{"title":"Evaluating Close Fit in Ordinal Factor Analysis Models With Multiply Imputed Data.","authors":"Dexin Shi, Bo Zhang, Ren Liu, Zhehan Jiang","doi":"10.1177/00131644231158854","DOIUrl":null,"url":null,"abstract":"<p><p>Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.</p>","PeriodicalId":11502,"journal":{"name":"Educational and Psychological Measurement","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10795567/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational and Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00131644231158854","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/27 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.
期刊介绍:
Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.