A Beginner's Guide to Structural Equation Modeling

Randall E. Schumacker; Richard G. Lomax · Psychology Press eBooks · 2004

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Author / responsible party

Randall E. Schumacker; Richard G. Lomax

Publisher

Psychology Press eBooks

Year

2004

Language

English

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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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APA 7

Schumacker, R. E. & Lomax, R. G. (2004). A Beginner's Guide to Structural Equation Modeling. Psychology Press eBooks. https://doi.org/10.4324/9781410610904

MLA

Schumacker, Randall E, and Richard G. Lomax. A Beginner's Guide to Structural Equation Modeling. Psychology Press eBooks, 2004. https://doi.org/10.4324/9781410610904.

Chicago

Schumacker, Randall E. and Richard G. Lomax. 2004. A Beginner's Guide to Structural Equation Modeling. Psychology Press eBooks. https://doi.org/10.4324/9781410610904.

Harvard

Schumacker, R. E. and Lomax, R. G. 2004, A Beginner's Guide to Structural Equation Modeling, Psychology Press eBooks, available at: https://doi.org/10.4324/9781410610904 [Accessed 7 Aug. 2026].

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Title: A Beginner's Guide to Structural Equation Modeling

Author / contributors: Randall E. Schumacker; Richard G. Lomax

Publisher: Psychology Press eBooks

Publication year: 2004

Language: English

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Structural equation modeling; Computer science; Machine learning