Methods: Data were obtained from a sample of Washington and Georgia state high school students (n= 1,468; n= 337 African American, n= 597 Hispanic, n= 534 Caucasian) who completed WARNS as a standard practice in their schools. First, an item response theory graded response model was used to detect WARNS items for DIF on six domains using statistical significance and effect sizes. Items identified with large effects will be analyzed with a multilevel logistic regression model with contextual proxy variables (e.g., school climate rating, school SES) serving as level two predictors to explain the sources of DIF.
Results: On the Aggression-Defiance domain, 4 of 8 items showed DIF with large effects (>.80) between African American, Caucasian, and Hispanic students. The Family Environment domain had 1 of 5 items with large DIF. A single large DIF item was identified on the Peer Deviance domain between African American and Caucasian students, and 1 item on the School Engagement domain between African American and Hispanic students. The presentation will report the use of an ecological model of item responding (Zumbo et al., 2015) focusing on contextual variables as explanatory sources of DIF.
Conclusion: We demonstrate a novel method of how school and community data can be used with local assessment data to better understanding item bias in a confirmatory manner.