations?• 2. If so, how and in what way(s)?

The students had the opportunity to review their previous postings. They also had theopportunity to read and respond to other students' postings. I did not communicate withstudents regarding whether or not they should respond to other students' postings, butsome of the responses revealed that some of them did. There were no grades awardedfor answering the questions and no penalization for not answering the questions. Thefirst contact was August 2010, and the last contact for purposes of this study wasDecember 2012.

Research Design

The research data for this study were transcript analyses. The open-ended questionsin the study provided the participating students with the opportunity to make generalcomments, resulting in a variety of suggestions, comments, complaints, andcompliments related to course expectations. Because types of data can determinea study's research design or method, I determined that a qualitative, multi-case,descriptive study was the best way to analyze the data collected in this project. Findingthe students' expectations required that I ask the students and wait for them to tell me.This study is qualitative because of the use of open-ended questions and the transcriptsof the responses. The study is a multi-case study because each student was unique tothe course and had their unique expectations. The study is also descriptive because Iused a stacked matrix that allowed me to quantify some aspects of the qualitative datasuch as the percentage of students who agree to the same theme that emerged fromthe responses. A stacked matrix is a way to report the result of multiple case analyses



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in qualitative research (Miles, Huberman, & Saldaña, 2013). Examples of StackedMatrices are shown in Tables 1 and 2.

Table 1. Participants and themes.

Table 2. Comparison of interview results.



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Qualitative Data and Met-ExpectationsHypothesis

Understanding how data are collected and analyzed within a research study is essentialto successful analysis of findings and subsequently making recommendations for futureresearch. The student co-researcher did not understand Mitchell's or Vroom's workbecause they had not specifically studied these two authors nor had they heard ofthem when I asked them. Without the student co-researchers having said knowledge,it would have required extensive time to explain the data analysis relationship to themfor their comprehensive participation in this aspect of the study and meet the publicationdeadline. Therefore, I chose not to involve them in the data analysis portion of thestudy.

I used research techniques developed and described by Mitchell (1974) and Vroom(1964). Understanding some of the classic literature about a method and how it hasbeen applied provides a rich foundation for a research study because all methodshave pros and cons. Mitchell (1974) developed expectancy theory surveys using ‘thesubject's own outcomes’ to provide more reliability and validity to the study. Vroom's(1964) theory is based on a within-subjects approach, so the subject should be askedto list his or her own outcomes, especially in settings where the researcher may haveno control over the outcomes (Mitchell, 1974). Two limitations to this approach are thatthe list of outcomes could be extensive and thus need to be reduced by the researcher,and participants may not list negative outcomes, which could be important to the study.In my met-expectation study with course students, I had some control by providingthe same questions to all students. The list of expectations was extensive because 85students responded, so NVivo 10 software was used to thoroughly examine all of thetranscripts. The extent to which course students provided negative information was notthe intent of this particular study, so all responses were examined. Another concernabout methodological difficulties in previous studies on met expectations was providedby Irving and Meyer (1994) who suggested that there are serious problems with the useof difference scores, direct measures, and measures that collapse across constructs.



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As a result, I took measures (i.e. responses to questions on each course's ExpectationsDiscussion Board) at two points: upon entry and between 6 and 8 weeks of the course.

A learning outcome for students involved as student co-researchers in research projectsis that they should examine the literature to learn about the pros and cons of anychosen method of collecting and analyzing data to improve the validity and reliabilityof their research work. Knowing what has been tried and the limitations of previousattempts can help improve the design of the research.

Authenticating the Research

There are several methods involved in authenticating qualitative research during thedata collection and analysis process including member checking, triangulation ofdata, grounded theory, and avoidance of researcher bias. Authentication of researchis similar to validity and reliability in quantitative research. In my met-expectationsproject, I used several measures of authenticity to ensure that the data collection andanalysis provided reliability and validity of results (Merriam, 1998; Patton, 2008). Alldata collection was administered the same way using the same questions in each ofthe 13 sections of the seven different online courses in a designated ExpectationsDiscussion Board within each course.

I controlled for researcher bias by not responding to any of the expectations postings ofstudents unless during member checking there was an obvious disconnect regardingthe course content and the student's expectation that was perceived to have animmediate impact on the student's presence within the course. In an attempt to preventthis from happening at all, I provided a discussion board forum for students to introducethemselves to others within the course. If it was determined in the introduction forumthat the student had enrolled in the wrong course, the researcher informed the studentthat he or she was in the wrong course. The student may or may not have responded tothe questions in the expectations forum before I discovered that the student was in thewrong course. Otherwise, I did not offer any input to students within the ExpectationsDiscussion Board forum.



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I did provide feedback to students who reassessed their expectations at the 6- to 8-week time period of the course to acknowledge the student's posting and progress orlack thereof. Students were not informed in advanced that I would provide feedback ifthey reassessed their expectations.

Member Checking

Member checking was done if a student posted information that was beyond the scopeof the course or showed obvious confusion. Member checking was also done duringthe transcript analysis phase because the data were not examined for themes duringthe course and respondents provided a variety of information within responses to eachquestion. Portions of responses that were general in nature and did not answer thespecific questions were discarded.

Triangulation of Data

Denzin (1978) defined triangulation as ‘the combination of methodologies in the studyof the same phenomenon’ (p. 291). Triangulation is used to provide researchers moreconfidence in the results of a study (Jick, 1979). The effectiveness of triangulation isbased on the ‘premise that the weaknesses in each single method will be compensatedby the counter-balancing strengths of another … that multiple and independentmeasures do not share the same weaknesses or potential for bias’ (Rohner, 1977, p.134).

I achieved triangulation in this study because the data were gathered separately fromdifferent groups of students: specifically, 13 different groups of students in sevendifferent master's or doctoral courses. The different groups of students producedcomparable outcomes over a 2-year span of time, and the different courses producedcomparable results using comparable approaches of data gathering. The within-methods comparisons were also consistent, and there was consistency in between-methods comparisons (Merriam, 1998; Patton, 2008). Comparisons were doneby comparing the group of master's students' responses to the group of doctoralstudents' responses and by comparing the groups of students within different sections



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of the same course. Patterns were revealed across the courses (Patton, 2008).The participants reported similar thoughts and used similar wording in their writtenresponses, producing consistency across sources (Patton, 2008).

Transcript Analysis

This is not my first time examining transcripts, but my experience had been withinterview transcripts where I had personally interviewed each participant, hadtranscribed each recorded interview, and then coded the data. The transcript analysisfor this met-expectations study was a little different because I never talked to theparticipants face-to-face. It reduced researcher bias but made it a little more difficult torelate the context of the participant experience and why participants responded the waythey did. Essentially, no probing or extending of meaning was done.

The primary sources of data were the graduate students' responses in the form oftranscripts from the Expectations Discussion Boards. To make sense of the data, Iunitized and categorized the postings from the online Expectations Discussion Boardsto determine study participants and the coding scheme.

Study Participants

Understanding of who the participants are within a study is essential because qualitativeresearchers want to include participants who meet the study criteria and can provide theinformation (data) for the phenomena being studied. The actual participants are usuallya sample of the possible participants because all possible participants have the right tochoose not to participate in the study for various reasons. Some reasons may includelack of interest and lack of trust related to anonymity. Often, the researcher does notknow why possible participants choose not to participate in a research study. There isnot an empirically identified right or wrong number of participants in a qualitative study(Mason, 2010).

In my study of students' course expectations, there were 103 possible participantsbased on enrollment for the 13 sections of seven different courses. Of those 103



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possible participants, there was a total of 85 responses or 82.52% response rate tothe expectations of course question, 77 responses or 74.75% response rate to theexpected contribution to course question, and 12 responses or 14.11% response rate tothe expectation changes question. The expected changes percentage is based on the85 participants who provided a response to the initial expectations question. There were48 possible master's and 55 possible doctoral respondents with a 95.83% response rateto the expectations of the course for the master's students and 70.91% response ratefor doctoral students. The master's students' response rate on the expected contributionto the course was 89.58%, and the doctoral students' rate was 61.82%. The master'sstudents' response rate when asked to reassess expectations was 0%, and the doctoralstudents' response rate was 30.77%. For further anonymity of participants, I coded theacademic term and course numbers.

I unitized the data by separating the participants' online postings from other postingswithin the forums such as feedback from faculty or peers. I analyzed and assessedeach posting based on whether the participants were in doctoral or master's coursesand into the following categories: expectation of course, expected contribution tocourse, and expectation changes. Once the data were placed into the three categoriesfor both the master's and doctoral students in Excel spreadsheets, I discovered that nomaster's students had responded to the expectation changes questions; therefore, threespreadsheets for doctoral students and two spreadsheets for master's students wereimported into NVivo 10 software. The data were then coded into five nodes to begincoding of the responses into thematic categories. A total of 397 messages were coded,and up to eight initial themes emerged within the five different categories of responses.The number of responses to each category needed to be further assessed to determineconsistency and accuracy of the emerging themes (Gunawardena, Lowe, & Anderson,1997); thus, grounded theory was conducted.

Grounded Theory

I used grounded theory data analysis methods (Strauss & Corbin, 1994) to investigatethe revealed patterns of interaction between and among the participant responses.Grounded theory provided constant comparison and analytic meaning to investigateexpectations of graduate students in the online environment. I reassessed and then



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recategorized the postings. When it was determined that new categories for the onlinepostings were exhausted, the constant comparative method (Glaser & Strauss, 1967)was used to develop rules that defined which postings were included or excluded fromeach category, and a new coding sheet was developed.

I conducted many revisions, modifications, and amendments to the data coding untilall the postings were placed into appropriate categories and further analysis did notprovide new information or insights. During the constant comparison process, themeswere confirmed or discarded based on the transcript analysis. Some themes neededto be combined because the transcripts revealed similar meanings, and other themeswere discarded because there was not enough support for the theme. Sometimesparticipants will provide multiple responses related to a particular theme, so thepercentages must be based on unique participants regardless of whether or not theyprovided multiple responses.

Qualitative Multi-Case Analysis: PracticalLessons Learned

Before you conduct any research data gathering, it is vital that you read themethodological literature so that you will clearly understand and address all therequirements needed to successfully implement and report your study's finding usingthe chosen method. You must also be very proficient with the use of software beforeusing it to analyze data because inputting the data incorrectly can produce inaccurateresults. You must also be able to interpret the results provided by the software program.Having experience of manually analyzing the data helps you to know what you shouldbe looking for within the software. Although you still must code the data and think aboutit the same way, the computer allows you to conduct the analysis at a quicker pace.

Practical tips that I would like to provide to researchers, including researchers who maywish to involve graduate student as co-researchers, are as follows:

• 1. Read the literature and be proficient in the method or technique that youare using to write a literature review or analyze data. When involving doctoral



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students as co-researchers in your research, start them off with literaturewith which you are familiar. This will allow you to better assess the work theysubmit from a content perspective. My main aim was to have the student co-researchers duplicate the findings that I had achieved from the literature 11years earlier and to see whether they would find new literature that enhancedmy findings. They did find new research that enhanced the work. I alsoread the works that they found that was most pertinent to this study. I foundadditional articles that they had not found.

• 2. Be patient with doctoral students, even if they are supposed to be the beststudents. When you are under publishing constraints, it is best to involvethe student co-researchers as early in the process as possible. I was notunder a publishing constraint when I first asked my student co-researchersto participate, so I was able to have them complete the work in plenty oftime before I needed it. The concern was that I did not assess the workas thoroughly upfront because I trusted that the student co-researchershad submitted their best work. This was not the case, and so for practicalpurposes, my suggestion is to be highly thorough in examining the studentco-researchers' works upfront so that when the time constraint does occur,you will know that you have quality piece of work that is not in need ofnumerous corrections.

• 3. Distance does not have to be a problem with online students. It can be aproblem to conduct research with doctoral students who you do not see face-to-face, but it need not be. If you give student co-researchers work to do attheir level of proficiency, it can work. Writing a literature review on one or twotopics can be accomplished by doctoral students. Student co-researcherscan also proofread the final project for APA formatting accuracy and provideclarity to content they have submitted within the project.

• 4. Learning occurs through participation. Although it is not clear how muchparticipation in research a doctoral student needs to become proficient inresearch, participation must begin somewhere for them to learn how to doit. I believe that being involved as student co-researchers inspires studentsto read the study and to engage in how their work added to the successof the project. I did not know where or even if I could use my student co-researchers' results, but I did include some of their work throughout thearticle where pertinent references were needed. The student co-researchersdid not interact with the data at all in this particular case; however, had time



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allowed, I would have involved them more. They were able to read the finaljournal publication and see how the literature review related to each partof the article: from the introduction to the conclusion to the implications forfuture research.

Conclusion

I teach online and very seldom do I have the opportunity to meet any of my students.Understanding their expectations is essential to developing and maintaining a positiveonline learning environment. Involving online doctoral students as co-researchers in theresearch process before their dissertation is important so that they learn how to conductresearch and report findings. This study allowed me to accomplish both tasks. I learnedabout what my graduate students' expectations of courses are and what they expect tocontribute when they enter an online course. I also learned how to be patient with andnot expect too much from online doctoral students who were involved as co-researchersin the writing process. They will make mistakes and provide us with teachable moments.I believe this is what the research process is all about.

We learn from our attempts to collect and analyze data we believe to be valuable tothe research knowledge base and the learning community within which we operate.Mentoring online doctoral students can be extremely difficult when you never see them,but through small research experiences such as those provided in this case study,communication can be established and research skills can be developed. I know thatmy student co-researchers learned new research concepts that they will be familiarwith as they begin work on their dissertations; regardless of the research method theychoose, they will need to write a literature review.

Exercises and Discussion Questions

• 1. What are some measures of authenticating qualitative data?• 2. How does a stacked matrix allow for the interaction of qualitative and

quantitative data analysis?



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• 3. This case touched on teaching online doctoral students to engage in theresearch process. What are some of the benefits and limitations associatedwith working with online doctoral students?

• 4. How was triangulation of data accomplished in this study?• 5. Why is a relationship of trust essential in the collaboration of research

process?

Further Reading

Paechter, M., Maier, B., & Macher, D. (2010). Students' expectations of andexperiences in e-learning: Their relation to learning achievements and coursesatisfaction. Computers & Education, 54, 222–229. doi: http://dx.doi.org/10.1016/j.compedu.2009.08.005

Porter, L. W., & Steers, R. M. (1973). Organizational, work, and personal factors inemployee turnover and absenteeism. Psychological Bulletin, 80, 151–176. http://dx.doi.org/10.1037/h0034829

Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw,S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review ofEducational Research, 76, 93–135. doi: http://dx.doi.org/10.3102/00346543076001093http://dx.doi.org/10.3102/00346543076001093

References

Denzin, N. K. (1978). The research act (2nd ed.). New York, NY: McGraw-Hill.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies forqualitative research. Chicago, IL: Aldine/Atherton.

Gunawardena, L., Lowe, C., & Anderson, T. (1997). Interaction analysis of a globalon-line debate and the development of a constructivist interaction analysis model for



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http://dx.doi.org/10.3102/00346543076001093

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http://dx.doi.org/10.2190/7MQV-X9UJ-C7Q3-NRAG

http://dx.doi.org/10.1037/0021-9010.79.6.937

http://dx.doi.org/10.1037/0021-9010.79.6.937

http://dx.doi.org/10.1037/0021-9010.79.6.937

http://dx.doi.org/10.2307/2392366

http://nbn-resolving.de/urn:nbn:de:0114-fqs100387

http://nbn-resolving.de/urn:nbn:de:0114-fqs100387

http://dx.doi.org/10.1037/h0037495

http://dx.doi.org/10.1037/h0037495

http://dx.doi.org/10.1177/106939717701200203

SAGE AUTHORS




http://dx.doi.org/10.4135/978144627305014534156



http://dx.doi.org/10.4135/978144627305014534156

Involving Online Doctoral Students in the Journal Publication Process - A Qualitative Research Study...

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