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Sensitivity Of Goodness Of Fit Indices To Lack Of Measurement Invariance With Categorical Indicators And Many Groups

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  • Boris Sokolov

    (National Research University Higher School of Economics)

Abstract

Using Monte Carlo simulation experiments, this paper examines the performance of popular SEM goodness-of-fit indices, namely CFI, TLI, RMSEA, and SRMR, with respect to a specific task of measurement invariance testing with categorical data and many groups (10-50 groups). Study factors include the number of groups, the level of non-invariance in the data, and the absence/presence of model misspecifications other than non-invariance. In sum, the study design yields a total of 81 conditions. All simulated data sets are analyzed using two popular SEM estimators, MLR and WLSMV. The main contribution of this paper to the methodological literature on cross-cultural survey research is that it produces revised guidelines for evaluating the goodness of fit of invariance MGCFA models with many groups

Suggested Citation

  • Boris Sokolov, 2019. "Sensitivity Of Goodness Of Fit Indices To Lack Of Measurement Invariance With Categorical Indicators And Many Groups," HSE Working papers WP BRP 86/SOC/2019, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:86/soc/2019
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    References listed on IDEAS

    as
    1. Stegmueller, Daniel, 2011. "Apples and Oranges? The Problem of Equivalence in Comparative Research," Political Analysis, Cambridge University Press, vol. 19(4), pages 471-487.
    2. Steenkamp, Jan-Benedict E M & Baumgartner, Hans, 1998. "Assessing Measurement Invariance in Cross-National Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(1), pages 78-90, June.
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    More about this item

    Keywords

    SEM; MGCFA; measurement invariance; fit indices; simulations;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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