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

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  • J. Scott Long

    ()
    (Indiana University)

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    Abstract

    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.

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    Bibliographic Info

    Paper provided by Stata Users Group in its series North American Stata Users' Group Meetings 2006 with number 15.

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    Date of creation: 23 Jul 2006
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    Handle: RePEc:boc:asug06:15

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    Web page: http://www.stata.com/meeting/5nasug
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    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.
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