From the last posts in this series, you should feel comfortable using Stata’s data editor, changing values and types, and creating new variables.
We’ll now teach you to make your variables more approachable by adding labels.
The image below shows label information for the foreign variable.
Once you’ve imported, examined, and cleaned your data, a common next step would be to make some visual displays or graphs. In this article we’ll go over the details of creating, naming, saving, and exporting graphs in Stata.
We will do all of this using syntax, rather than Stata’s “Graphics” menu. If you want a quick lesson on using the menus to make graphs in Stata, check out this article. (more…)
This month we are featuring a 9-module software tutorial by Kim Love: An Introduction to Data Analysis using R.
It’s perfect for people who:
Stata 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
There’s a common saying among pediatricians: children are not little adults. You can’t take a drug therapy that works in adults and scale it down to a kid-sized treatment.
Children are actively growing. Their livers metabolize drugs differently, and they have a stage of life called puberty that many of us have long forgotten.
Likewise, pilot studies are not little research studies. Please do not take a poorly funded clinical trial and try to sneak your inadequate sample size through peer review by calling it a pilot.
Once 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: