This month’s post will be brief, but helpful for anyone who wants to learn Stata. I wrote a short tutorial called First Steps with Stata for an introductory social science data course I visited earlier this month. It’s intended for folks who have never used a statistical package or a command-driven interface, and represents initial steps prior to doing any statistical analysis:

1. Loading and viewing Stata dta files
2. Describing and summarizing data
3. Modifying and recoding data
4. Batch processing with Do files / scripts
5. Importing data from other formats

I chose the data that I used in my examples to illustrate the difference between census microdata and summary data, using sample data from the Current Population Survey to illustrate the former, and a table from the American Community Survey to represent the latter.

I’m not a statistician by training; I know the basics but rely on Python, databases, Excel, or GIS packages instead of stats packages. I learned a bit of Stata on my own in order to maintain some datasets I’m responsible for hosting, but to prepare more comprehensively to write this tutorial I relied on Using Stata for Quantitative Analysis, which I highly recommend. There’s also an excellent collection of Stata learning modules created by UCLA’s Advance Research Computing Center. Stata’s official user documentation is second to none for clearly introducing and explaining the individual commands and syntax.

In my years working in higher ed, the social science and public policy faculty I’ve met have all sworn by Stata over the alternatives. A study of citations in the health sciences, where stats packages used for the research were referenced in the texts, illustrates that SPSS is employed most often, but that Stata and R have increased in importance / usage over the last twenty years, while SAS has declined. I’ve had some students tell me that Stata commands remind them of R. In searching through the numerous shallow reviews and comparisons on the web, I found this post from a data science company that compares R, Python, SPSS, SAS, and Stata to be comprehensive and even-handed in summarizing the strengths and weaknesses of each. In short, I thought Stata was fairly intuitive, and the ability to batch script commands in Do files and to capture all input / output in logs makes it particularity appealing for creating reproducible research. It’s also more affordable than the other proprietary stats packages and runs on all operating systems.

Example – print the first five records of the active dataset to the screen for specific variables:

list statefip age sex race cinethp in f/5

     +-------------------------------------------+
     | statefip   age      sex    race   cinethp |
     |-------------------------------------------|
  1. |  alabama    76   female   white       yes |
  2. |  alabama    68   female   black       yes |
  3. |  alabama    24     male   black        no |
  4. |  alabama    56   female   black        no |
  5. |  alabama    80   female   white       yes |
     |-------------------------------------------|