Each HLM workshop covers basics and applications of multilevel modeling with extensions to more complex designs.  Participants will learn how to analyze both organizational and longitudinal (growth curve) data using multilevel modeling and to interpret the results from their analyses.  Although the workshop does not require any prior knowledge or experience with multilevel modeling, participants are expected to have a working knowledge of multiple regression as well as some familiarity using SPSS (or other software package such as SAS) to manipulate data. Analyses will be demonstrated using the software HLMv7. Instruction will consist of lectures, guided use of the software during computer labs, and individualized consultations. Participants are encouraged to bring their own data to the workshop. Our training emphasizes practical applications and places limited emphasis on statistical theory.

Introduction to Structural Equation Modeling using Mplus

June 20-24, 2016

Instructor: D. Betsy McCoach

This introductory workshop on Structural Equation Modeling covers basics of path analysis, confirmatory factor analysis, and latent variable modeling. Using Mplus, participants will learn how to build, evaluate, and revise structural equation models. Although the workshop does not require any prior knowledge or experience with SEM, participants are expected to have a working knowledge of multiple regression, as well as some experience using a statistical software program such as SPSS.

Generalized Linear Mixed Models (GLMM)

June 27-28, 2016

Instructor: Ann A. O’Connell

This short-course (2 days) covers extensions of mixed and hierarchical linear models for outcome variables that represent dichotomous, ordinal, multinomial, or count data. We being with a review of single-level generalized linear models in terms of estimation, interpretation, and model fit. We then expand on this foundation to build and interpret generalized linear mixed models. Emphasis will be on model building, interpretation, comparison, and the use of analysis adjustments for the limited nature of these kinds of dependent variables. Emphasis is on application and interpretation, with hands-on analyses and examples from the education, health, and social/behavioral sciences literature. Participant background should include regression, analysis of variance, and some exposure to multilevel modeling. Software for examples will include HLM, SAS, and SPSS.

Longitudinal Modeling using Mplus

June 29-July 1, 2016

Instructor: D. Betsy McCoach

During this three day workshop, students will learn how to model longitudinal data using Mplus. The main focus of the workshop focuses is on fitting growth curve models in Mplus. Specifically, we will cover linear, polynomial, multiphase, non-linear growth curve models, and multivariate growth curve models, and latent change score models for both observed variables and latent constructs.

Topics to be covered include

• Time unstructured vs. Time structured data
• Centering and coding variables for longitudinal models
• Estimating Linear growth curve models
• Time Varying Covariates
• Estimating multiphase growth curve models
• Estimating Polynomial growth curve models
• Latent Basis growth curve Models
• Estimating other types of non-linear growth curve models
• Latent Change Score Models
• Multivariate Models
• Factorial invariance across time

2016 DATIC Workshops (See www.datic.uconn.edu )