Errors of inference in structural equation modeling

Although structural equation modeling (SEM) is one of the most comprehensive and flexible approaches to data analysis currently available, it is nonetheless prone to researcher misuse and misconceptions. This article offers a brief overview of the unique capabilities of SEM and discusses common sour...

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Bibliographic Details
Published in:Psychology in the schools 2007-05, Vol.44 (5), p.461-470
Main Authors: McCoach, D. Betsy, Black, Anne C., O'Connell, Ann A.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Data Analysis
Error Patterns
Errors
Fundamental and applied biological sciences. Psychology
Goodness of Fit
Hypothesis Testing
Inferences
Methodology. Experimentation
Misconceptions
Prediction
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
Statistics. Mathematics
Structural Equation Models
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Summary:Although structural equation modeling (SEM) is one of the most comprehensive and flexible approaches to data analysis currently available, it is nonetheless prone to researcher misuse and misconceptions. This article offers a brief overview of the unique capabilities of SEM and discusses common sources of user error in drawing conclusions from these analyses. We make recommendations to guide proper analytical practices and appropriate inferences and provide references for more advanced study. © 2007 Wiley Periodicals, Inc. Psychol Schs 44: 461–470, 2007.
ISSN:0033-3085
1520-6807
DOI:10.1002/pits.20238