Constructing Multivariate Analyses (of Dangerous Dyads)

Abstract

Research in the last couple of decades or so on the proposition that democratic states have not and are not likely to fight interstate wars with each other (and are less likely to engage in militarized conflict with each other than states in general) has produced many multivariate models of international conflict and war. These models as a group have characteristic strengths and weaknesses; this paper will focus primarily on the latter. Perhaps the primary flaw in most of these models, from this writer's point of view, involves their complexity. Virtually all recent models aimed at the evaluation of the impact of regime type on interstate conflict involve a number of control variables that is sufficiently large to make it difficult to interpret statistical results. Furthermore, the control variables are often introduced for no very good reason other than that it is not possible to publish papers that rely entirely on bivariate analyses in good journals.But not all variables apparently or conceivably related to the outcome variable (such as interstate conflict) are equally good candidates for inclusion as control variables, and there are substantive distinctions between different types of control variables to which more attention would usefully be paid. This paper will briefly review the structure and evolution of multivariate models within the democratic peace literature with a view toward highlighting uncertainties and some confusion that those models produce in the aggregate. A discussion of how multivariate models of conflict might be more usefully constructed and interpreted will serve as the basis for the examination of one recently published multivariate

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Concept and measurement: Exploring foreign policy similarity

“…77-78). As a result, we are unable to determine the relationship between the measurement and other variables in statistical models, particularly in terms of whether they are confounding, intervening, or independent factors (Ray 2005). 13 Ordeshook (1986, pp.…”

An Empirical Evaluation of Explanations for Political System Support

“…This study thus contributes to the field by identifying what determinants future research should focus on without assuming independence among the variables and linearity or yielding conditional results that are difficult to interpret (Achen 2005;Hindman 2015;Lee Ray 2005). These advantages have increasingly led political scientists to use machine learning tools to study questions relating to topics as diverse as interest group politics, voting behaviour, survey research methods, legislator ideology, genocide, civil war onset and congruence (Becker, Fetzer, and Novy 2017;Bonica 2018;Cohen and Warner 2021;Grimmer and Stewart 2013;Hainmueller and Hazlett 2014;Lupu and Warner 2021;Muchlinski et al 2016).…”