Understanding the limitations of global fit assessment in structural equation modeling
Personality and Individual DifferencesPublished 2 November 2006
James H. Steiger
Citations2,349
SJR quartileQ1
SJR score1.42
SNIP1.34
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
Barrett's (2007) article on "adjudging model fit" raises some important issues concerning the use of global fit indices to justify weak structural equation models, and recommends prohibition of future use of such indices. In this commentary, I critique Barrett's presentation, and show that his recommendations are (a) unnecessarily regressive, and (b) likely to be ignored. Then I suggest a constructive alternative in line with the spirit of his concerns.
Keywords
Decision SciencesMathematics
Structural Equation Modeling A Multidisciplinary JournalCutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives
105,395 Citations1999Li‐tze Hu, Peter M. Bentler
Psychological MethodsPower analysis and determination of sample size for covariance structure modeling.
9,775 Citations1996Robert C. MacCallum, Michael W. Browne +1 more
PsychometrikaA Reliability Coefficient for Maximum Likelihood Factor Analysis
7,328 Citations1973Ledyard R Tucker, Charles Lewis
Personality and Individual DifferencesStructural equation modelling: Adjudging model fit
2,518 Citations2006Paul Barrett
PsychometrikaStructural Analysis of Covariance and Correlation Matrices
1,012 Citations1978Karl G. Jöreskog
Multivariate Behavioral ResearchSome Cautions Concerning The Application Of Causal Modeling Methods
548 Citations1983Norman Cliff
There seems to be some danger of overlooking the long-established principles of scientific inference when complex computer programs are used to analyze correlational data, even though these new methods provide great increases in the rigor with which correlation data can be analyzed.
Choice Reviews OnlineA Handbook for data analysis in the behavioral sciences: methodological issues
545 Citations1993
This book discusses methodological and statistical issues surrounding the development of Mathematical Models in Psychology, as well as some of the techniques used in Bayesian Statistics, a branch of statistics based on Bayesian inference.
Psychological MethodsBeyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis.
538 Citations2004James H. Steiger
Confidence interval methods for improving on the standard F tests in the balanced, completely between-subjects, fixed-effects analysis of variance are presented.
BiometricsSymmetrical Confidence Intervals for Bioequivalence Trials
339 Citations1976Wilfred J. Westlake
This modification has the effect of decreasing the "effective" length of the confidence interval, on which the decision concerning bioequivalence is based, while increasing the confidence coefficient.
Structural Equation Modeling A Multidisciplinary JournalPoint Estimation, Hypothesis Testing, and Interval Estimation Using the RMSEA: Some Comments and a Reply to Hayduk and Glaser
300 Citations2000James H. Steiger
Educational and Psychological MeasurementCorrect Confidence Intervals for Various Regression Effect Sizes and Parameters: The Importance of Noncentral Distributions in Computing Intervals
270 Citations2001Michael Smithson