BEYOND LINEARITY BY DEFAULT - GENERALIZED ADDITIVE-MODELS

Social scientists almost always use statistical models positing the de pendent variable as a global, linear function of X, despite suspicions that the social and political world is not so simple, or that our the ories are so strong. Generalized additive models (GAMs) let researcher s fit each independent variable with arbitrary nonparametric functions , but subject to the constraint that the nonparametric effects combine additively. In this way GAMs strike a sensible balance between the fl exibility of nonparametric techniques and the ease of interpretation a nd familiarity of linear regression. GAMs thus offer social scientists a practical methodology for improving on the extant practice of globa l linearity by default. We reanalyze published work from several subfi elds of political science, highlighting the strengths (and limitations ) of GAMs. We estimate non-linear marginal effects in a regression ana lysis of incumbent reelection, nonparametric duration dependence in an analysis of cabinet duration, and within-dyad interaction effects in a reconsideration of the democratic peace hypothesis. We conclude with a more general consideration of the circumstances in which GAMs are l ikely to be of use to political scientists, as well as some apparent l imitations of the technique.