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- W2925225516 abstract "Investigation of measurement invariance (MI) commonlyassumes correct specification of dimensionality across multiplegroups. Although research shows that violation of thedimensionality assumption can cause bias in model parameterestimation for single-group analyses, little research on this issuehas been conducted for multiple-group analyses. This study exploredthe effects of mismatch in dimensionality between data and analysismodels with multiple-group analyses at the population and samplelevels. Datasets were generated using a bifactor model withdifferent factor structures and were analyzed with bifactor andsingle-factor models to assess misspecification effects onassessments of MI and latent mean differences. As baseline models,the bifactor models fit data well and had minimal bias in latentmean estimation. However, the low convergence rates of fittingbifactor models to data with complex structures and small samplesizes caused concern. On the other hand, effects of fitting themisspecified single-factor models on the assessments of MI andlatent means differed by the bifactor structures underlying data.For data following one general factor and one group factoraffecting a small set of indicators, the effects of ignoring thegroup factor in analysis models on the tests of MI and latent meandifferences were mild. In contrast, for data following one generalfactor and several group factors, oversimplifications of analysismodels can lead to inaccurate conclusions regarding MI assessmentand latent mean estimation." @default.
- W2925225516 created "2019-04-01" @default.
- W2925225516 creator A5012105503 @default.
- W2925225516 date "2018-01-01" @default.
- W2925225516 modified "2023-09-23" @default.
- W2925225516 title "Assessing Measurement Invariance and Latent Mean Differences with Bifactor Multidimensional Data in Structural Equation Modeling" @default.
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