Further exploration of the effects of time-varying covariate in growth mixture models with nonlinear trajectories
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology
Link
https://link.springer.com/content/pdf/10.3758/s13428-023-02183-5.pdf
Reference61 articles.
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2. Bakk, Z., & Kuha, J. (2018). Two-step estimation of models between latent classes and external variables. Psychometrika, 83, 871–892. https://doi.org/10.1007/s11336-017-9592-7
3. Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L., & Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92(440), 1375–1386. https://doi.org/10.1080/01621459.1997.10473658
4. Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8(3), 338–363. https://doi.org/10.1037/1082-989X.8.3.338
5. Bishop, C. (2006). Pattern recognition and machine learning. Springer-Verlag.
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