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Scientific Coordination

Alisa Remizova

Administrative Coordination

Janina Götsche

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Fundamentals and Advanced Topics in Modeling Interaction Effects

About
Location:
Online via Zoom
 
General Topics:
Data Analysis
Course Level:
Beginner
Format:
Workshop
Software used:
R, Stata
Duration:
2 days
Language:
English
Fees:
Students: 220 €
Academics: 330 €
Commercial: 660 €
 
Keywords
Additional links
Lecturer(s): Janina Beiser-McGrath, Liam F. Beiser-McGrath

About the lecturer - Janina Beiser-McGrath

About the lecturer - Liam F. Beiser-McGrath

Course description

Many social phenomena that we study in the social sciences follow an interaction logic. That means that the effect of an explanatory variable on an outcome differs depending on the value of a third variable. For example, the degree to which citizens are convinced by political messaging may depend on their party preference or their education.
 
This course will introduce students to best practices for modeling interaction effects in quantitative data and equip students with tools to visualize interaction effects using state-of-the-art graphical approaches. In detail, we will talk about how to include and interpret interaction terms in regression models, about other ways in which interaction logics can be included in regressions, and about how to visualize these effects to help interpret and communicate interaction effects in the data.
 
The course will also deal with advanced and cutting-edge topics in modeling interactions. In interaction models, the control strategy is very important in order for the interaction of interest to not erroneously reflect the effect of other interaction terms or nonlinear effects that are omitted from the statistical model. Participants will learn intuitive as well as advanced strategies for avoiding misattribution in interaction models, the latter in the form of regularized estimators such as the adaptive Lasso.
 
Finally, interaction effects are not always linear. Instead, it is possible that the effect of an explanatory variable varies across the values of a moderating variable in a nonlinear, for example, a U-shaped pattern. We will learn how to model and visualize nonlinear interactions and avoid erroneously inferring a nonlinear interaction pattern when there is none.
 
This course will consist of a mix of lectures and hands-on computer labs, where students can apply the learned material to data on society and politics.
 
 
Please note that this course will be taught using both Stata as well as R and RStudio. Participants can choose the preferred software they want to use during the workshop. However, only R will be available for advanced topics like nonlinear interactions and more advanced approaches to control strategies in interaction models. Here, Stata users will learn how to migrate their data to R and how to implement these specific techniques in R, so no previous knowledge of R will be necessary. We will also assist students in installing R, RStudio, and any needed R packages.
 
Organizational structure of the course 
Each workshop day will consist of lectures and hands-on computer labs where students will learn how to analyze and visualize interaction effects in their chosen software, either in Stata or in R. The computer labs will offer instruction and lab materials as well as the opportunity to apply the learned techniques to real-world data and problems, with the guidance and support of the instructors. During the computer labs, participants will work on exercises analyzing data, generating graphs, and solving problems in groups and individually.


Target group

Participants will find the course useful if:
  • They plan to analyze quantitative data in a social science discipline with interactions and nonlinear effects


    • Learning objectives

    By the end of the course, participants will:
  • Gain a deep understanding of how to analyze and model interaction effects in a regression framework
  • Be able to visualize interaction effects using their software of choice
  • Be able to communicate findings on interaction effects in academic publications
  • Gain a deep understanding of best practices and cutting-edge control strategies for interaction models


    • Prerequisites

  • An initial understanding of regression models
  • Interest and previous experience working with quantitative data
  • Initial knowledge of R or Stata for data management and analysis (basic knowledge of R is recommended but not required)
    •  
    Software and hardware requirements
    All students should have R and RStudio installed, as well as Stata if they prefer to use Stata. We will provide instructions for installing R and RStudio prior to the course and also offer assistance at the beginning of the course. Stata can be used for modeling, interpreting, and visualizing interactions and nonlinearities. For some more advanced topics like nonlinear interactions and more advanced approaches to control strategies in interaction models, only R will be available. Here, Stata users will learn how to migrate their data to R and how to implement these specific techniques in R, so no previous knowledge of R will be necessary.  We will also assist students in the installation of any needed R packages.
     
    If you need Stata, please let us know the latest two weeks in advance of the course start so that we can take care of it.


    Schedule

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