A Beginner's Guide to Structural Equation Modeling
Abstract
The second edition features: a CD with all of the book's Amos, EQS, and LISREL programs and data sets; new chapters on importing data issues related to data editing and on how to report research; an updated introduction to matrix notation and programs that illustrate how to compute these calculations; many more computer program examples and chapter exercises; and increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues. The new edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged.
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Multiple fit indices should be considered collectively when evaluating model fit, rather than relying solely on the p-value.
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- Authors
- Schumacker, R. E., & Lomax, R. G.
- Affiliations
- University of Alabama (US)
- ORCID
- Lomax, Richard G.
- Venue
- Psychology Press eBooks
- Publisher
- Psychology Press
- Published
- 2004-06-24
- OpenAlex
- W4301352023
- Cited by
- 6,235
- References
- 0
- Citations by year
- 2026: 204 · 2025: 461 · 2024: 506 · 2023: 507 · 2022: 612 · 2021: 502 · 2020: 454 · 2019: 357 · 2018: 325 · 2017: 316 · 2016: 274 · 2015: 247 · 2014: 227 · 2013: 268 · 2012: 199
- FWCI
- 33.68
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Psychology Press eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- structural equation modeling, Amos, EQS, LISREL, matrix notation, hypothesis testing, statistical power, sample size, data editing, correlation, multivariate analysis
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