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Introduction to Stata

Kerstin Hoenig
kerstin.hoenig@lifbi.de

Leibniz Institute for Educational Trajectories

Kerstin Hoenig studied social sciences at Mannheim University, and Johns Hopkins University.

From 2008 to 2013, she worked as a researcher for the National Educational Panel Study at the University of Bamberg.

Since 2014, she has worked for the Leibniz Institute for Educational Trajectories, where she is currently heading the research unit ‘Educational Decisions and Social Inequality’.

In August 2016, she submitted her doctoral thesis on social capital and educational success.

Kerstin has been teaching Stata courses for graduate and postgraduate students since 2012.


Course Dates and Times

Course Dates and Times

Friday 3 March: 13:00-15:00 and 15:30-17:00
Saturday 4 March: 09:30-12:00 and 13:00-14:30
7.5 hours over two days

Prerequisite Knowledge

  • Basic statistical knowledge is assumed (e.g. descriptive statistics, basics of regression analysis)
  • No previous knowledge of Stata is required
  • Knowledge of syntax-based software (e.g. SPSS, R) is helpful but not required
  • Please note that the course does not give a detailed insight into the statistical procedures and the underlying mathematics, but introduces how to use standard statistical procedures in Stata


Short Outline

The course is conceptualised as an add-on to content-based courses. The aim is to introduce how Stata works and how it can be used to conduct descriptive, bivariate and multivariate analyses, but the course does NOT give a detailed insight into statistical techniques or modelling. After attending this course, students will be able to use the basic commands of Stata, understand the basic logic “how Stata does things”, and can then proceed to use Stata for their own analyses. The course cannot cover all Stata commands and procedures. Instead, it will focus on the “frequently used methods”. However, as the basic logic is similar for all Stata commands, participants will be able to transfer the knowledge they gain to their specific project.


Long Course Outline

This course is best suited for students who have no experience with Stata, but who bring in basic statistical skills, e.g. some knowledge of descriptive, bivariate and multivariate statistics. The aim is to give an idea of how Stata can be best used to conduct statistical analyses. Stata strongly relies on the command line interface. Thus, the course gives an insight to general construction of Stata code (syntax). The instructors also try to help students to write clear and parsimonious Stata commands and to improve do-files and do-file documentation. Essentially, students learn to use do-files as a simple lab-book, where annotations and ideas can be recorded in addition to the raw code and in order to facilitate replicable analyses. In detail, we will start off by introducing the software package Stata. What is it? What does the Stata interface look like? How does it work? We achieve this by giving an applied overview of the various screens and windows. We will then move on to data management (importing and exporting data from various sources, recoding variables and labels, generating new variables), uni- and bivariate statistics. On Saturday morning, we will cover the basics of regression models. Depending on the specific interests of the participants, the Saturday afternoon session will focus on more advanced topics, such as variations of regression models (e.g. logistic regression), graphs and tables of results, and loops and macros.

Day Topic Details
Friday Introduction: Stata windows, basics of syntax, a first look at a dataset, importing data
  • Why Stata?
  • Organising the Stata screen
  • Basics of Stata syntax
  • Developing a workflow routine
  • Do- and log-files
  • How to import and export data
  • Basic commands for data management: generate, replace, recode, label
  • Basic commands for uni- and bivariate analysis: tabulate, summarize, tabstat, correlate, test
Saturday morning Basics of regression
  • Basics of regression
  • Brief overview of post-regression diagnostics
  • Interaction terms and factor variables
Saturday afternoon Regression models

Depending on the interest of the participants, session may contain (but cannot cover all of the topics):

  • Logit models and/or other types of regression models
  • Introduction to loops and macros
  • Publishable graphs and tables
Day Readings
Friday

Kohler/Kreuter, Chapters 1-5, 7, 11

Saturday morning

Kohler/Kreuter, Chapter 9

Saturday afternoon

Will be announced depending on choice of participants

Software Requirements

Stata, version 12 or higher.

Literature

ACOCK, A. C. 2016. A Gentle Introduction to Stata, Fifth Edition, College Station, Tex, Stata Press.

KOHLER, U. & KREUTER, F. 2012. Data Analysis Using Stata, Third Edition, College Station, Tex, Stata Press.

LONG, J. S. 2009. The Workflow of Data Analysis Using Stata, College Station, Tex, Stata Press.

MITCHELL, M. N. 2010. Data Management Using Stata: A Practical Handbook, College Station, Tex, Stata Press.

MITCHELL, M. N. 2012. A Visual Guide to Stata Graphics, Third Edition, College Station, Tex, Stata Press.

POLLOCK, P. H. 2015. A Stata Companion to Political Analysis, Third Edition, Washington, D.C, CQ Press College.


Additional Information

Disclaimer

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 in due time.

Note from the Academic Conveners

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, contact the instructor before registering.