Mitigating the Occurrences of Graduate Student Attrition: Making the Case for Standardized Noncognitive Assessments
Type of DegreePhD Dissertation
Education Foundation, Leadership, and Technology
Restriction TypeAuburn University Users
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The purpose of this dissertation study was twofold. First, this research study examined the role of noncognitive factors on degree completion, academic achievement, and persistence. Second, this research study examined the utility of the Personal Potential Index (PPI) as an avenue to reliably assess noncognitive factors of graduate students that avoids the drawbacks associated with self-assessment methods and unstandardized recommendation letters. The current study utilized a nonexperimental quantitative research design using de-identified, archival, PPI survey data of graduate student applicants (n = 362) obtained from Educational Testing Service (ETS). The PPI requires raters to evaluate applicants on six noncognitive attributes including: knowledge and creativity, communication skills, teamwork, resilience, planning and organization, and ethics and integrity (Kyllonen, 2008). The enrolled graduate applicant sample (n = 112) was used to determine if noncognitive scores predicted degree completion status, graduate GPA, and time-to-degree completion. The researcher also examined group differences and the presence of floor and ceiling effects for the PPI scales. Results indicated that the PPI scales alone were not significant predictors of degree completion, graduate GPA, and time-to-degree. To explore the data further, hierarchical multiple logistic regression analyses and hierarchical multiple regression analyses were conducted to determine if the PPI scales predicted graduate school success outcomes above and beyond common admissions data (GRE scores, undergraduate GPA, degree level, and degree program). For degree completion, when the six PPI scale scores were added to the model, the planning and organization PPI scale was a significant predictor. For graduate GPA, of the admissions data, degree program was a significant predictor of graduate GPA. When the six PPI scales were added to the model, degree program and GRE Analytical Writing scores were significant predictors of graduate GPA. For time-to-degree completion, of the admissions data, degree level was a significant predictor of time-to-degree completion and remained the only significant predictor when the PPI scale scores were added to the model. The researcher was also interested in determining if there were significant differences in undergraduate GPA, GRE scores, the six PPI scale scores, and graduate GPA based on the interaction among degree completion status (graduated vs dropped out), classification/level (master’s vs doctoral), and program (STEM vs NON). Results indicated a significant two-way interaction was present for degree completion and degree program. Simple effects analysis determined that students enrolled in STEM programs who graduated had significantly higher graduate GPAs than those who withdrew from their programs. When graduate GPA was taken out of the model, a significant two-way interaction was present for degree completion and degree level. Simple effects analyses determined that doctoral students who graduated had significantly lower GRE quantitative scores than those who withdrew from their programs. Finally, to determine how well the PPI scales performed in terms of creating enough variability among graduate applicants, ceiling and floor effects were examined. Results indicated that a ceiling effect was present for the ethics and integrity scale.