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16.3: Summary

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    As an analysis and research tool, logit modeling expands your capabilities beyond those that can reasonably be estimated with OLS. Now you can accommodate models with binary dependent variables. Logit models are a family of generalized linear models that are useful for predicting the odds or probabilities of outcomes for binary dependent variables. This chapter has described the manner in which logits are calculated, how model fit can be characterized, and several methods for making the logit results readily interpretable.

    Perhaps one of the greatest difficulties in applications of logit models is the clear communication of the meaning of the results. The estimated coefficients show the change in the log of the odds for a one unit increase in the XX variable – not the usual way to describe effects. However, as described in this chapter, these estimated coefficients can be readily transformed into changes in the odds, or the logit itself can be reversed" to provide estimated probabilities. Of particular utility are logit graphics, showing the estimated shift in YY from values of zero to one; the estimated probabilities of YY=1 for cases with specified combinations of values in the XX variables; and estimates of the ranges of probabilities for YY=1 across the ranges of values in any XX.

    In sum, the use of logit models will expand your ability to test hypotheses to include a range of outcomes that are binary in nature. Given that a great deal of the phenomena of interest in the policy and social sciences are of this kind, you will find this capability to be an important part of your research toolkit.

    This page titled 16.3: Summary is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Jenkins-Smith et al. (University of Oklahoma Libraries) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.

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