Little, T. D., Bovaird, J. A., & Card, N. A. (Eds.) (2007). Modeling contextual effects in longitudinal studies. Mahwah, NJ: LEA
Longitudinal data are critical for understanding how individuals change across time. Researchers are faced with a complex task when modeling the contexts in which longitudinal processes unfold. Modeling Contextual Effects in Longitudinal Studies reviews the challenges and alternative approaches to modeling these influences and provides methodologies and data analytic strategies for behavioral and social science researchers.
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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.
Applied Meta-Analysis for Social Science Research
By Noel A. Card
Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and “Practical Matters” sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs.