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Original Articles

Sample Size Requirements in Single- and Multiphase Growth Mixture Models: A Monte Carlo Simulation Study

Pages 457-476 | Published online: 31 Jul 2012

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (7)

Benedikt Bernd Claus, Norbert Scherbaum, Michael Specka, Patrik Roser & Udo Bonnet. (2023) Exploratory Growth Mixture Modeling of Cannabis-Withdrawal Syndrome Trajectories of Adult Pure Cannabis Dependents During Detoxification: Two Subtypes?. Journal of Psychoactive Drugs 0:0, pages 1-12.
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Katharina Senger, Julian A. Rubel, Maria Kleinstäuber, Annette Schröder, Katharina Köck, Michael J. Lambert, Wolfgang Lutz & Jens Heider. (2022) Symptom change trajectories in patients with persistent somatic symptoms and their association to long-term treatment outcome. Psychotherapy Research 32:5, pages 624-639.
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Gavin van der Nest, Valeria Lima Passos, Math J. J. M. Candel & Gerard J. P. van Breukelen. (2022) Model fit criteria curve behaviour in class enumeration – a diagnostic tool for model (mis)specification in longitudinal mixture modelling. Journal of Statistical Computation and Simulation 92:8, pages 1640-1672.
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Yan Wang, Hsien-Yuan Hsu & Eunsook Kim. (2021) Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal 28:5, pages 716-724.
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Daniel P. Martin & Timo von Oertzen. (2015) Growth Mixture Models Outperform Simpler Clustering Algorithms When Detecting Longitudinal Heterogeneity, Even With Small Sample Sizes. Structural Equation Modeling: A Multidisciplinary Journal 22:2, pages 264-275.
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Su-Young Kim. (2014) Determining the Number of Latent Classes in Single- and Multiphase Growth Mixture Models. Structural Equation Modeling: A Multidisciplinary Journal 21:2, pages 263-279.
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Yo In’nami & Rie Koizumi. (2013) Review of Sample Size for Structural Equation Models in Second Language Testing and Learning Research: A Monte Carlo Approach. International Journal of Testing 13:4, pages 329-353.
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