Formulation and Application of the Generalized Multilevel Facets Model.
In this study, the authors develop a generalized multilevel facets model, which is not only a multilevel and two-parameter generalization of the facets model, but also a multilevel and facet generalization of the generalized partial credit model. Because the new model is formulated within a framewor...
| Published in: | Educational & Psychological Measurement Vol. 67; no. 4; pp. 583 - 606 |
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| Main Authors: | , |
| Format: | Article |
| Published: |
Sage Publications Inc.
August 2007
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=507991923&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507991923 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: August 2007 vid: 67 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 507991923 10.1177/0013164406296974 ppf: 583 ppct: 23 formats: tig: atl: Formulation and Application of the Generalized Multilevel Facets Model. aug: au: Wang, Wen-Chung Liu, Chih-Yu su: Psychiatric rating scales Test scoring Item response theory sug: subj: Psychiatric rating scales Test scoring Item response theory ab: In this study, the authors develop a generalized multilevel facets model, which is not only a multilevel and two-parameter generalization of the facets model, but also a multilevel and facet generalization of the generalized partial credit model. Because the new model is formulated within a framework of nonlinear mixed models, no efforts are needed to develop parameter estimation procedures, and existing computer programs can be directly applied. Through simulations, the authors found that the parameters in the generalized multilevel facets model could be recovered fairly well using the SAS NLMIXED procedure. To illustrate applications of the new model, a real data set about ratings of household appliances was analyzed with gender, age, and education level as the Level 2 predictors. Further model generalization is discussed. 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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