The Sampling Ratio in Multilevel Structural Equation Models: Considerations to Inform Study Design.
Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the resu...
| Publicado en: | Educational & Psychological Measurement Vol. 82; no. 3; pp. 409 - 444 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sage Publications Inc.
Jun2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=156316967&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 156316967 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Jun2022 vid: 82 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 156316967 10.1177/00131644211020112 ppf: 409 ppct: 35 formats: tig: atl: The Sampling Ratio in Multilevel Structural Equation Models: Considerations to Inform Study Design. aug: au: Kush, Joseph M. Konold, Timothy R. Bradshaw, Catherine P. affil: University of Virginia, Charlottesville, VA, USA su: Analysis of variance Structural equation modeling Experimental design Effect sizes (Statistics) Regression analysis Sampling errors Factor analysis Statistical correlation Measurement errors sug: subj: Analysis of variance Structural equation modeling Experimental design Effect sizes (Statistics) Regression analysis Sampling errors Factor analysis Statistical correlation Measurement errors keyword: doubly latent interchangeability and exchangeability multilevel sampling and measurement error sampling ratio structural equation model doubly latent interchangeability and exchangeability multilevel sampling and measurement error sampling ratio structural equation model ab: Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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