Differential Item Functioning across Gender with MIMIC Modeling: PISA 2018 Financial Literacy Items

The aim of this study is to investigate the presence of DIF over the gender variable with the latent class modeling approach. The data were collected from 953 students who participated in the PISA 2018 8th-grade financial literacy assessment in the USA. Latent Class Analysis (LCA) approach was used to identify the latent classes, and the data fit the three-class model better in line with fit indices. In order to obtain more information about the characteristics of the emerging classes, uniform and non-uniform DIF sources were identified 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 was a source of DIF for latent classes. It is important to include direct effects by gathering unbiased estimates for the measurement and structural parameters. Disregarding these effects can lead to incorrect identification of implicit classes. A sample application of MIMIC model was performed in a latent class framework with a stepwise approach in this study.