The Model-Size Effect on Traditional and Modified Tests of Covariance Structures
| Autor: | Sven Reinecke, Anne Boomsma, Walter Herzog |
|---|---|
| Přispěvatelé: | Psychometrics and Statistics, Clinical Psychology and Experimental Psychopathology, Sociology/ICS |
| Rok vydání: | 2007 |
| Předmět: |
CONFIRMATORY FACTOR-ANALYSIS
HYPOTHESIS Sociology and Political Science Monte Carlo method Degrees of freedom (statistics) General Decision Sciences Multivariate normal distribution Covariance FIT EQUATION MODELS TEST STATISTICS NUMBER Modeling and Simulation Statistics Range (statistics) SPECIFICATION ERROR LIKELIHOOD RATIO CRITERIA SMALL SAMPLE-SIZES General Economics Econometrics and Finance Statistic Type I and type II errors Statistical hypothesis testing Mathematics |
| Zdroj: | Structural Equation Modeling, 14(3), 361-390 |
| ISSN: | 1532-8007 1070-5511 |
| DOI: | 10.1080/10705510701301602 |
| Popis: | According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that the traditional maximum likelihood ratio statistic, T-ML, overestimates nominal Type I error rates up to 70% under conditions of multivariate normality. Some alternative statistics for the correction of model-size effects were also investigated: the scaled Satorra-Bentler statistic, T-SC; the adjusted Satorra-Bender statistic, T-AD (Satorra & Bentler, 1988, 1994); corresponding Bartlett corrections, T-MLb, T-SCb, and T-ADb (Bartlett, 1950); and corresponding Swain corrections, T-MLs, T-SCs, and T-ADs (Swain, 1975). The empirical findings indicate that the model test statistic T-MLs should be applied when large structural equation models are analyzed and the observed variables have (approximately) a multivariate normal distribution. |
| Databáze: | OpenAIRE |
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