Testing Latent Mean Differences Between Observed and Unobserved Groups Using Multilevel Factor Mixture Models.
When assessing latent mean differences, researchers frequently do not explore possible heterogeneity within their data sets. Sources of differences may be functions of a nested data structure or heterogeneity in the form of unobserved classes of observations defined by a difference in factor means....
| Publicado en: | Educational & Psychological Measurement Vol. 68; no. 3; pp. 357 - 379 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sage Publications Inc.
June 2008
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| 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=508070784&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508070784 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: N pubinfo: dt: June 2008 vid: 68 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 508070784 10.1177/0013164407312600 ppf: 357 ppct: 22 formats: tig: atl: Testing Latent Mean Differences Between Observed and Unobserved Groups Using Multilevel Factor Mixture Models. aug: au: Allua, Shane Stapleton, Laura M. Beretvas, S. Natasha su: Observation (Psychology) Structural equation modeling Statistical significance sug: subj: Observation (Psychology) Structural equation modeling Statistical significance ab: When assessing latent mean differences, researchers frequently do not explore possible heterogeneity within their data sets. Sources of differences may be functions of a nested data structure or heterogeneity in the form of unobserved classes of observations defined by a difference in factor means. In this study, the use of multilevel structural equation models in combination with factor mixture models (FMMs) for assessing latent mean differences is discussed. Interpretation of single- and multilevel model parameter estimates when comparing latent means for observed and unobserved groups is demonstrated using a large-scale data set in which students are clustered within schools. Methodological dilemmas are discussed, and directions for future research with respect to multilevel FMMs are suggested. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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