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