Abstract
Covariance structure analysis uses chi 2 goodness-of-fit test statistics whose adequacy is not known. Scientific conclusions based on models may be distorted when researchers violate sample size, variate independence, and distributional assumptions. The behavior of 6 test statistics is evaluated with a Monte Carlo confirmatory factor analysis study. The tests performed dramatically differently under 7 distributional conditions at 6 sample sizes. Two normal-theory tests worked well under some conditions but completely broke down under other conditions. A test that permits homogeneous nonzero kurtoses performed variably. A test that permits heterogeneous marginal kurtoses performed better. A distribution-free test performed spectacularly badly in all conditions at all but the largest sample sizes. The Satorra-Bentler scaled test statistic performed best overall.
Keywords
StatisticsGoodness of fitCovarianceTest statisticStatisticSample size determinationEconometricsConfirmatory factor analysisTest (biology)Independence (probability theory)Monte Carlo methodMathematicsStatistical hypothesis testingF-test of equality of variancesSample (material)Structural equation modeling
MeSH Terms
FemaleHumansMaleModelsStatisticalModelsTheoretical
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Publication Info
- Year
- 1992
- Type
- article
- Volume
- 112
- Issue
- 2
- Pages
- 351-362
- Citations
- 1355
- Access
- Closed
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Cite This
Li‐tze Hu,
Peter M. Bentler,
Yutaka Kano
(1992).
Can test statistics in covariance structure analysis be trusted?.
Psychological Bulletin
, 112
(2)
, 351-362.
https://doi.org/10.1037/0033-2909.112.2.351
Identifiers
- DOI
- 10.1037/0033-2909.112.2.351
- PMID
- 1454899