Detection and Prediction of Gender-Based Differential Item Functioning using the MIMIC Model
Kevin Krost () and
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Joshua Cohen: Virginia Tech
2017 Stata Conference from Stata Users Group
There has been extensive research indicating gender-based differences among STEM subjects, particularly mathematics (Albano & Rodriguez, 2013; Lane, Wang, & Magone, 1996). Similarly, gender-based differential item functioning (DIF) has been researched due to the disadvantages females face in STEM subjects when compared to their male counterparts. Given that, this study will apply the multiple indicators multiple causes (MIMIC) model, a type of structural equation model, to detect the presence of gender-based DIF using the Program for International Student Assessment (PISA) mathematics data from students in the United States of America then predict the DIF using math-related covariates. This study will build upon a previous study which explored the same data using the hierarchical generalized linear model and will be confirmatory in nature. Based on the results of the previous study, it is expected that several items will exhibit DIF which disadvantages females, and that mathematics-based self-efficacy will predict the DIF. However, additional covariates will also be explored and the two models will be compared in terms of their DIF-detection and the subsequent modeling of DIF. Implications of these results include females under-achieving when compared to their male counterparts, thus continuing the current trend. These gender differences can further manifest at the national level, causing US students as a whole to under-perform at the international level. Last, the efficacy of the MIMIC model to detect and predict DIF will be illustrated and become increasingly used to model and better understand differences and DIF.
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Persistent link: https://EconPapers.repec.org/RePEc:boc:scon17:19
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