Alternative Multiple Imputation Inference for Mean and Covariance Structure Modeling

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dc.contributor.authorTaehun Lee
dc.contributor.authorLi Cai
dc.date.accessioned2016-01-14T19:54:00Z
dc.date.accessioned2016-03-30T15:32:31Z
dc.date.available2016-01-14T19:54:00Z
dc.date.available2016-03-30T15:32:31Z
dc.date.issued2012-12-01
dc.description.abstractModel-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure modeling. Extending the logic developed by Yuan and Bentler, Cai, and Cai and Lee, we propose an alternative method for conducting multiple imputation–based inference for mean and covariance structure modeling. In addition to computational simplicity, our method naturally leads to an asymptotically chi-square model fit test statistic. Using simulations, we show that our new method is well calibrated, and we illustrate it with analyses of three real data sets. A SAS macro implementing this method is also provided.en_US
dc.description.peerreviewYesen_US
dc.description.peerreviewnoteshttps://us.sagepub.com/en-us/nam/manuscript-submission-guidelinesen_US
dc.identifier.citationLee, T., & Cai, L. (2012). Alternative Multiple Imputation Inference for Mean and Covariance Structure Modeling. Journal of Educational and Behavioral Statistics, 37(6), 675-702. doi: 10.3102/1076998612458320en_US
dc.identifier.doi10.3102/1076998612458320en_US
dc.identifier.urihttp://hdl.handle.net/11244/25572
dc.language.isoen_USen_US
dc.publisherJournal of Educational and Behavioral Statistics
dc.rights.requestablefalseen_US
dc.subjectmultiple imputationen_US
dc.subjectplausible valuesen_US
dc.subjectstructural equation modelingen_US
dc.subjectgoodness-of-fit testen_US
dc.titleAlternative Multiple Imputation Inference for Mean and Covariance Structure Modelingen_US
dc.typeResearch Articleen_US

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