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This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page (please see the book's entry at www.guilford.com ) offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools. New to This Edition: Thoroughly revised and restructured to follow the phases of most SEM analyses Syntax, data, and output files for all detailed research examples are now provided online Exercises with answers, which support self-study Topic boxes on specialized issues, such as dealing with problems in the analysis; the assessment of construct measurement reliability; and more Updated coverage of a more rigorous approach to hypothesis and model testing; the evaluation of measurement invariance; and more.This book is important reading for graduate students, instructors, and researchers in psychology, education, human development and family studies, management, sociology, social work, nursing, public health, criminal justice, and communication. It also serves as a text for graduate-level courses in structural equation modeling, multivariate statistics, advanced quantitative methods, or research methodology.
The revised guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) features new sections on statistical concepts, nonrecursive models of multiple-sample SEM, and extensive explanations of latent growth models.
This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools. New to This Edition *Thoroughly revised and restructured to follow the phases of most SEM analyses. *Syntax, data, and output files for all detailed research examples are now provided online. *Chapter on computer tools. *Exercises with answers, which support self-study. *Topic boxes on specialized issues, such as dealing with problems in the analysis; the assessment of construct measurement reliability; and more. *Updated coverage of a more rigorous approach to hypothesis and model testing; the evaluation of measurement invariance; and more. *”Troublesome” examples have been added to provide a context for discussing how to handle various problems that can crop up in SEM analyses.
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 (www.guilford.com/little-materials) 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).
The Oxford Handbook of Quantitative Methods in Psychology provides an accessible and comprehensive review of the current state-of-the-science and a one-stop source for learning and reviewing current best-practices in a quantitative methods across the social, behavioral, and educational sciences.
Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.
The Oxford Handbook of Quantitative Methods in Psychology provides an accessible and comprehensive review of the current state-of-the-science and a one-stop source for learning and reviewing current best-practices in a quantitative methods across the social, behavioral, and educational sciences.

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