Data Analysis Training Institute of Connecticut – 2017 Workshops
Structural Equation Modeling using Mplus
June 5^{th}9^{th}, 2017 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. Longitudinal Modeling using MPlus June 15^{th}17^{th}, 2017 Instructor: D. Betsy McCoach During this a threeday workshop, students will learn how to model longitudinal data using Mplus. The workshop focuses on fitting and interpreting autoregressive and growth curve models in Mplus. Specifically, we will cover linear, polynomial, multiphase, nonlinear growth curve models, multivariate growth curve models, autoregressive models, and hybrid autoregressive/growth models for both observed variables and latent constructs. Some prior knowledge and experience in Structural Equation Modeling is recommended. Dyadic Data Analysis Using Multilevel Modeling with R June 19^{th}23^{rd}, 2017 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 actorpartner interdependence model, the analysis of distinguishable and indistinguishable dyads, mediation and moderation of dyadic effects, and overtime analyses of dyadic data. All analyses will be conducted using R, but no prior knowledge or experience with R is required. Participants are expected to have a working knowledge of multiple regression or analysis of variance. Multilevel Modeling Using HLM July 17^{th}21^{st}, 2017 Instructor: D. Betsy McCoach This 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 experience using statistical software (such as SPSS, SAS, R, Stata). All analyses will be demonstrated using the software HLMv7. Instruction will consist of lectures, computer demonstrations of data analyses, and handson opportunities to analyze practice data sets using HLM. The workshop emphasizes practical applications and places minimal emphasis on statistical theory. The workshop takes place in a computer lab, so you do not need to bring a laptop or software.
