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When & Where
Wed, December 11, 2019 - 9:00 AM to 12:00 PM
D-Lab Convening Room

This three-part series will cover the following materials:

Part 1:  Introduction

  • Getting a dataset into Stata (no previous knowledge expected)
  • Examining a dataset and finding variables of interest
  • Summarizing and tabulating variables
  • Stata specific tools and resources (do files, logs, help files, etc.)
  • Coding and cleaning data (making new variables from old variables; labeling variables and values, etc.)
  • Using logical operators in Stata
  • Cross-tabulations

Part 2: Data Analysis in Stata 

  • Correlation
  • T-tests
  • OLS and logistic regression (basic syntax, using interaction terms, interpreting output)
  • Visualization (histograms, bar graphs, scatter plots)
  • Regression postestimation (getting predicted values, basic graphs)
  • Merging and appending datasets

Part 3: Stata Programming

  • Local and global variables (macros)
  • Looping (foreach, forvalues)
  • Reshaping data between wide and long formats
  • Recalling and using command output
  • Generating nicely formatted journal-style tables

Prior knowledge: A basic understanding of variables (nominal, ordinal, continuous), descriptive stats (mean, standard deviation), correlation, and regression will be assumed.

Technology requirement: Please bring a laptop with Stata installed. You can install a trial version of Stata. Please arrive 15 minutes early for installation assistance.

Registration note: To participate in multiple parts of this intensive, please be sure to register for each day separately.

Primary Tool: 
Training Host: 
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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