Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests
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| Title: | Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests |
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| Language: | English |
| Authors: | Bang Quan Zheng (ORCID |
| Source: | Structural Equation Modeling: A Multidisciplinary Journal. 2025 32(1):136-141. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 6 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Monte Carlo Methods, Structural Equation Models, Goodness of Fit, Robustness (Statistics), Evaluation Methods, Social Science Research, Behavioral Science Research, Error of Measurement, Test Reliability |
| DOI: | 10.1080/10705511.2024.2354802 |
| ISSN: | 1070-5511 1532-8007 |
| Abstract: | This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can enhance its effectiveness in evaluating model fit and specification. To illustrate this point, we present three common scenarios relevant to social and behavioral science research using Monte Carlo simulations, where fit indices may inadequately address concerns regarding goodness-of-fit, while the chi-square statistic can offer valuable insights. Our recommendation is to report both the chi-square test and fit indices, prioritizing precise model specification to ensure the reliability of model fit indicators. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1457158 |
| Database: | ERIC |
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| Abstract: | This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can enhance its effectiveness in evaluating model fit and specification. To illustrate this point, we present three common scenarios relevant to social and behavioral science research using Monte Carlo simulations, where fit indices may inadequately address concerns regarding goodness-of-fit, while the chi-square statistic can offer valuable insights. Our recommendation is to report both the chi-square test and fit indices, prioritizing precise model specification to ensure the reliability of model fit indicators. |
|---|---|
| ISSN: | 1070-5511 1532-8007 |
| DOI: | 10.1080/10705511.2024.2354802 |
