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.
Learn More about Modeling contextual effects in longitudinal studies
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.
Learn More about Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences
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 https://www.depts.ttu.edu/immap/Books.php provides datasets for all of the examples.
Learn More about Longitudinal Structural Equation Modeling
Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to today’s complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences.
Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.
Learn More about The Oxford Handbook of Quantitative Methods
Additional Related Books