Modified Distribution-Free Goodness-of-Fit Test Statistic.
Psychometrika
; 83(1): 48-66, 2018 03.
Article
em En
| MEDLINE
| ID: mdl-28597361
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 3762-83, 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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Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Análise Multivariada
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
Psychometrika
Ano de publicação:
2018
Tipo de documento:
Article