Stats Tips

New Tip from the RCS Stats Team

January 16, 2019

Treatment effect commands in Stata

For researchers who are working with observational data and are interested in causal effects, Stata has a set of treatment effect commands that can help. Among these commands, there is a particular interest in matching. How do these commands work? This blog shows you some examples of some of these commands, and how to replicate them step by step: https://...

Read more about New Tip from the RCS Stats Team

New Tips from the RCS Stats Team

June 5, 2018

Imagine conducting a randomized controlled trial with noncompliance (participants assigned to the treatment group can choose to avoid treatment, and participants assigned to the control group are able to seek out treatment). If the experimenter observes the ultimate treatment status for all participants, then she can use random assignment as an instrumental variable to measure the causal effect of treatment. Miguel...

Read more about New Tips from the RCS Stats Team

New Tips from the RCS Stats Team

April 18, 2018
R Library - DPLYR

Dplyr is an R package used for data manipulation which provides much more concise, readable blocks of data manipulation code once you can understand its syntax. Dplyr is built around 5 verbs: SelectFilterArrangeMutate, and Summarize.

Select - Selects certain columns in your dataframe
Filter - Select specific rows in your dataframe
Arrange - Orders the rows in your dataframe
Mutate - Creates new columns in... Read more about New Tips from the RCS Stats Team