Stata Lab

Instructor: Daniel Ramirez Smith
Ph.D. Candidate at Pennsylvania State University
Visiting scholar at IC3JM

 

 

Click here for the course materials and notes

 

Click here to see the list of confirmed attendees

 

We are pleased to announce that the D-Lab will be offering an intensive course on Stata. It will take place from February 5th to February 8th of 2019, from 3 pm to 6 pm in the room 18.1.A.02 at the Getafe Campus of Universidad Carlos III de Madrid.

 

Those who would like to register are kindly asked to send an email to d-lab@uc3m.es including their names and academic information(master's/Ph.D. student, the name of the institute etc.) until January 7th. Please note that there are limited seats and graduate students of UC3M at both the master and Ph.D. level will be given priority. The final list of the participants will be published on our website. Also, please note that participants should bring their own laptops with Stata.

 

We will cover the essentials of data management and analysis at every stage of a typical research problem. The key of this course is to learn how to engage in a research project from A-Z. We will use the latest round of the European Social Survey. Here are some of the topics we will discuss:


1) Data management. How do we start to work with data?
a. What are all these windows in Stata?
b. Managing datasets efficiently. (Do-files, Log-files, .dta, etc.)
c. Working with variables (Recoding, generating, cloning, labeling)
d. Merging datasets. (Wide and long datasets)
2) What to do before we analyze data?
a. Looking at data (Browse, describe, summarize, inspect)
b. Cleaning data before analysis.
c. Missing data.
d. Variable transformation (Skewness, outliers, quadratic terms, etc.)
3) Analyzing data and interpreting 
output.
a. Descriptive analysis.
b. Regression on continuous outcomes.
i. Post-estimation tests.
ii. Applying weights.
iii. Nested regression/KHB (mediation analysis).
iv. Predicted values.
c. Regression on discrete outcomes.
i. Post-estimation tests.
ii. Mediation analysis (KHB).
iii. Odds ratios and incidence rate ratios.
iv. Predicted probabilities.
d. Moderation.
i. Interactions.
ii. Interpreting different types of interactions.
iii. Post-estimation tests.
iv. Margins as a
way presenting results.
e. Plots and graphs.

 

 

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