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Modified Distribution-Free Goodness-of-Fit Test Statistic

So Yeon Chun (), Michael W. Browne () and Alexander Shapiro ()
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Michael W. Browne: Ohio State University
Alexander Shapiro: Georgia Institute of Technology

Psychometrika, 2018, vol. 83, issue 1, 48-66

Abstract: Abstract Covariance structure analysis and its structural equation modeling extensions have become one of the most widely used methodologies in social sciences such as psychology, education, and economics. An important issue in such analysis is to assess the goodness of fit of a model under analysis. One of the most popular test statistics used in covariance structure analysis is the asymptotically distribution-free (ADF) test statistic introduced by Browne (Br J Math Stat Psychol 37:62–83, 1984). The ADF statistic can be used to test models without any specific distribution assumption (e.g., multivariate normal distribution) of the observed data. Despite its advantage, it has been shown in various empirical studies that unless sample sizes are extremely large, this ADF statistic could perform very poorly in practice. In this paper, we provide a theoretical explanation for this phenomenon and further propose a modified test statistic that improves the performance in samples of realistic size. The proposed statistic deals with the possible ill-conditioning of the involved large-scale covariance matrices.

Keywords: covariance structures; distribution-free test statistic; asymptotics; Chi-square distribution; ill-conditioned problem (search for similar items in EconPapers)
Date: 2018
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