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ERIC Number: EJ978548
Record Type: Journal
Publication Date: 2012
Pages: 27
Abstractor: As Provided
Reference Count: 29
ISBN: N/A
ISSN: ISSN-1070-5511
Principles and Practice of Scaled Difference Chi-Square Testing
Bryant, Fred B.; Satorra, Albert
Structural Equation Modeling: A Multidisciplinary Journal, v19 n3 p372-398 2012
We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra-Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001) and new (Satorra & Bentler, 2010) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction factor ("c"), depending on the particular structural equation modeling software used. Because of how LISREL 8 defines the SB scaled chi-square, LISREL users should compute "c" for each model by dividing the model's normal theory weighted least-squares (NTWLS) chi-square by its SB chi-square, to recover "c" accurately with both tests. EQS and M"plus" users, in contrast, should divide the model's maximum likelihood (ML) chi-square by its SB chi-square to recover c. Because ML estimation does not minimize the NTWLS chi-square, however, it can produce a negative difference in nested NTWLS chi-square values. Thus, we recommend the standard practice of testing the scaled difference in ML chi-square values for models M[subscript 1] and M[subscript 0] (after properly recovering "c" for each model), to avoid an inadmissible test numerator. We illustrate the difference in computations across software programs for the original and new scaled tests and provide LISREL, EQS, and M"plus" syntax in both single- and multiple-group form for specifying the model M[subscript 10] that is involved in the new test. (Contains 1 table.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers: N/A