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Title: Correcting for Measurement Error in Latent Variables Used as Predictors
Resulting in 1 citation.
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Schofield, Lynne Steuerle |
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Correcting for Measurement Error in Latent Variables Used as Predictors Annals of Applied Statistics 9,4 (December 2015): 2133-2152. Also: http://www.ncbi.nlm.nih.gov/pubmed/26977218 Cohort(s): NLSY97 Publisher: Institute of Mathematical Statistics Keyword(s): College Major/Field of Study/Courses; Methods/Methodology; Modeling, Mixed Effects; Modeling, Structural Equation; Personality/Big Five Factor Model or Traits; Test Scores/Test theory/IRT Permission to reprint the abstract has not been received from the publisher. 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 under-represented minorities. |
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Bibliography Citation
Schofield, Lynne Steuerle. "Correcting for Measurement Error in Latent Variables Used as Predictors." Annals of Applied Statistics 9,4 (December 2015): 2133-2152.
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