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A Method for Identifying Variables for Predicting STEM Enrollment
Author(s) -
Nicholls Gillian M.,
Wolfe Harvey,
BesterfieldSacre Mary,
Shuman Larry J.,
Larpkiattaworn Siripen
Publication year - 2007
Publication title -
journal of engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.896
H-Index - 108
eISSN - 2168-9830
pISSN - 1069-4730
DOI - 10.1002/j.2168-9830.2007.tb00913.x
Subject(s) - ethnic group , mathematics education , race (biology) , psychology , experiential learning , population , demography , biology , sociology , botany , anthropology
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.