How To Generate 'Marginal Effects' Estimates For Sub-Groups in Your Data

In a scenario described in a recent exchange on the Stata List, a researcher seeks to accomplish a discrete task: specifically, to "estimate some marginal effects for different income levels" (and in the coding the "group" var identifies the various income groups ["group==1", group==2", etc.]). The researcher correctly identify three separate--though similar--coding approaches toward the desired task. The three separate coding approaches yield slightly distinct sets of results (in the scenario, two). Consequently, as a commentator notes, the correct coding approach for any given task "depend[s] on your research goals, [and] you have to choose the one that fulfills them." One important take-away is that is often critical to understand how subtly different coding approaches--each pursuing a common objective--can yield results that differ in critical, yet subtle, ways. As a commentator observes: "We are all guilty, much of the time, of throwing around the term 'marginal effects' without specifying which of the infinitely many different marginal effects are associated with any model" (emphasis added).