Data Analysis Training Institute of Connecticut 2016 Workshops
We are pleaseed to announce 5 workshops in quantitative data analysis in June 2016: Hierarchical Linear Modeling, Dyadic Analysis, Structural Equation Modeling Using Mplus, Generalized Linear Mixed Models, and Longitudinal Modeling Using Mplus. Please see www.datic.uconn.edu for costs.
Hierarchical Linear Modeling (HLM)
June 06-10, 2016
Instructor: D. Betsy McCoach & Ann A. O'Connell
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 SPSS (or SAS). Analyses will be demonstrated using the software HLMv7. Instruction will consist of lectures, computer workshops, and individualized consultations. The workshop emphasizes practical applications and places minimal emphasis on statistical theory.
Dyadic Analysis
June 13-17, 2016
Instructors: David A. Kenny & Randi Garcia
The workshop on dyadic data analysis will focus on data where both members of a dyad are measured on the same set of variables. Among the topics to be covered are the measurement of nonindependence, the actor-partner interdependence model, the analysis of distinguishable and indistinguishable dyads, mediation and moderation of dyadic effects, and over-time analyses of dyadic data. The software package used in the workshop will be SPSS, but there will be discussion of other packages (e.g., HLM) and structural equation modeling. 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 or analysis of variance as well as SPSS. See schedule!
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
Instructors: 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.
You may register now at https://www.regonline.com/datic
For more information, contact: Betsy at betsy@uconn.edu