Collection article | Sammelwerksbeitrag
Effect Comparison in Multilevel Structural Equation Models with Non-Metric Outcomes
This study discusses difficulties of effect comparisons in multilevel structural equation models with non-metric outcomes, such as nonlinear dyadic mixed-effects regression. In these models, the fixation of the level-1 error variances induces substantial drawbacks in the context of effect comparisons which align with the well-known problems of standard single- and multilevel nonlinear models. Specifically, the level-1 and level-2 coefficients as well as the level-2 variance components are implicitly rescaled by the amount of unobserved level-1 residual variation and thus may apparently differ across (and within) equations despite of true effect equality. Against this background, the present study discusses a multilevel extension of the method proposed by Sobel and Arminger (1992) with which potential differences in level-1 residual variation can be taken into account through the specification of non-linear parameter constraints. The problems of effect comparisons in multilevel probit SEM's and the proposed correction method are exemplified with a simulation study.
- Umfang
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Seite(n): 3892-3901
- Sprache
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Englisch
- Anmerkungen
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Status: Veröffentlichungsversion; nicht begutachtet
- Erschienen in
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JSM 2016 Proceedings, Social Statistics Section
- Thema
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Sozialwissenschaften, Soziologie
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
statistische Analyse
statistische Methode
Simulation
Mehrebenenanalyse
multivariate Analyse
Modellvergleich
empirische Sozialforschung
- Ereignis
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Geistige Schöpfung
- (wer)
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Kern, Christoph
Stein, Petra
- Ereignis
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Veröffentlichung
- (wer)
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American Statistical Association
- (wo)
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Vereinigte Staaten von Amerika, Alexandria, VA
- (wann)
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2016
- URN
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urn:nbn:de:0168-ssoar-50108-5
- Rechteinformation
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
- Letzte Aktualisierung
-
21.06.2024, 16:26 MESZ
Datenpartner
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Sammelwerksbeitrag
Beteiligte
- Kern, Christoph
- Stein, Petra
- American Statistical Association
Entstanden
- 2016
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