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Robustness studies in covariance structure modeling - An overview and a meta-analysis

Hoogland, J. J. & Boomsma, A., Feb-1998, In : Sociological Methods & Research. 26, 3, p. 329-367 39 p.

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  • Jeffrey J. Hoogland
  • A Boomsma

In covariance structure modeling, several estimation methods are available. The robustness of an estimator against specific violations of assumptions can be determined empirically by means of a Monte Carlo study. Many such studies in covariance structure analysis have been published, but the conclusions frequently seem to contradict each other An overview of robustness studies in covariance structure analysis is given, and an attempt is made to generalize findings. Robustness studies are described and distinguished from each other systematically by means of certain characteristics. These characteristics serve as explanatory variables in a meta-analysis concerning the behavior of parameter estimators, standard error estimators, and goodness-of-fit statistics when the model is correctly specified.

Original languageEnglish
Pages (from-to)329-367
Number of pages39
JournalSociological Methods & Research
Volume26
Issue number3
Publication statusPublished - Feb-1998

    Keywords

  • CONFIRMATORY FACTOR-ANALYSIS, MAXIMUM-LIKELIHOOD, LATENT-VARIABLES, EQUATION MODELS, TEST STATISTICS, MEASUREMENT ERROR, LIKERT VARIABLES, SAMPLING ERROR, ESTIMATORS, METHODOLOGIES

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