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The impact of non-normality, sample size and estimation technique on goodness-of-fit measures in structural equation modeling: evidence from ten empirical models of travel behavior

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Abstract

Ten empirical models of travel behavior are used to measure the variability of structural equation model goodness-of-fit as a function of sample size, multivariate kurtosis, and estimation technique. The estimation techniques are maximum likelihood, asymptotic distribution free, bootstrapping, and the Mplus approach. The results highlight the divergence of these techniques when sample sizes are small and/or multivariate kurtosis high. Recommendations include using multiple estimation techniques and, when sample sizes are large, sampling the data and reestimating the models to test both the robustness of the specifications and to quantify, to some extent, the large sample bias inherent in the χ 2 test statistic.

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Authors and Affiliations

  1. Federal Transit Administration, 1200 New Jersey Avenue SE, Washington, DC, 20003, USA

    David T. Ory

  2. Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA

    Patricia L. Mokhtarian

Authors
  1. David T. Ory
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  2. Patricia L. Mokhtarian
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Correspondence to Patricia L. Mokhtarian.

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Ory, D.T., Mokhtarian, P.L. The impact of non-normality, sample size and estimation technique on goodness-of-fit measures in structural equation modeling: evidence from ten empirical models of travel behavior. Qual Quant 44, 427–445 (2010). https://doi.org/10.1007/s11135-008-9215-6

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