Psychological Sciences Wins Graduate School Thesis Award
Elisabeth “Liz” Pyburn, a former student in the Psychological Sciences M.A. program, received the inaugural 2016 Graduate School Outstanding Thesis Award for the STEM, Health, and Behavioral Studies for her project entitled “Persons Can Speak Louder Than Variables: Person-Centered Analysis and the Prediction of Student Success.”
In the thesis, which was chaired by Prof. S. Jeanne Horst of the Dept. of Graduate Psychology, Liz cited a prominent educational researcher’s call for “methodological synergy” – the need to build synergy between rigorous statistical methodology and applied higher education research. Liz carefully examined two person-centered statistical methods – cluster analysis and mixture modeling – and applied them to a higher education research question. She demonstrated that by applying both methods to motivation and help-seeking data from entering first-year students, she could identify patterns of student goal orientation and help-seeking that were predictive of their later academic success. The work lent support for goal orientation theory and for patterns of motivation and help-seeking that appear to be adaptive for students. Not only did her work clearly compare and contrast the two statistical methods, but provided useful theory-based implications for educators.
Liz is now a PhD student in the Assessment & Measurement program at JMU. The Outstanding Thesis Award was recognized at The Graduate School Awards Reception by the dean of the graduate school, Dr. Jie Chen on Sunday, April 17th in the Madison Union Ballroom.
Thesis Abstract: In order to ensure that analyses are appropriate for one’s research question(s), it is important to consider whether a person-centered or variable-centered approach is needed. Person-centered approaches are often not considered in situations for which they would be appropriate. To that end, a description of the characteristics and procedures of two common person-centered analyses (cluster analysis and mixture modeling) are provided. Although both analyses accomplish the same general aim – to group persons based on their similarity on a series of variables, thus providing ease of interpretation – the methods employed for each analysis differ considerably. As illustration, both analyses were applied to a sample of student data. Scores on six measures, collected during a university-wide assessment day, were used to group students via cluster analysis and mixture modeling – mastery approach, performance approach, and performance avoidance goal orientations; work avoidance; and two help-seeking orientations. Profiles were then compared to identify similarities and differences between analysis solutions. Predictive utility of the profiles was also assessed by entering them into a regression predicting GPA. Both analyses resulted in three groups for their final solutions, based on decision criteria considered best practice for each analysis. Groupings were supported by validity evidence. Patterns of means between the cluster analysis and mixture modeling profiles were similar in terms of overall ranking and cluster-to-class assignment; however, qualitative differences among the profiles were also identified. Specifically, the mixture modeling classes did not differ very much on work avoidance and the two help-seeking variables, whereas the cluster analysis classes did. Cluster and class sizes were also discrepant, with Class 3 consisting of many more students than any of the other clusters or classes. Regression analyses indicated that neither the clusters nor the classes meaningfully predicted GPA. Researchers should consider person-centered analyses if their research questions so dictate; however, the different processes employed in mixture modeling and cluster analysis require that researchers also consider which analysis is most appropriate for their needs. Prior hypotheses regarding population and/or sample structure should also be considered.