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Structural equation modeling and its relationship to multiple regression and factor analysis

Using a conceptual and nontechnical approach, the meaning of structural equation modeling (SEM) and the similarities to, and differences from, more commonly used procedures such as correlation, regression, path analysis, and factor analysis are explained. Application of the statistical technique is...

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Published in:Research in nursing & health 1998-06, Vol.21 (3), p.271-281
Main Authors: Musil, Carol M., Jones, Susan L., Warner, Camille D.
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Language:English
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Jones, Susan L.
Warner, Camille D.
description Using a conceptual and nontechnical approach, the meaning of structural equation modeling (SEM) and the similarities to, and differences from, more commonly used procedures such as correlation, regression, path analysis, and factor analysis are explained. Application of the statistical technique is presented using data from a study of the relationships among stresses, strains, and physical health in a random sample of 492 community‐dwelling elders aged 65 and older. Advantages of each statistical procedure are described. Theoretical issues related to the use of each procedure are presented with emphasis on the need for a sound theoretical model and match between the statistical procedure and the aims of the analysis. © 1998 John Wiley & Sons, Inc. Res Nurs Health 21:271–281, 1998
doi_str_mv 10.1002/(SICI)1098-240X(199806)21:3<271::AID-NUR10>3.0.CO;2-G
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language eng
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source Wiley-Blackwell Read & Publish Collection
subjects Aged - psychology
Biological and medical sciences
Causality
Epidemiology
factor analysis
Factor Analysis, Statistical
General aspects
Health Status
Humans
Longitudinal Studies
Medical sciences
Methodology
Models, Statistical
multiple regression
Nursing
Predictive Value of Tests
Public health. Hygiene
Public health. Hygiene-occupational medicine
Regression Analysis
Reproducibility of Results
Stress, Psychological - psychology
structural equation modeling (SEM)
title Structural equation modeling and its relationship to multiple regression and factor analysis
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