The Stata dataset groupcompare-hrs1.dta was used for the examples in

  Long, J. Scott and Sarah A. Mustillo. (2018) Using predictions and 
  marginal effects to compare groups in regression models for binary 
  outcomes. Sociological Methods and Research, in press.

  Methods for group comparisons using predicted probabilities and marginal
  effects on probabilities are developed for regression models for binary
  outcomes. Unlike approaches based on the comparison of regression
  coefficients across groups, the methods we propose are unaffected by the
  scalar identification of the coefficients and are expressed in the natural
  metric of the outcome probability. While we develop our approach using
  binary logit with two groups, we consider how our interpretive framework can
  be used with a broad class of regression models and can be extended to any
  number of groups.
 
Our dataset groupcompare-hrs1.dta created using the HRS files:

  (1) Health and Retirement Study public use dataset for 2006 file h06f2b.dta. 
  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 P file rndhrs_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 analysis file was created with the script groups-hrs-supportv9.do.
To reproduce the the results from the paper, in Stata run the command
-search groupsbrm- and follow the instructions to download the do-files for 
analysis. For details on using this dataset, load the dataset in Stata and
run the command -notes _dta-.

Scott Long (jslong@iu.edu) and Sarah Mustillo (smustill@nd.edu)
2018-09-05