Not-So-Average After All: Individual vs. Aggregate Effects in Substantive Research

In nonlinear models, the effect of a given variable cannot be gauged directly from the associated coefficient. Instead, researchers typically compute the average effect in the population to assess the substantive significance of the variable of interest. Based on the average response, analysts often make policy recommendations that are to be implemented at the individual level. Such extrapolations, however, can lead to gross generalizations or incorrect inferences. Particularly when cases carry special meaning (e.g., countries), the political and socioeconomic relevance of research findings should be assessed at the individual level. Put differently the real-world applicability of the average effect is often limited. This paper outlines the conditions under which aggregation to mean is problematic, and advocates for a case-centered approach to model evaluation. Our approach allows researchers to draw more meaningful inferences, and makes the connection between research and practical applications more realistic.