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Group comparisons and other issues in interpreting models for categorical outcomes using Stata


  • J. Scott Long

    () (Indiana University)


This presentation examines methods for interpreting regression models for categorical outcomes using predicted values. The talk begins with a simple example using basic commands in Stata. It builds on this example to show how more advanced programming features in Stata along with commands in Long and Freese's SPost package can be used in more complex applications that involve plotting predictions. These tools are then applied to the problem of comparing groups in models for categorical outcomes, focusing on the binary regression model. Identification issues make commonly used tests inappropriate since these tests confound the magnitude of the regression coefficients and the variance of the error. An alternative approach is proposed based on the comparisons of the predictions across groups. This approach is illustrated by extending the tools presented in the first part of the talk.

Suggested Citation

  • J. Scott Long, 2006. "Group comparisons and other issues in interpreting models for categorical outcomes using Stata," North American Stata Users' Group Meetings 2006 15, Stata Users Group.
  • Handle: RePEc:boc:asug06:15

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    References listed on IDEAS

    1. Jun Xu & J. Scott Long, 2005. "Confidence intervals for predicted outcomes in regression models for categorical outcomes," Stata Journal, StataCorp LP, vol. 5(4), pages 537-559, December.
    2. Paul D. Allison, 1999. "Comparing Logit and Probit Coefficients Across Groups," Sociological Methods & Research, , vol. 28(2), pages 186-208, November.
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