Recent Papers

• Muthén, B. & Asparouhov, T. (2019). What multilevel modeling can teach us about single-level modeling: Latent transition analysis with random intercepts (RI-LTA). Submitted for publication.

• Hamaker, E. & Muthén, B. (2019). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Accepted for publication in Psychological Methods.

• Asparouhov, T. & Muthén, B. (2019). Nesting and equivalence testing for structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 26:2, 302-309, DOI: 10.1080/10705511.2018.1513795. (Download scripts).

• Asparouhov, T. & Muthén, B. (2019). Latent variable centering of predictors and mediators in multilevel and time-series models. Structural Equation Modeling: A Multidisciplinary Journal, 26, 119-142. DOI: 10.1080/10705511.2018.1511375 (Download scripts).

• Asparouhov, T. & Muthén, B. (2018). Continuous - Time Survival Analysis in Mplus. Version 3. June 29, 2018. (Download scripts).

• Asparouhov, T. & Muthén, B. (2018). Comparison of models for the analysis of intensive longitudinal data. Accepted for publication in Structural Equation Modeling: A Multidisciplinary Journal. Version 3, May 15, 2019. (Download scripts).

• Asparouhov, T. & Muthén, B. (2018). SRMR in Mplus. Technical Report. May 2, 2018.

• Hamaker, E.L., Asparouhov, T., Brose, A., Schmiedek, F. & Muthen, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, DOI: 10.1080/00273171.2018.1446819

• Asparouhov, T., Hamaker, E.L. & Muthen, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25:3, 359-388, DOI: 10.1080/10705511.2017.1406803 (Download Mplus analyses).

• Schultzberg, M. & Muthén, B. (2018). Number of subjects and time points needed for multilevel time series analysis: A simulation study of dynamic structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25:4, 495-515, DOI:10.1080/10705511.2017.1392862. (Supplementary material). Paper referred to in Part 7 of the August 18 DSEM workshop.

• Asparouhov, T, & Muthén, B. (2017). Prior-Posterior Predictive P-values. Mplus Web Notes: No. 22. April 27, 2017. Version 2. (Download Mplus analyses).

• Asparouhov, T., Hamaker, E.L. & Muthen, B. (2017). Dynamic Latent Class Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24:2, 257-269, DOI: 10.1080/10705511.2016.1253479

• Asparouhov, T. & Muthén, B. (2016). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. In Harring, J. R., & Stapleton, L. M., & Beretvas, S. N. (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applications (pp. 163-192). Charlotte, NC: Information Age Publishing, Inc. (Download scripts).

• Muthén, B. & Asparouhov, T. (2016). Multi-Dimensional, Multi-Level, and Multi-Timepoint Item Response Modeling. In van der Linden, W. J., Handbook of Item Response Theory. Volume One. Models, pp. 527-539. Boca Raton: CRC Press. (Download output files). (Download table 6 output).

• Muthén, B. & Asparouhov, T. (2016). Recent methods for the study of measurement invariance with many groups: Alignment and random effects. (Download scripts).

• Asparouhov, T., Muthén, B. & Morin, A. J. S. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. Journal of Management, 41, 1561-1577.

• Asparouhov, T. & Muthen, B. (2015). Residual associations in latent class and latent transition analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22:2, 169-177, DOI: 10.1080/10705511.2014.935844. (Download scripts).

• Muthén, B. & Asparouhov T. (2015). Growth mixture modeling with non-normal distributions. Statistics in Medicine, 34:6, 1041–1058. DOI: 10.1002/sim6388

• Muthén, B. & Asparouhov, T. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 12-23. DOI:10.1080/10705511.2014.935843

• Asparouhov, T. & Muthén B. (2015). Structural equation models and mixture models with continuous non-normal skewed distributions. Structural Equation Modeling: A Multidisciplinary Journal, DOI:10.1080/10705511.2014.947375. (Download Mplus inputs and outputs).

• Asparouhov & Muthén (2014). Variable-specific entropy contribution. Technical appendix.

• Asparouhov, T. & Muthén, B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. Web note 21. May 14, 2014. Revised July 16, 2018.

• Muthén, B. & Asparouhov, T. (2014). IRT studies of many groups: The alignment method. Frontiers in Psychology, Volume 5, DOI: 10.3389/fpsyg.2014.00978

• Asparouhov, T. & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 329-341. The posted version corrects several typos in the published version. An earlier version of this paper was posted as web note 15. (Download appendices with Mplus scripts).

• Asparouhov, T. & Muthén, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling: A Multidisciplinary Journal, 21:4, 495-508. DOI: 10.1080/10705511.2014.919210. An earlier version of this paper was posted as web note 18. (Download Mplus input, output, and data files).

• Asparouhov, T. & Muthén, B. (2014). Using Mplus individual residual plots for diagnostic and model evaluation in SEM. Web note 20.

• Version 7 papers.