From the last post in this series, you should know how to change between numeric types and easily change numeric data. We’ll now expand your type-changing skills to include changing string variables with two new commands. (more…)
numeric variable
Getting Started with Stata Tutorial #12: Changing Variables to and from Strings
May 29th, 2025 by TAF SupportGetting Started with Stata Tutorial #11: Editing Variables Using recode and recast
May 12th, 2025 by TAF SupportFrom our last posts in this series, you should be comfortable with how Stata handles data editing, as well as with making your own variables. In this post, we’ll talk about commands that edit the content or storage type of your variables in Stata: recode and recast. Let’s start off with the recode command.
Getting Started with Stata Tutorial #9: Saving, Reordering, and Dropping Data
March 17th, 2025 by TAF SupportStata makes it a breeze to edit or clean your data. If you’re unfamiliar with using data sets in Stata, check out these blog posts to get a good grasp on importing and browsing data in Stata.
For this tutorial we will be using Stata’s “auto” data set. If you haven’t loaded it in yet, type
Getting Started with Stata Tutorial #8: Examining Data in Stata
January 14th, 2025 by guest contributerOnce you’ve imported your data into Stata the next step is usually examining it.
Before you work on building a model or running any tests, you need to understand your data. Ask yourself these questions:
- Is every variable marked as the appropriate type?
- Are missing observations coded consistently and marked as missing?
- Do I want to exclude any variables or data points?
Member Training: Explaining Logistic Regression Results to Non-Researchers
August 1st, 2020 by TAF Support
Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?
Should I Specify a Model Predictor as Categorical or Continuous?
October 22nd, 2018 by Karen Grace-MartinPredictor variables in statistical models can be treated as either continuous or categorical.
Usually, this is a very straightforward decision.
Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.
Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.
But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.