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Most multivariate models aimed at evaluating the impact of democracy on interstate conflict contain a set of control variables sufficiently large to have a confusing impact. Partly for that reason, the potentially confounding impacts of such variables as wealth, political stability, and political similarity on the relationship of democracy to conflict have still not been evaluated in a definitive manner. In other cases, multivariate models contain intervening variables that are likely to produce misleading results. Multivariate analyses aimed specifically at uncovering spurious relationships in a more straightforward and incremental manner are better able to produce clear and informative results.

Most multivariate models designed by analysts of intemational conflict focus on one key explanatory factor and include several control variables. There are prominent norms or customs in the subfield of international politics regarding the construction of multivariate models and the selection of control variables. Several of these norms or customs may make the results of multivariate analyses confusing and difficult to interpret. Analysts typically do not, for example, distinguish between confounding and intervening variables even though the implications and impacts of such variables are substantially different. Most researchers also fail to distinguish between confounding variables and variables that have an impact on interstate conflict that is complementary to that of the key explanatory factor. Commonly, control variables are included in a model for no other reason than that they also have an impact on interstate conflict or some other outcome variable. In some recent analyses, "independent" variables are included that are related by definition to the key explanatory variable, or to each other. This practice introduces into multivariate models artifactual, misleading degrees of statistical association between variables related to each other by definition with tautological relationships masquerading as empirical causal connections that complicate the interpretation of results. Finally, the construction of pooled cross-sectional, time series analyses is consistently based on the assumption that the key explanatory factor, as well as the control variables, have essentially identical impacts on interstate conflict across space, and over time. Substantial evidence, some of which is provided in this paper, suggests that this assumption is unwarranted. This paper provides five guidelines for the construction of multivariate models that address these issues in a manner aimed at making the results of multivariate analyses more intelligible and credible.

Interstate wars are part of a process of "bargaining" beriveen and among nation-stales that also involves voluntary and forced migrations, as well as the definition and re-drawing of boundaries. Europe has undergone such a thorough process of this kind in recent centuries that interstate wars may not occur there in the future. Territorial disputes and wars also seem unlikely in Narth America. Even Latin America may have enough states with definitive boundaries to make interstate wars unlikely. Africa, however, has mostly new states, with recently drawn and fragile boundaries. Though it was mostly Penceful from 1960 to 1990, since the end of the Cold War military conflicts with interstate aspects have become commonplace in Africa. Interstate wars hare occurred periodically in the Middle East in recent decades and are likely to continue to occur. Asia's future may be like Europe's past. Independent. strong states interact intensively there, as European states did in the late nineteenth and early twentieth centuries. For all these reasons, conflicts classifiable as interstate wars between independent states are likely to occur, perhaps even with some regularity.

Singer et al. (1972) hypothesized that the distribution of military—industrial capabilities among the major powers, as reflected in an index referred to as CON, would have an impact on the incidence of war for those states. Subsequent research on the possible connection between CON and interstate conflict has continued for almost four decades. A recent book declares that CON is the single most potent predictor of interstate conflict. However, theoretical arguments linking CON to conflict tend to be implausible or illogical. Furthermore, empirical analyses over the years have produced inconsistent results. These problems cannot be traced to flaws in CON. The long history of CON suggests that models of interstate conflict would benefit from more cogent theoretical bases for choices of individual variables, as well as the set of variables included in those models.