Core reporting practices in structural equation modeling.

BACKGROUND Structural equation modeling (SEM) is a popular analysis technique because of the wide range of questions that it can help answer. There are several pieces of information specific to SEM that should be reported when this technique is used. OBJECTIVES To demonstrate a basic framework for reporting SEM analyses, to provide definitions of key terms readers will encounter, and to illustrate 2 examples for reporting SEM results. METHODS Data from 650 participants who completed 3 self-report surveys were used to test a confirmatory factor analysis and a structural model as examples of information to be reported. RESULTS The results displayed are requisite information for any SEM analysis. CONCLUSIONS It is important for investigators to provide this information so that readers can properly evaluate the results and conclusions based on the analyses.

[1]  R. MacCallum,et al.  Power analysis and determination of sample size for covariance structure modeling. , 1996 .

[2]  Scott R. Eliason Maximum likelihood estimation: Logic and practice. , 1994 .

[3]  G. Hancock,et al.  EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT HANCOCK AND FREEMAN POWER AND SAMPLE SIZE FOR THE ROOT MEAN SQUARE ERROR OF APPROXIMATION TEST OF NOT CLOSE FIT IN STRUCTURAL EQUATION MODELING , 2001 .

[4]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[5]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[6]  Geoffrey M. Maruyama,et al.  Basics of structural equation modeling , 1997 .

[7]  S. West,et al.  The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. , 1996 .

[8]  A. Satorra,et al.  Corrections to test statistics and standard errors in covariance structure analysis. , 1994 .

[9]  J. Loehlin Latent variable models , 1987 .

[10]  Keith F Widaman,et al.  On specifying the null model for incremental fit indices in structural equation modeling. , 2003, Psychological methods.

[11]  J. S. Long,et al.  Covariance Structure Models: An Introduction to LISREL , 1983 .

[12]  J. S. Long Confirmatory Factor Analysis: A Preface to Lisrel , 1983 .

[13]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[14]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[15]  B. Byrne A Primer of Lisrel: Basic Applications and Programming for Confirmatory Factor Analytic Models , 1989 .

[16]  R. MacCallum,et al.  Applications of structural equation modeling in psychological research. , 2000, Annual review of psychology.

[17]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[18]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[19]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[20]  John T. Willse,et al.  The Search for "Optimal" Cutoff Properties: Fit Index Criteria in Structural Equation Modeling , 2006 .