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  Model fit after pairwise maximum likelihood

Barendse, M. T., Ligtvoet, R., Timmerman, M. E., & Oort, F. J. (2016). Model fit after pairwise maximum likelihood. Frontiers in Psychology, 7: 528. doi:10.3389/fpsyg.2016.00528.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0007-6621-1 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0007-6622-0
Genre: Zeitschriftenartikel

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Barendse_etal_2016_Model fit after pairwise maximum likelihood.pdf (Verlagsversion), 155KB
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https://hdl.handle.net/21.11116/0000-0007-6623-F
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Barendse_etal_2016_Model fit after pairwise maximum likelihood.pdf
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© 2016 Barendse, Ligtvoet, Timmerman and Oort. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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 Urheber:
Barendse, M. T.1, Autor           
Ligtvoet, R., Autor
Timmerman, M. E., Autor
Oort, F. J., Autor
Affiliations:
1Ghent University, Ghent, Belgium, ou_persistent22              

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 Zusammenfassung: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log–likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two–way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations.

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Sprache(n): eng - English
 Datum: 2016-04-21
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3389/fpsyg.2016.00528
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Titel: Frontiers in Psychology
  Kurztitel : Front Psychol
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Pully, Switzerland : Frontiers Research Foundation
Seiten: - Band / Heft: 7 Artikelnummer: 528 Start- / Endseite: - Identifikator: ISSN: 1664-1078
CoNE: https://pure.mpg.de/cone/journals/resource/1664-1078