Drawing Valid Inferences from the Nested Structure of EngineeringEducation Data: Application of a Hierarchical Linear Model to theSUCCEED Longitudinal Database

摘要

Although hierarchical linear models are seldom used in engineering educational research, thenested structure of students in various colleges of engineering and the longitudinal nature ofstudent records supports the use of such models. Hierarchical linear models account for thenested structure and can test hypotheses on both the schools and the students within the schoolssimultaneously, thereby eliminating aggregation bias and misestimated standard errors that resultwhen the nested structure is ignored. In the present study, a hierarchical linear model is fitted tothe 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 ascovariates. The results indicate that there is significant cumulative GPA variance betweenschools, accounting for 19% of the variance. Additionally, the gender gap and citizenship gapaccounted for 6% of the within school cumulative GPA variance, but school setting accountedfor 61% of the between school citizenship gap variance. In particular, students that receive theirdegree in engineering had the highest cumulative GPA. Non-citizens tended to have highercumulative GPAs than citizens. Another finding is total SAT score is more predictive ofcumulative GPA in urban schools than suburban schools. Finally, urban and/or research schoolshad the strongest relationship between number of terms until graduation with cumulative GPA inthat longer times to graduation are associated with lower cumulative GPA.