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
Multiple indicator growth mixture models: a statistical simulation to evaluate performance for social science analysis
Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource, 213 S.
Language
Deutsch
Notes
Veröffentlichungsversion
begutachtet

Classification
Sozialwissenschaften, Soziologie, Anthropologie

Event
Veröffentlichung
(where)
Stuttgart
(when)
2019
Creator

DOI
10.18419/opus-10420
URN
urn:nbn:de:0168-ssoar-64291-0
Rights
Open Access; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:58 AM CEST

Data provider

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Associated

Time of origin

  • 2019

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