Introduction to Stata for Data Management and Analysis

online course

This online short course will give a thorough introduction to the software Stata. It is tailored to the needs of academics and other research practitioners who are new to Stata or who wish to refresh their skills. The course will not cover basic statistical methods and their underlying mathematics but will focus on their practical application using electoral research data in Stata.

In the first part of the course, we will cover the program's interface and introduce its syntax structure and basic rules for writing clean and reproducible Stata code. Subsequently, we will provide you with skills in hands-on data management, common data analyses, and the visualization of results. Depending on the participants' prior knowledge, we might provide further insight into the automatization of data wrangling and analysis procedures and the export of publication-ready results. Additionally, we will review available help and support features (online and offline) to equip participants with the necessary knowledge to further develop their skills and solve occurring problems. This course will be interactive and case-based in nature.

Course leader

Irina Bauer and Annika Stein are doctoral researchers working for the panel study FReDA (Family Research and Demographic Analysis) at GESIS Mannheim, Germany.

Target group

Participants will find the course useful if they
- are new to statistical computing (with Stata)
- are familiar with other statistical software but want to get to know Stata

Prerequisites:
- Familiarity with quantitative (survey) data
- Basic knowledge of uni- and bivariate statistics (i.e., descriptive statistics, basics of regression analysis)

Course aim

By the end of the course participants will
- be familiar with Stata's interface and facilities.
- understand how to integrate Stata into their research process to create reproducible and publication-ready results.
- know how to solve common data management problems and how to document all modifications of the data.
- be able to perform typical descriptive and inferential statistical procedures and use graphs to communicate their results effectively.
- know how to proceed from here and how to get additional support if needed.

Fee info

EUR 200: Student/PhD student rate.
EUR 300: Academic/non-profit rate.
The rates include the tuition fee and the course materials.