Title

groupsbrm -- Using predictions to compare groups in regression
models for binary outcomes

Overview

The groupsbrm package provides do-files to reproduce the analyses in:

Scott Long and Sarah Mustillo, Working paper 2018-01-30,
Using predictions to compare groups in regression models for binary outcomes.

These include files to create the analysis dataset and file to run the analyses.

Downloading the source datasets

The paper uses data from:

(1) Health and Retirement
Study public use dataset. Produced and distributed by the University of Michigan
with funding from the National Institute on Aging (grant number NIA U01AG009740).
Ann Arbor, MI.

(2) RAND HRS Data, Version N. Produced by the RAND Center for the
Study of Aging, with funding from the National Institute on Aging and the Social
Security Administration. Santa Monica, CA. The version of the dataset that
you download could be a more recent version such as Version P.

To obtain these datasets follow these instructions:

Note: These procedures worked on 2018-01-30 but the steps could be changed by HRS.

1. Register at the University of Michigan/HRS data download site at http://hrsonline.isr.umich.edu/

2. At that site, click on "Media and Researchers" on the right side.

3. Click the link for "Pubic Data" on the right side of the page.

4. Complete the registration process and log in to HRS.

5. From the Data Downloads page, download 2 datasets (the screen might vary from what is described below):

5a. On the left side, click on "2006 HRS Core Fat Files (Final) RAND (v.B)" to download h06f2b.dta.

5b. On the right side, click on "RAND HRS Data File (v.N)" to download randpstata.zip.

Creating the analysis dataset

1. Unzip randpstata.zip and h06f2b_STATA.zip so that rndhrs_n.dta and h06f2b.dta are located in your Stata working directory.

2. Place groups-hrs-supportV9.do in your working directory along with the datasets.

3. Run groups-hrs-supportV9.do to create groups-hrs3.dta.

Reproducing the results from our paper

1. Place groups-hrs3.dta, groups-descriptiveV7.do, groups-diabetesV7.do, and groups-goodhlthV7.do in your working directory.

2. Verify that you have installed the spost13_ado package. To do this, run spex logit. If you receive an error, run search spost13_ado and follow the instructions.

3. Run the three do-files. Your results could differ from those in our paper if you have different releases of the HRS datasets.

Acknowledgments

Analysis uses SPost 13:
J. Scott Long & Jeremy Freese. 2014.
Regression Models for Categorical Dependent Variables Using Stata, 3rd Edition.
College Station, TX: Stata Press