Analyzing Fairness Among Linguistic Minority Populations Using a Latent Class Differential Item Functioning Approach

ABSTRACT

Differential item functioning (DIF) analyses have been used as the primary method in large-scale assessments to examine fairness for subgroups. Currently, DIF analyses are conducted utilizing manifest methods using observed characteristics (gender and race/ethnicity) for grouping examinees. Homogeneity of item responses is assumed denoting that all examinees respond to test items using a similar approach. This assumption may not hold with all groups. In this study, we demonstrate the first application of the latent class (LC) approach to investigate DIF and its sources with heterogeneous (linguistic minority groups). We found at least three LCs within each linguistic group, suggesting the need to empirically evaluate this assumption in DIF analysis. We obtained larger proportions of DIF items with larger effect sizes when LCs within language groups versus the overall (majority/minority) language groups were examined. The illustrated approach could be used to improve the ways in which DIF analyses are typically conducted to enhance DIF detection accuracy and score-based inferences when analyzing DIF with heterogeneous populations.

Notes

¹ We were limited to the variables available from the PISA’s student questionnaire, which provided us with a limited view of the types of differences that may underlie heterogeneity within these groups. Hence, our results do not represent the full extent to which these groups might vary but rather represent a limited set of variables, which can be useful in demonstrating the LC approach we are presenting although our results might not be extensive enough to understand the differences between the language groups.