Multiple Indicator Growth Mixture Models: eine statistische Simulation zur Performanzevaluation für sozialwissenschaftliche Analysen
Abstract: Multiple Indicator Growth Mixture Models (MIGMM) combine the design principles of latent measurement models, growth curve models, and latent class analysis. MIGMMs are thus analytical tools for empirical social research, which consider the measurements as latent constructs and simultaneously allow post-hoc identification and description of group differences with respect to temporal change. By identifying unobserved subpopulations, social change processes and their differences between and within the unobserved subpopulations can be investigated. While simple Growth Mixture Models, based on manifest variables, have already been evaluated in numerous Monte Carlo studies, a systematic analysis of the performance of multiple indicator GMMs is still lacking. This simulation study aims to systematically evaluate the performance of MIGMMs under different data situations, focusing in particular on temporal, group-specific and combined invariance violations of the latent measurement models.
- Alternative title
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Multiple indicator growth mixture models: a statistical simulation to evaluate performance for social science analysis
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource, 213 S.
- Language
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Deutsch
- Notes
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Veröffentlichungsversion
begutachtet
- Classification
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Sozialwissenschaften, Soziologie, Anthropologie
- DOI
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10.18419/opus-10420
- URN
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urn:nbn:de:0168-ssoar-64291-0
- Rights
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Open Access; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:58 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
Time of origin
- 2019