Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”


Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

Your subscription could not be saved. Please try again.
Your subscription to the ECPR Methods School offers and updates newsletter has been successful.

Discover ECPR's Latest Methods Course Offerings

We use Brevo as our email marketing platform. By clicking below to submit this form, you acknowledge that the information you provided will be transferred to Brevo for processing in accordance with their terms of use.

Introduction to STATA

Course Dates and Times

Friday 26 July 13:00–15:00 and 15:30–18:00

Saturday 27 July 09:00–12:30 and 14:00–17:30


Andrew X. Li

Central European University

STATA is a statistical software widely used for data analysis tasks in academia and in industry.

Compared with R, which is becoming increasingly popular, STATA has a less steep learning curve and is more effective in carrying out analyses using established methods such as panel data methods.

This short course shows how STATA can be an effective and powerful tool in every step of the data analysis process. We will cover:

  • data acquisition
  • cleaning and manipulation
  • visualisations
  • statistical analysis and prediction.

I will briefly mention key statistical concepts and methods when needed, but the emphasis is on STATA itself.

Participants with absolutely zero foundation in statistics are warned to take this course at their own risk as they may not be able to understand what the software is doing. On the other hand, this course assumes little or no experience with STATA. Due to the short duration of the course, we are unlikely to cover highly advanced functions and usage of the software.

If you have absolutely zero foundation in statistical analysis, take this course at your own risk; you may not be able to make sense of what the software is doing.

ECTS Credits for this course

Instructor Bio

Andrew is an assistant professor at CEU's Department of International Relations. He obtained his PhD from the National University of Singapore and King’s College London.

His research interests include international political economy, research design, and quantitative methods. He teaches the Research Design and Methods in IR course series at CEU.

Twitter @lixiang577

This course will give you practical knowledge of the complete data analysis workflow:

  1. Data acquisition
  2. Data manipulation/cleaning
  3. Data visualisation
  4. Analysis and prediction
  5. Reporting the results

Day 1

We start with a general introduction to STATA. You will see that there are two ways to use STATA, either through the software’s graphical user interface or through the command lines. This course focuses on the latter as that’s the way preferred by almost all STATA experts.

We will learn how to get a dataset into STATA for analysis. There are a number of ways to do it, depending on the format of the raw data, including copy and paste. After that, we look briefly at the type of variables (string, float and etc) stored in STATA and learn some basic syntax and mathematical calculations.

Next, we cover data manipulation and cleaning. We will learn how to name and label variables and data values, how to generate new variables and replace the values of existing variables, how to order observations and variables, how to preserve and restore datasets and how to merge or append different datasets into one.

The goal for Day 1 is to have a nice clean dataset in Stata (.dta) format that's ready for analysis.

Day 2

We begin with data visualisation. I will introduce you to various types of graphs STATA can generate, and the formats which these graphs can be exported to or saved as. This part covers various visualisation tools and options in Stata such as title and subtitle, axis label, marker label, color control, etc.

We then move on with data analysis and prediction. You will learn how to generate summary statistics, how to run an OLS regression, how to conduct a t-test for two sample means and how to perform in-sample predictions.

Finally, we will learn how to generate beautiful regression tables that look like those in published journal articles.

The last 90 minutes of the day is reserved for self-study. You'll be able to clarify doubts and put what you have learned into practice. You are strongly encouraged to bring your own data to analyse for your research.

This course requires no prior experience with STATA or any other statistical software/programming language, but I expect you to have basic theoretical knowledge of statistical concepts and OLS regression.

The course is best suited to students who have completed an introductory course on statistical analysis but haven't yet had a chance to carry out the analyses using statistical software.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.

Day Topic Details
1 Getting to know STATA; Data manipulating and cleaning

Input data into STATA, basic syntax and mathematical calculations.

How to:

  • import data into STATA
  • label variables and data values;
  • generate new variables and change existing values;
  • order variables and sort data;
  • merge and append datasets;
3 Data visualisation (more advanced), analysis and prediction

How to:

  • create more sophisticated graphs
  • generate summary statistics
  • run OLS regressions
  • conduct t-tests of two sample means
  • make predictions
  • generate regression tables
2 Data visualization, analysis and prediction

How to:

  • create graphs
  • generate summary statistics
  • run OLS regressions
  • conduct t-tests of two sample means
  • make predictions
Day Readings

Cameron, Adrian Colin and Pravin K. Trivedi
Microeconometrics Using Stata
College Station, TX: Stata Press, 2009. Chapter 2.1–2.3, pp. 29–37


Cameron, Adrian Colin and Pravin K. Trivedi
Microeconometrics Using Stata
College Station, TX: Stata Press, 2009. Chapter 2.4–2.6 and Chapter 3, pp. 38–112


Cameron, Adrian Colin and Pravin K. Trivedi. Microeconometrics Using Stata. College Station, TX: Stata Press, 2009. Chapter 3, pp. 71-112

Software Requirements

STATA 15, Microsoft Word and Excel.

This course will be conducted in a computer lab, where you will have access to Stata and Microsoft Office.

If you would like to bring your own laptop, I strongly prefer Windows PC.


Hardware Requirements

All sessions will take place in a Lab, on computers with Stata 15 and Microsoft Office installed.

STATA is not a free software and we understand that it may be too expensive for you to purchase individual copies.