Abstract
In this paper, we present statistical simulation techniques of interest in substantial interpretation of regression results. Taking stock of recent literature on causality, we argue that such techniques can operate within a counterfactual framework. To illustrate, we report findings using post-electoral data on voter turnout. The analysis of quantitative data, and the estimation of regression models in particular, can now be considered commonplace in the social sciences. There are, of course, notable variations in the ways those analyses are generated (research design, estimation methods, etc.). In the same way, there are discrepancies in terms of standards when it comes to the interpretation of the results and their proper
Highlights
As it is the main focus of our demonstration, let us focus mainly on the results for our variable of interest, that is cynicism. It is statistically significant at the 0.001 level, and the negative coefficient suggests that a greater level of cynicism induces a lower propensity to vote, when all things remain constant
We have shown in this paper that relatively simple methods can be applied, following statistical regressions, to yield estimates reporting quantities of interest expressible in the language of probabilities
We contend that taking greater advantage of such techniques can only be beneficial to flesh out the implications of inferential claims about presumed exogenous variables
Summary
By simulation we mean here two things: 1) the manipulation of the variables to compute quantities of interest and their variations given different values assigned to them, and 2) the generation of these estimates while taking into account the variables’ distributional characteristics. To the extent that we are interested in obtaining a refined analysis of the marginal effect of one (or more) explanatory variable(s) when they take on different values it is possible to apply the counterfactual scheme described above by comparing the predicted probabilities for two given scenarios.
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