CORRECTING FOR MEASUREMENT ERROR IN LATENT VARIABLES USED AS PREDICTORS

• 成果类型: Article
• 署名作者: Schofield, Lynne Steuerle
• 署名单位: Swarthmore College
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/15-AOAS877
• 发表日期: 2015
• 页码: 2133-2152
• 关键词: individual achievement-test; stem; personality; science; models; persistence; majors; women; experiences; minorities

摘要

This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory, is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I find prior mathematics proficiency and personality have been previously underestimated in the STEM retention literature. Pre-college mathematics proficiency and personality explain large portions of the racial and gender gaps. The findings have implications for those who design interventions aimed at increasing the rates of STEM persistence among women and underrepresented minorities.