Longitudinal structural equation modeling Little, Todd D.

By: Little, Todd D
Material type: TextTextSeries: Methodology in the Social SciencesPublisher: New York Guilford Press 2013Description: xxii, 386 p.ISBN: 9781462510160Subject(s): Social sciences - Statistical methods | Longitudinal methodDDC classification: 001.433 Summary: 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).
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Non-fiction 001.433 L4L6 (Browse shelf) Available 181226

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).

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