Advanced Topics in Data Preparation using Stata (MT) New
Have you received or collected your data (or anticipate doing so!), but are not sure what to do next? This course is designed to equip you with the skills you need to efficiently clean, reformat, and prepare your datasets using Stata. Ideal for social science researchers and analysts who want to use quantitative data for their dissertation or other research project and want to prepare their data efficiently and follow best practices.
Over four interactive sessions, you will master essential techniques for handling missing data, merging and appending datasets, batch processing, and recoding variables. Each session combines concise, focused lectures with practical, hands-on exercises using either your own data or datasets provided by the instructor.
- Postgraduate students and staff
- Further details regarding eligibility criteria are available here
Some experience using Stata software. If you have not used Stata before, it is recommended that you first take the ‘Introduction to Stata’ module.
Number of sessions: 4
# | Date | Time | Venue | Trainer | |
---|---|---|---|---|---|
1 | Tue 5 Nov 10:00 - 12:00 | 10:00 - 12:00 | Titan Teaching Room 2, New Museums Site | map | Sarah Joan Pemberton |
2 | Tue 12 Nov 10:00 - 12:00 | 10:00 - 12:00 | Titan Teaching Room 2, New Museums Site | map | Sarah Joan Pemberton |
3 | Tue 19 Nov 10:00 - 12:00 | 10:00 - 12:00 | Titan Teaching Room 2, New Museums Site | map | Sarah Joan Pemberton |
4 | Tue 26 Nov 10:00 - 12:00 | 10:00 - 12:00 | Titan Teaching Room 2, New Museums Site | map | Sarah Joan Pemberton |
By the end of this course, you will be confident in your ability to transform raw data into a dataset ready for analysis. You will learn how to:
- Handle missing data (e.g., identifying missing data, recoding missing values)
- Process datasets in batch (e.g., using loops to handle multiple datasets or repetitive tasks, practical examples of using ‘foreach’ and ‘forvalues’ loops)
- Recode variables (e.g., creating new variables through transformations, recoding continuous and categorical variables, using conditional statements and functions)
- Merge and append datasets
The module will consist of four sessions. Each session will include a one-hour lecture and one-hour practical workshop.
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For more information please visit our website
This module runs once in Michaelmas Term and once in Lent. You only need to book on ONE of these iterations, either in Michaelmas OR in Lent.
Booking / availability