Webinárok

Visualizing Regression and Moderation Results in Stata

Marvin Hanisch, PhD

Date:

Tuesday, 07. July 2026
10:00 – 11:30 CET (UTC+01:00)
Free | online
Language of the webinar: English

Abstract:

Effective visualizations can bring your statistical analyses to life. In this hands-on and interactive webinar, you will learn how to use Stata’s margins and marginsplot commands to make your regression results more accessible.

We will work through three visualization challenges. First, we will plot the results of an ordinary least squares (OLS) linear regression model. Second, we will discuss two ways to visualize interaction terms, which are central to moderation hypotheses. Third, we will explore visualizing nonlinear models, such as logit regressions.

By the end, you will be able to produce polished, journal-quality graphics and explain when, where, and for whom effects matter.

Register now and get more out of Stata!

Working with Stata Frames

Dr. Asjad Naqvi
06. March 2026

Abstract:

Frames are a relatively new and powerful feature in Stata that allow users to work with multiple datasets simultaneously within a single session, to streamline data management and empirical workflows. This webinar introduces the core concepts of frames, such as creating, linking, and switching between datasets, and demonstrates how they can replace repetitive merges and creation of temporary datasets. Using practical examples, we show how frames support cleaner and more efficient handling of complex data structures, improve transparency and reproducibility of empirical analyses and results, and facilitate the development of more modular and efficient Stata programs and packages.

Conditional Average Treatment Effects Estimation Using Stata 

Di Liu  – StataCorp LLC.
04. September 2025

Abstract

Treatment effects estimate the causal effects of a treatment on an outcome. These effects may be heterogeneous. Average treatment effects conditional on a set of variables (CATEs) help us understand such heterogeneous treatment effects and, by construction, are useful for evaluating how different treatment-assignment policies impact various groups within a population. 

A must-attend for anyone working with causal inference, policy evaluation, or Stata-based data analysis.