Using predictions and marginal effects to compare groups in regression models for binary outcomes

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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 the authors propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While they develop their approach using binary logit with two groups, they consider how their interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.

The dataset groupcompare-hrs1.dta was 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.

HRS Researcher Contributions are provided by fellow researchers interested in sharing their work. Interested researchers are encouraged to contribute their own datasets by submitting them via electronic mail to HRS User Support. HRS does not produce these files and thus can not support them, nor be responsible for their content or use. They are provided here as a service to the research community.

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Latest Release
Oct 2018 (Ver 1.0)
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