220078 SE SE Advanced Data Analysis 3 (2021W)

Course Objectives and Summary:

This is an interdisciplinary data analysis seminar in workshop form focused on the application of principles of linear modeling in the context of linear regression analysis to exploring questions about mediated (i.e., indirect) and moderated (i.e., interaction) effects. We will spend part of the course talking about partitioning effects into direct and indirect components and how to quantify and test hypotheses about indirect effects, part talking about estimating, testing, and probing interactions in linear models, and part integrating moderation and mediation as conditional process analysis by discussing and how to conceptualize and test the contingencies of a mechanism (moderated mediation) and whether moderated effects are mediated (mediated moderation). Computer applications will focus on R statistical language. No knowledge of matrix algebra is required or assumed, yet a moderate proficiency in R and R programming is preferred.

By the end of this course, participants will be able to:
- interpret the results of basic moderation and mediation models within regression framework
- know how test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators,
- have the ability to visualize and probe interactions in regression models in order to interpret interaction effects in the appropriate ways,
- have learned how to estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects,
- and use R language to run and understand moderation, mediation, and conditional process models.

Following the University motto “welcome (back) to the University of Vienna”, this is an on-site course.