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dc.contributor.authorWu, Jiun-Yuen_US
dc.contributor.authorLin, John J. H.en_US
dc.contributor.authorNian, Mei-Wenen_US
dc.contributor.authorHsiao, Yi-Chengen_US
dc.date.accessioned2019-04-03T06:41:52Z-
dc.date.available2019-04-03T06:41:52Z-
dc.date.issued2017-09-22en_US
dc.identifier.issn1664-1078en_US
dc.identifier.urihttp://dx.doi.org/10.3389/fpsyg.2017.01464en_US
dc.identifier.urihttp://hdl.handle.net/11536/146108-
dc.description.abstractThe issue of equality in the between-and within-level structures in Multilevel Confirmatory Factor Analysis (MCFA) models has been influential for obtaining unbiased parameter estimates and statistical inferences. A commonly seen condition is the inequality of factor loadings under equal level-varying structures. With mathematical investigation and Monte Carlo simulation, this study compared the robustness of five statistical models including two model-based (a true and a mis-specified models), one design-based, and two maximum models (two models where the full rank of variance-covariance matrix is estimated in between level and within level, respectively) in analyzing complex survey measurement data with level-varying factor loadings. The empirical data of 120 3rd graders' (from 40 classrooms) perceived Harter competence scale were modeled using MCFA and the parameter estimates were used as true parameters to perform the Monte Carlo simulation study. Results showed maximum models was robust to unequal factor loadings while the design-based and the miss-specified model-based approaches produced conflated results and spurious statistical inferences. We recommend the use of maximum models if researchers have limited information about the pattern of factor loadings and measurement structures. Measurement models are key components of Structural Equation Modeling (SEM); therefore, the findings can be generalized to multilevel SEM and CFA models. Mplus codes are provided for maximum models and other analytical models.en_US
dc.language.isoen_USen_US
dc.subjectmultilevel confirmatory factor analysisen_US
dc.subjectdesign-based approachen_US
dc.subjectmodel-based approachen_US
dc.subjectmaximum modelen_US
dc.subjectlevel-varying factor loadingsen_US
dc.subjectcomplex survey samplingen_US
dc.subjectmeasurementen_US
dc.titleA Solution to Modeling Multilevel Confirmatory Factor Analysis with Data Obtained from Complex Survey Sampling to AvoidConflated Parameter Estimatesen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fpsyg.2017.01464en_US
dc.identifier.journalFRONTIERS IN PSYCHOLOGYen_US
dc.citation.volume8en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department教育研究所zh_TW
dc.contributor.departmentInstitute of Educationen_US
dc.identifier.wosnumberWOS:000411426000001en_US
dc.citation.woscount2en_US
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