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Count variables are common in behavioral science as an outcome. Count regression models, such as Poisson regression, are recommended when analyzing count variables but can be challenging to interpret given their non-linear functional form. I recommend relative importance analysis as a method to use in interpreting count regression model results. This work extends on past research by describing an approach to determining the importance of independent variables in count regression models using dominance analysis. Herein, dominance analysis is reviewed as a relative importance method, recommend a pseudo-$R^2$ to use with count regression model-based dominance analysis, and outline the results of an analysis with simulated data that uses the recommended methodology. This work contributes to the literature by extending dominance analysis to count regression models and provides a thoroughly documented example analysis that researchers can use to implement the methodology in their research.

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