DATIC (www.datic.uconn.edu<http://www.datic.uconn.edu>) is offering 4 workshops at the University of Connecticut in June, 2018: Mixture Modeling, Introduction to Data Analysis in R, Multilevel Modeling in R, and Dyadic Analysis with R. Registration is now open. Go to www.datic.uconn.edu<http://www.datic.uconn.edu> for more information and to register for the workshops.
Mixture Modeling June 4-6, 2018 Dr. Eric Loken This 3-day mixture modeling workshop will survey techniques for exploring heterogeneous latent structure in data. We will begin by defining a variety of mixture models. The main focus will be on latent class analysis (LCA) and latent profile analysis (LPA), with applications in health and education. Additional models will include mixture regression models, mixture IRT, k-means clustering, and growth mixture models for longitudinal data. The course will emphasize hands-on work by participants, who will also be encouraged to make connections to their own data, learning to execute many of these models in R. Particular attention will be paid to issues that arise in applied settings including model assumptions, parameter estimation, and interpretation. Introduction to Data Analysis in R Instructor: Dr. Randi L. Garcia Two separate sessions of the R workshop are being offered. Session 1: June 7 – June 8, 2018 · Thursday and Friday prior to Multilevel Modeling with R Workshop Session 2: June 21 – June 22, 2018 · Thursday and Friday prior to Dyadic Data Analysis with R workshop Are you curious about using R for data analysis? Have you been thinking about making the switch to R, but don’t know where to start? This two-day workshop is the perfect quick start guide to analyzing your data with R. We will cover the fundamentals of data analysis in R with a special focus on translating your existing knowledge and skills from other software (e.g., SPSS) into R. The goal of this workshop is to develop proficiency in R for data preparation and preliminary data analysis. We will build confidence in importing data from different sources into RStudio and getting that data ready for any advanced technique you might then employ. Among the topics to be covered are intro to the RStudio environment, packages, and RMarkdown, data manipulation, data visualization, correlations, reliability tests, basic inference tests, ANOVA, linear regression, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and more. Instruction on the specific statistics and statistical models will be minimal to zero. It is assumed that you already know how to do these analyses, but you want to see how to do them in R. You do not need to be registered for any other DATIC workshops to enroll in the 2 day Introduction to Data Analysis in R workshop. Multilevel Modeling Using R Workshop June 11-15, 2018 Drs. D. Betsy McCoach & Randi Garcia This workshop covers the basics and applications of multilevel modeling with extensions to more complex designs. Participants will learn how to analyze both organizational and longitudinal (mostly 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. The emphasis will be practical with minimal emphasis on statistical theory, but those seeking more statistical information can arrange an individualized session with the instructors. All analyses will be demonstrated using R. Instruction will consist of lectures, computer demonstrations of data analyses, and hands-on opportunities to analyze practice data sets using R. The workshop emphasizes practical applications and places minimal emphasis on statistical theory. No prior familiarity with R is required, but if you have never used R and want to gain a general proficiency working with data in R, we encourage you to take the two-day DATIC Intro to R and RStudio workshop held on Thursday, June 7, through Friday, June 8, 2018. Dyadic Data Analysis with R June 25 – June 29, 2018 Instructors: Drs. Randi L. Garcia and David A. Kenny The Dyadic Data Analysis workshop focuses on the analysis of dyadic data when both members of a dyad are measured on the same variables. All analyses will use multilevel modeling in R via the RStudio graphical interface. Participants will learn how to analyze dyadic data and to interpret the results from their analyses. Among the topics to be covered are the vocabulary of dyadic analysis, non-independence, data structures, and the Actor-Partner Interdependence Model. We also discuss mediation and moderation of dyadic effects. On day 4, participants choose from one of two break-out sessions: 1) the analysis of over-time dyadic data (e.g., growth curve models) or 2) dyadic data analysis with SEM using the lavaan R package (e.g., Actor‑Partner Interdependence Model and Common Fate Model). The discussion of over‑time data is limited to one day so the workshop should not be construed as workshop on longitudinal dyadic analysis. Participants should have a working knowledge of multiple regression. No prior familiarity with R is required, but if you have never used R and want to gain a general proficiency working with data in R, we encourage you to take the two-day DATIC Intro to R and RStudio workshop. D. Betsy McCoach Professor, Measurement, Evaluation, and Assessment program Department of Educational Psychology University of Connecticut 249 Glenbrook Road, Unit 3064 Storrs, CT 06269-3064 860-486-0183 be...@uconn.edu<mailto:be...@uconn.edu> Here’s a link to my newest article with former student Jessica Flake (she is first author, and it is her dissertation): http://www.tandfonline.com/eprint/tPmpDnkNXfpfPMrXfJ3N/full [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
