Texas Tech University

Supplemental Materials for Books and Published Papers

Supplemental Materials for Books and Published Papers

Books

Contextual effects

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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Dyadic


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

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Lee, J., Little, T. D., & Preacher, K.J. (2010). Partial factorial invariance in cross-cultural 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.

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Long_SEM


Little, T. D. (2013). Longitudinal Structural Equation Modeling. New York: Guilford press.
Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website (http://immap.educ.ttu.edu/guilford/little) provides datasets for all of the examples—which include studies of bullying, adolescent students' emotions, and healthy aging—with syntax and output from LISREL, Mplus, and R (lavaan).

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Papers

Brook, J., Rifenbark, G.G., Boulton, A.J., Little, T.D., & McDonald, T. (submitted). Risk and protective factors for drug use among youth living in foster care. Journal of Adolescent Health.

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Geldhof, G. J., Pornprasertmanit, S., Schoemann, A., & Little, T. D. (in press). Orthogonalizing through residual centering: Applications and caveats. Educational and Psychological Measurement.

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Jorgensen, T. D., Schoemann, A. M., McPherson, B., Rhemtulla, M., Wu, W., & Little T. D. (submitted). Assignment methods in the three-form planned missing design. Manuscript submitted for publication to International Journal of Behavioral Development.

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Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (submitted). On the Joys of Missing Data. Journal of Pediatric Psychology.

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Pornprasertmanit, S., & Little, T. D. (in press). Determining directional dependency in causal associations. International Journal of Behavioral Development.

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Shogren, K.A., Wehmeyer, M.L., Palmer, S.B., Rifenbark, G.G., Little, T.D. (2012). Postschool outcomes of youth with disabilities: The impact of self-determination. Journal of Special Education.

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