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
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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.
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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:
- 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?
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SPSS has a nice little feature for adding and averaging variables with
missing data that many people don’t know about.
It allows you to add or average variables that have some missing data, while specifying how many are allowed to be missing. (more…)
In our previous posts, we’ve relied on Stata’s pre-loaded datasets to perform analyses. But when you’re working with your own data, you’ll need to know how to import it into Stata.
To demonstrate how this process works, we will use the Iris dataset from UCI.
Download the dataset, then move it to whichever directory you intend to use for Stata files.
There are three main ways of importing data in Stata: either use the menus to import the data, call the dataset by its full file extension, or change your directory to the one with your data and then refer to the dataset by name. (more…)
Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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