Workshop: Stata (Advanced)

Stata provides an exceptionally large number of statistical applications. Because of its open programmability, new statistical developments are quickly implemented in the software. On the other hand, the standardized syntax allows for a quick acquisition of new statistical models. In the workshop, we will learn advanced techniques of data analysis and graph construction. This includes the principles of statistical model development.

Target group 

Scientists in different disciplines who already have a basic knowledge of Stata and want to use Stata to apply quantitative social science methods.

Goals

This workshop is designed to enable participants to individually create statistical models for social science research questions, develop and test them systematically, and prepare their results graphically for publications. Participants should have knowledge of the basic commands regarding data management, descriptive analysis and OLS regression. The workshop is intended as a follow-up to the “Stata basic course”. We will use example datasets, but participants will also have the opportunity to work on their own data.

Content

• advanced variable generation and recoding
• combination of datasets and different data formats (long and wide format, episode data)
• iteration loops
• advanced graphs and graph modification
• working with saved results and local macros
• further regression models and regression diagnostics
• factor variables and interaction effects
• model construction and development
• output of regression results in tables and graphs (marginal effects)
• regression with complex samples

Literature

• Kohler, Ulrich / Kreuter, Frauke (2012): Data Analysis Using Stata, Stata Press.
• Hamilton, Lawrence C. (2013): Statistics with Stata, Cengage.
• Bittmann, Felix (2019): Stata. A really Short Introduction, Walter de Gruyter.

In cooperation with

Competence Center for Empirical Research Methods

• advanced variable generation and recoding
• combination of datasets and different data formats (long and wide format, episode data)
• iteration loops
• advanced graphs and graph modification
• working with saved results and local macros
• further regression models and regression diagnostics
• factor variables and interaction effects
• model construction and development
• output of regression results in tables and graphs (marginal effects)
• regression with complex samples