A Method for Identifying Variables for Predicting STEM Enrollment

Abstract

Abstract This research examines demographic, academic, attitudinal, and experiential data from the Cooperative Institutional Research Program (CIRP) for over 12,000 students at two universities to test a methodology for identifying variables showing significant differences between students intending to major in science, technology, engineering, or mathematics (STEM) versus non‐STEM subjects. The methodology utilizes basic statistical techniques to identify significant differences between STEM and non‐STEM students within seven population subgroups based upon school attended, race/ethnicity, and gender. The value of individual variables is assessed by how consistently significant differences are found across the subgroups. The variables found to be most valuable in identifying STEM students reflect both quantitative and qualitative measures. Quantitative measures of academic ability such as SAT mathematics score, high school grade point average, and to a lesser extent SAT verbal score are all indicators. Qualitative measures including self‐ratings of mathematical ability, computer skills, and academic ability are also good indicators.

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