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:
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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Have you ever wondered whether you should report separate means for different groups or a pooled mean from the entire sample? This is a common scenario that comes up, for instance in deciding whether to separate by sex, region, observed treatment, et cetera.
How do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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