Drawing Valid Inferences From The Nested Structure Of Engineering Education Data: Application Of A Hierarchical Linear Model To The Succeed Longitudinal Database

Abstract

Although hierarchical linear models are seldom used in engineering educational research, the nested structure of students in various colleges of engineering and the longitudinal nature of student records supports the use of such models. Hierarchical linear models account for the nested structure and can test hypotheses on both the schools and the students within the schools simultaneously, thereby eliminating aggregation bias and misestimated standard errors that result when the nested structure is ignored. In the present study, a hierarchical linear model is fitted to the SUCCEED longitudinal database using only students that graduated. As an example, cumulative GPA is regressed on Carnegie school classification, school setting, degree received, gender gap, and citizenship gap with SAT total score and number of terms attended as covariates. The results indicate that there is significant cumulative GPA variance between schools, accounting for 19% of the variance. Additionally, the gender gap and citizenship gap accounted for 6% of the within school cumulative GPA variance, but school setting accounted for 61% of the between school citizenship gap variance. In particular, students that receive their degree in engineering had the highest cumulative GPA. Non-citizens tended to have higher cumulative GPAs than citizens. Another finding is total SAT score is more predictive of cumulative GPA in urban schools than suburban schools. Finally, urban and/or research schools had the strongest relationship between number of terms until graduation with cumulative GPA in that longer times to graduation are associated with lower cumulative GPA.

The Role Of Academic Performance In Engineering Attrition

“…A better understanding of the postmatriculation factors that influence student migration out of an engineering degree program Page 11.1324.2 would help suggest improvements in advising systems and modifications to curricula. Postmatriculation factors that have been studied include ethnicity, gender, 11 institutional specific metrics (e.g., institutional size, type (Carnegie classifications), or control (public vs. private), 3,4,5 institutional selectivity, 6,7 faculty to student ratio or class size, 5 student body and racial climate, 16,17 financial aid, 17,20,21 enrichment programs, 17 number of accredited engineering programs, 23 and number of student support programs 24 ), and student-specific factors such as course performance (1 st semester, freshman year, math and science) [27][28][29] , student involvement and effort, 13,18 student academic and social integration, 19 and student perceptions and attitude 24,25,26 ). Among these factors and as reported for high school GPA, the existence of a correlation between college GPA and persistence or graduation in engineering is widely reported.…”