James P. Selig received his Ph.D. in quantitative psychology from the University of Kansas. He is currently an Associate Professor of Biostatistics at the University of Arkansas for Medical Sciences where he teaches graduate courses in advanced statistics and statistical software. His quantitative areas of interest include: longitudinal data analysis, multilevel modeling, structural equation modeling, interdependent data analysis, and mediation models. He is particularly interested in temporal design, or issues of time and timing in the design of longitudinal studies, and in the topic of time lag dependent effects. He currently serves on the editorial board for Developmental Psychology and has previously served on the editorial boards for Parenting: Science and Practice, and The Journal of Humanistic Counseling.
Card, N. A., Selig, J. P., Little, T. D. (Eds.) (2008). Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences. New York, NY: Routledge
Lee, J., Little, T. D., & Preacher, K.J. (2010). Partial factorial invariance in crosscultural research. In E. Davidov, P. Schmidt & J. Billiet (Eds.), Cross-cultural data analysis: Methods and applications. New York: Guilford press. Structural equation modeling (SEM) has been suggested as a single, integrated framework for testing cross-cultural group differences. Recently, particular forms of SEM have been widely used to detect the items that function differently for different groups (i.e., differential item functioning; DIF). Accordingly, the primary goal of this chapter is to discuss some methodological issues that may arise when researchers conduct SEM-based DIF analysis.