New to Stata 14 is a suite of commands to fit item response theory (IRT) models. IRT models are used to analyze the relationship between the latent trait of interest and the items intended to measure the trait. Stata’s irt commands provide easy access to some of the commonly used IRT models for binary and polytomous responses, and irtgraph commands can be used to plot item characteristic functions and information functions.
To learn more about Stata’s IRT features, I refer you to the [IRT] manual; here I want to go beyond the manual and show you a couple of examples of what you can do with a little bit of Stata code. Read more…
This post was written jointly with David Drukker, Director of Econometrics, StataCorp.
The topic for today is the treatment-effects features in Stata.
Treatment-effects estimators estimate the causal effect of a treatment on an outcome based on observational data.
In today’s posting, we will discuss four treatment-effects estimators:
- RA: Regression adjustment
- IPW: Inverse probability weighting
- IPWRA: Inverse probability weighting with regression adjustment
- AIPW: Augmented inverse probability weighting
We’ll save the matching estimators for part 2.
We should note that nothing about treatment-effects estimators magically extracts causal relationships. As with any regression analysis of observational data, the causal interpretation must be based on a reasonable underlying scientific rationale. Read more…