Training and Professional Development

  • 5/23 Advances in Latent Variable Modeling using Mplus

     

    One day workshop -- Advances in Latent Variable Modeling using Mplus--- May 23, 2016 at University of Connecticut

    (part of the Modern Modeling Methods conference- www.modeling.uconn.edu )

    Lecturers:  Bengt Muthén, Tihomir Asparouhov and Ellen Hamaker

    This pre-conference workshop has two parts:

    (1) Analyses you probably didn’t know that you could do in Mplus

    * with highlights from the forthcoming book

    Regression and Mediation Analysis Using Mplus (Muthen, B., Muthen, L., & Asparouhov, T.)

    (2) Preview of the forthcoming Mplus Version 8

    Part (1) will sample examples from a range of Mplus topics such as heteroscedasticity modeling for regression and mediation analysis using the CONSTRAINT option and random coefficient modeling; Heckman modeling; Bayesian approach to missing data on binary covariates; Monte Carlo studies of moderated mediation;sensitivity analysis of mediator-outcome confounding; counterfactually-defined causal effect mediation modeling with binary outcomes and binary and nominal mediators; and two-part mediation analysis with floor and ceiling effects.

    Part (2) provides a preview of the forthcoming Mplus Version 8 which features new methods for analyzing intensive longitudinal data such as that obtained from ecological momentary assessments. Long time series for both n=1 and n >1 are discussed. The focus is on time-series analyses with auto-regressive and moving-average components both for observed-variable models such as regression and mediation analysis and for latent-variable models such as factor analysis and structural equation modeling. Applications to be discussed include:

    * multilevel AR(1) model with random mean and random AR parameter

    * latent multilevel AR(1) model with multiple indicators and random innovation error

    * latent multilevel VAR(1) model and dynamical networks

    * dynamic SEM

    * dynamic latent class analysis using hidden Markov and Markov-switching AR(1) models

     

    For information or to register, go to www.modeling.uconn.edu

    For more information, contact: Betsy at betsy@uconn.edu