The Effect of Estimation Methods on SEM Fit Indices
We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI...
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Veröffentlicht in: | Educational and psychological measurement 2020-06, Vol.80 (3), p.421-445 |
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creator | Shi, Dexin Maydeu-Olivares, Alberto |
description | We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). We considered different types and levels of misspecification in factor analysis models: misspecified dimensionality, omitting cross-loadings, and ignoring residual correlations. Estimation methods had substantial impacts on the RMSEA and CFI so that different cutoff values need to be employed for different estimators. In contrast, SRMR is robust to the method used to estimate the model parameters. The same criterion can be applied at the population level when using the SRMR to evaluate model fit, regardless of the choice of estimation method. |
doi_str_mv | 10.1177/0013164419885164 |
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We considered different types and levels of misspecification in factor analysis models: misspecified dimensionality, omitting cross-loadings, and ignoring residual correlations. Estimation methods had substantial impacts on the RMSEA and CFI so that different cutoff values need to be employed for different estimators. In contrast, SRMR is robust to the method used to estimate the model parameters. 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We considered different types and levels of misspecification in factor analysis models: misspecified dimensionality, omitting cross-loadings, and ignoring residual correlations. Estimation methods had substantial impacts on the RMSEA and CFI so that different cutoff values need to be employed for different estimators. In contrast, SRMR is robust to the method used to estimate the model parameters. The same criterion can be applied at the population level when using the SRMR to evaluate model fit, regardless of the choice of estimation method.</description><subject>Computation</subject><subject>Error of Measurement</subject><subject>Estimating techniques</subject><subject>Factor Analysis</subject><subject>Goodness of Fit</subject><subject>Least Squares Statistics</subject><subject>Maximum likelihood method</subject><subject>Maximum Likelihood Statistics</subject><subject>Structural equation modeling</subject><subject>Structural Equation Models</subject><issn>0013-1644</issn><issn>1552-3888</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GA5</sourceid><recordid>eNp1kT1PwzAQhi0EouVjZwFFYmEJ-PwRJwsSqlIoasVAmS3HcdpUaVziFIl_j6OWApXw4tO9z73n8yF0AfgWQIg7jIFCxBgkccx9cID6wDkJaRzHh6jfyWGn99CJcwvsDwM4Rj1KGOEEaB_R6dwEaVEY3Qa2CFLXlkvVlrYOJqad29wFPnxNJ8GwbINRnZfauDN0VKjKmfPtfYrehul08BSOXx5Hg4dxqDnHbchpFLGcJ6AFZFkSGS24AgOCJUkOURwlnBjNtSJECIEVEZAnBc1yETGfi-kput_4rtbZ0uTa1G2jKrlq_BObT2lVKf8qdTmXM_shBSHAEvAGlxsD05R6V5c-A-GUYu71m22Dxr6vjWvlsnTaVJWqjV07SRhmEfNW1KPXe-jCrpvaj99Rfqburz2FN5RurHONKXZNActuY3J_Y77k6veQu4LvFXkg3ABOzcxP138NvwBEF5jZ</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Shi, Dexin</creator><creator>Maydeu-Olivares, Alberto</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>ERI</scope><scope>GA5</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4120-6756</orcidid></search><sort><creationdate>20200601</creationdate><title>The Effect of Estimation Methods on SEM Fit Indices</title><author>Shi, Dexin ; Maydeu-Olivares, Alberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c550t-53664d591c71bb96ec75a1e17499d1686952ec5ca227770a271d9f3bd764a2283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computation</topic><topic>Error of Measurement</topic><topic>Estimating techniques</topic><topic>Factor Analysis</topic><topic>Goodness of Fit</topic><topic>Least Squares Statistics</topic><topic>Maximum likelihood method</topic><topic>Maximum Likelihood Statistics</topic><topic>Structural equation modeling</topic><topic>Structural Equation Models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Dexin</creatorcontrib><creatorcontrib>Maydeu-Olivares, Alberto</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>ERIC</collection><collection>ERIC - Full Text Only (Discovery)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Educational and psychological measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Dexin</au><au>Maydeu-Olivares, Alberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1253305</ericid><atitle>The Effect of Estimation Methods on SEM Fit Indices</atitle><jtitle>Educational and psychological measurement</jtitle><addtitle>Educ Psychol Meas</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>80</volume><issue>3</issue><spage>421</spage><epage>445</epage><pages>421-445</pages><issn>0013-1644</issn><eissn>1552-3888</eissn><abstract>We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). 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subjects | Computation Error of Measurement Estimating techniques Factor Analysis Goodness of Fit Least Squares Statistics Maximum likelihood method Maximum Likelihood Statistics Structural equation modeling Structural Equation Models |
title | The Effect of Estimation Methods on SEM Fit Indices |
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