 Schultzberg, M. & Muthén, B. (2017). Number of subjects and time points needed for multilevel time series analysis: A Monte Carlo study of dynamic structural equation modeling. Supplementary material is found here. Paper referred to in Part 7 of the August 18 DSEM workshop.
 Asparouhov, T, & Muthén, B. (2017). PriorPosterior Predictive Pvalues. Mplus Web Notes: No. 22. April 27, 2017. Version 2. (Download Mplus analyses)
 Asparouhov, T., Hamaker, E.L. & Muthen, B. (2017). Dynamic structural equation models. Technical Report. Version 2. (Download Mplus analyses)
 Hamaker, E.L., Asparouhov, T., Brose, A., Schmiedek, F. & Muthen, B. (2017). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Submitted to Multivariate Behavioral Research.
 Asparouhov, T., Hamaker, E.L. & Muthen, B. (2017). Dynamic Latent Class Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24:2, 257269, DOI: 10.1080/10705511.2016.1253479
 Muthén, B. & Asparouhov, T. (2016). MultiDimensional, MultiLevel, and MultiTimepoint Item Response Modeling. In van der Linden, W. J., Handbook of Item Response Theory. Volume One. Models, pp. 527539. 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 Mplus scripts.
 Asparouhov, T., Muthén, B. & Morin, A. J. S. (2015). Bayesian structural equation modeling with crossloadings and residual covariances: Comments on Stromeyer et al. Journal of Management, 41, 15611577.
 Asparouhov, T. & Muthen, B. (2015). Residual associations in latent class and latent transition analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22:2, 169177, DOI: 10.1080/10705511.2014.935844.
Download Mplus files.
 Muthén, B. & Asparouhov T. (2015). Growth mixture modeling with nonnormal 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), 1223. DOI:10.1080/10705511.2014.935843
 Asparouhov, T. & Muthén B. (2015). Structural equation models and mixture models with continuous nonnormal skewed distributions. Structural Equation Modeling: A Multidisciplinary Journal, DOI:
10.1080/10705511.2014.947375. Download Mplus inputs and outputs.
 Asparouhov, T. & Muthén, B. (2015). 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 realworld applications. Charlotte, NC: Information Age Publishing, Inc.
Mplus scripts.
 Asparouhov & Muthén (2014). Variablespecific 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.
 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: Threestep approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 329341. 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). Multiplegroup factor analysis alignment. Structural Equation Modeling: A Multidisciplinary Journal, 21:4, 495508. 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.
