Differential item functioning across gender with MIMIC modeling: PISA 2018 financial literacy items

Abstract

The aim of this study is to investigate the presence of DIF over the gender variable with the latent class modeling approach. Data were 880 students from the USA who participated in the PISA 2018 8th-grade financial literacy assessment. Latent class analysis (LCA) approach was used to determine the latent classes and the data fit the three-class model better in line with fit indices. To obtain more information about the characteristics of the emerging classes, uniform and non-uniform DIF sources were determined by using the Multiple Indicator Multiple Causes (MIMIC) model. The findings are very important in terms of contributing to the interpretation of latent classes. According to the results, the gender variable is a potential source of DIF for latent class indicators. Gathering unbiased estimates for the measurement and structural parameters, it is important to include direct effects in the classes. Ignoring these effects can lead to incorrect determination of implicit classess. An example of the application of Multiple Indicator Multiple Causes (MIMIC) model showed in a latent class framework with a stepwise approach with this study.

A Bayesian Moderated Nonlinear Factor Analysis Approach for DIF Detection under Violation of the Equal Variance Assumption

“…A number of simulation and applied studies have explored DIF detection methods within the MIMIC framework (Chun et al., 2016; Kim et al., 2017; Saaatcioglu, 2022; Woods, 2009; Woods & Grimm, 2011). Many of these studies have reported that the MIMIC model performs comparably to multiple‐group methods when it comes to detecting uniform DIF (Finch, 2005; Woods, 2009).…”