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Interpreting logit models

Author

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  • Luca J. Uberti

    (University of Luxembourg)

Abstract

The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios. I also show that interaction terms are typically easier to interpret in practice than implied by the recent literature on this topic.

Suggested Citation

  • Luca J. Uberti, 2022. "Interpreting logit models," Stata Journal, StataCorp LP, vol. 22(1), pages 60-76, March.
    • Handle: RePEc:tsj:stataj:y:22:y:2022:i:1:p:60-76
    DOI: 10.1177/1536867X221083855
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-1/st0663/
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    File URL: http://hdl.handle.net/10.1177/1536867X221083855
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    Cited by:

    1. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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