ArticlePsychometrika2018
Modified Distribution-Free Goodness-of-Fit Test Statistic.
Article in Psychometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling.Educational and psychological measurement · 2022Article
- Structural Equation Modeling With Many Variables: A Systematic Review of Issues and Developments.Frontiers in psychology · 2018Article
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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