Workshops

Workshops

These are stand-alone workshops related to quantitative and qualitative methods. This list is for information purposes only. To add a workshop, contact the Div. 5 website coordinator, Alex Beaujean.

Curran-Bauer Analytics

All workshops are held in Chapel Hill, North Carolina.

For more information or to register, visit the Curran-Bauer Analytics website or contact the instructors.

Structural Equation Modeling

This five-day workshop focuses on the application and interpretation of statistical models that are designed for the analysis of multivariate data, including path analysis, confirmatory factor analysis, structural equation models with latent variables and multiple group structural equation models.

May 13-17, 2019
Instructors: Dan Bauer and Patrick Curran

Longitudinal Structural Equation Modeling

This five-day workshop focuses on the application and interpretation of structural equation models fitted to repeated measures data, including longitudinal measurement models, autoregressive cross-lag models, latent curve models, growth mixture models and latent change score models.

May 20-24, 2019
Instructors: Dan Bauer and Patrick Curran

Network Analysis

This five-day workshop focuses on the application and interpretation of techniques for modeling connections between observations (e.g., actors) within a network, such as social relations among peers, connectivity networks in fMRI data, or symptom networks in diagnostic data.

June 3-7, 2019
Instructors: Dan Bauer and Patrick Curran

Latent Class/Cluster Analysis and Mixture Modeling

This five-day workshop focuses on the application and interpretation of statistical techniques, namely cluster analysis, latent class analysis and mixture models, designed to identify subgroups within a heterogeneous population.

June 10-14, 2019
Instructors: Dan Bauer and Patrick Curran

Multilevel Modeling

This five-day workshop focuses on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures such as individuals clustered within groups (e.g., students within schools) or repeated measures over time (e.g., longitudinal observations within persons).

June 23-28, 2019
Instructors: Dan Bauer and Patrick Curran

Bayesian Statistical Modeling: A First Course

This three-day course is intended as both a theoretical and practical introduction, discussing models up through multiple regression, covering aspects of modeling including model construction, graphical representations of models, practical aspects of Markov chain Monte Carlo (MCMC) estimation, evaluating hypotheses and model-data fit, model comparisons, and modeling in the presence of missing data. For more information or to register visit the workshop's website.

June 10-12
Location: University of Maryland, College Park, Maryland, and online
Instructor: Roy Levy

Bayesian Statistical Modeling: A Second Course

This two-day course assumes experience with introductory level Bayesian statistical modeling, such as that provided in our first course (or analogous coursework), and covers Bayesian approaches to factor analysis, structural equation modeling, multilevel modeling, item response theory, latent class analysis, and Bayesian networks. For more information or to register visit the workshop's website.

June 13-14
Location: University of Maryland, College Park, Maryland, and online
Instructor: Roy Levy

Summer Quantitative Method Series

All workshops are held in at Portland State University, in Portland, Oregon.

For more information or to register, visit the SQMS website or contact Jason Newsom.

Measurement Development and Evaluation

This workshop is designed for those interested in developing attitude, personality, opinion, or other noncognitive scales (i.e., excluding aptitude testing) for use in research studies. Includes best practices in noncognitive scale construction, exploratory and confirmatory factor analysis.

June 16-17, 2019
Instructors: Deborah Bandalos (James Madison University)

Introduction to Linear Models and Beyond with Stata

This course is designed for investigators and analysts in the health, behavioral, and social sciences who are interested in using Stata to analyze their applied research projects. A general introduction to Stata analysis software as well as logistic regression models, multiple regression, and modern missing data handling approaches.

June 18-19, 2019
Instructors: Alan Acock (Oregon State University)

Inter-university Consortium for Political and Social Research (ICPSR)

All workshops are held at the University of Utah (Salt Lake City, UT).

Level, Change, and Acceleration: Modeling Correlated Change in Longitudinal Data and Intensive Repeated Measures Design

June 24 - June 2
Instructor: Pascal Deboeck
More information

Dynamical Systems Analysis

June 17 - June 21
Instructors: Jonathan Butner & Brian Baucom
More Information

Statistical Methods for Sociogenomics and Behavioral Epigenomics

July 1 - July 3
Instructors: Daniel Adkins & Andrey Shabalin
More Information

International Association for Computerized Adaptive Testing

All workshops are held prior to the IACAT conference in Minneapolis, Minnesota.

For more information or to register, visit the IACAT conference workshop website.

Introduction to IRT and CAT

John Barnard (EPEC, Australia) and David J. Weiss (University of Minnesota; Assessment Systems)

Simulations and CAT

Theo Eggen and Angela Verschoor (both of CITO, Netherlands)

Multidimensional CAT

Chun Wang (University of Washington)

Multilevel Structural Equation Modeling

Covers advanced multilevel modeling, including multilevel SEM, with Mplus. For more information or to register visit the workshop's website or contact the organizer.

July 29-Aug. 2, 2019
Location: Philadelphia, PA
Instructors: Kris Preacher

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