Introduction to Stata

Instructors: Oliver Lipps & Ursina Kuhn

Modality: Online

Preliminary workshop: 10 - 11 August 2023

This two-day workshop is free of charge for participants of regular workshops (in week 1 or 2) or can be booked by itself for a fee (200 CHF).

Stata statistical software

Stata is a statistical software widely used in social sciences, particularly economics and political science. Its strength is the combination of user-friendliness and a wide range of functions enabling performance of complex statistical analysis. In this two-day online course, we provide an introduction for researchers who plan to use Stata for their analysis or to attend a course using Stata during Summer School.

Workshop Contents and Objectives

We will have four lessons (approximately half-days) that consist of presentations of main aspects and exercise part in breakout sessions with sample data and time to discuss questions and best practices.

In lesson 1, we will present the Stata environment, how to load data, set a working directory and inspect data.

In lesson 2, we will look at how to work with Stata, write a 'do-file' and conduct simple descriptive statistics and data manipulation, such as recoding variables, subsetting data sets, creating variables, and removing variables. We will also look at how Stata treats missing data and variable labels and address particularities of Stata to avoid frequent problems in data analysis.

In lesson 3, we will focus on more advanced practices to make data management simpler, such as using loops, local and global macros, observation subscripts _n and _N, and lagged values. Furthermore, we will demonstrate how to combine information from different data files using merge and append functions.

In lesson 4, we will look at dates and times, working with strings and wide and long data formats. We show how to carry out data analysis (notably regression) and create graphs, although we will not go into much detail about these aspects.

Prerequisites

Participants should have minimal experience with quantitative statistics and have already worked with other software used in quantitative social sciences, such as SPSS, SAS, or R.