Understanding Structural Equation Models Models of Relationships Between Variables
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- Hardcover
- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 87.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
29.12.2025
Verlag
Taylor & Francis eBooksSeitenzahl
402 (Printausgabe)
Dateigröße
15591 KB
Sprache
Englisch
EAN
9781040589472
The field of structural equation models (SEMs) is rapidly expanding. A researcher who wants to select and apply SEMs to their data faces several challenges: (1) They can often become extremely complex, with many parameters to estimate. Small samples or those with relatively few variables often cannot support this complexity reliably, leading to under-identified models, poor power, or unstable estimates; (2) Researchers must choose an appropriate measurement model, and these choices are not often well understood in advance; (3) No single "correct" SEM exists, although "better" ones do, and the existence of competing plausible alternatives is often overlooked; and (4) Critical examination of model assumptions involving the linearity of parameters and the existence of influential or outlying observations is often overlooked. This book provides an overview of SEMs as a flexible, skeptical, and iterative scientific process.
Key Features:
- Emphasis on multiverse analysis, right-sizing statistical models to data, and the generation of plausible skeptical alternatives
- Robust assumption checking (LOESS regression, regression and SEM diagnostics)
- Detailed, visual coverage of a variety of path diagrams, their links to matrix-based specifications, and data exploration using heat-map visualization and tests of dimensionality
- A variety of SEMs including mediational models, psychometrics (e.g., parallel, tau-equivalent, congeneric measurement), growth curve models, exploratory factor analysis, multigroup, categorical, and exploratory structural equation modeling
- Supplemented by a website featuring code, data, lecture slides, and additional material
This text is designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding. Its careful balance of theory, worked examples, and emphasis on skepticism will help its audience build confidence in using SEMs flexibly and responsibly for a broad range of social and behavioral science research.
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