Stata Fundamentals: Parts 1-3

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Location: Remote via Zoom. Link will be sent on the morning of the event.

Recordings: This D-Lab workshop will be recorded and made available to UC Berkeley participants for a limited time. Your registration for the event indicates your consent to having any images, comments and chat messages included as part of the video recording materials that are made available.

Date & Time: This workshop is a 3-part series running from
10am-1pm each day:

• Part 1: Tuesday, February 13
• Part 2: Thursday, February 15
• Part 3: Tuesday, February 20

Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.

Description

This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

Each of the parts is divided into a lecture-style coding walkthrough interrupted by challenge problems, discussions of the solutions, and breaks. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. 

Part 1:  Introduction

• Loading datasets 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
• Ordinary Least Squares (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

Workshop Materials: https://github.com/dlab-berkeley/Stata-Fundamentals