Rings Do not Come from Spring

“Ring by Spring” is a common phrase used by undergraduate students at George Fox University to describe the phenomenon where students are engaged before receiving their degree. This research paper aims to understand the factors that lead to students becoming involved in long-term relationships on campus at George Fox. Our research was gathered via a survey of 238 undergraduate students and then analyzed using regression modeling to determine which, if any, factors contributed to students engaging in long-term relationships of more than 6 months. After conducting research, we concluded that three factors were primarily significant in determining the likelihood of a long-term committed relationship: political affiliation, honors college enrollment, and hometown type, with political affiliation having the most economic significance of the three

Throwing Out the Baby With the Bath Water: A Comment on Green, Kim and Yoon

Donald P. Green, Soo Yeon Kim, and David H. Yoon contribute to the literature on estimating pooled times-series cross-section models in international relations (IR). They argue that such models should be estimated with fixed effects when such effects are statistically necessary. While we obviously have no disagreement that sometimes fixed effects are appropriate, we show here that they are pernicious for IR time-series cross-section models with a binary dependent variable and that they are often problematic for IR models with a continuous dependent variable. In the binary case, this perniciousness is the result of many pairs of nations always being scored zero and hence having no impact on the parameter estimates; for example, many dyads never come into conflict. In the continuous case, fixed effects are problematic in the presence of the temporally stable regressors that are common IR applications, such as the dyadic democracy measures used by Green, Kim, and Yoon

Random Coefficient Models for Time-Series–Cross-Section Data

This paper considers random coefficient models (RCMs) for time-series–cross-section data. These models allow for unit to unit variation in the model parameters. After laying out the various models, we assess several issues in specifying RCMs. We then consider the finite sample properties of some standard RCM estimators, and show that the most common one, associated with Hsiao, has very poor properties. These analyses also show that a somewhat awkward combination of estimators based on Swamy’s work performs reasonably well; this awkward estimator and a Bayes estimator with an uninformative prior (due to Smith) seem to perform best. But we also see that estimators which assume full pooling perform well unless there is a large degree of unit to unit parameter heterogeneity. We also argue that the various data driven methods (whether classical or empirical Bayes or Bayes with gentle priors) tends to lead to much more heterogeneity than most political scientists would like. We speculate that fully Bayesian models, with a variety of informative priors, may be the best way to approach RCMs

Comment on 'What To Do (and Not To Do) with Times-Series-Cross-Section Data'

Much as we would like to believe that the high citation count for this article is due to the brilliance and clarity of our argument, it is more likely that the count is due to our being in the right place (that is, the right part of the discipline) at the right time. In the 1960s and 1970s, serious quantitative analysis was used primarily in the study of American politics. But since the 1980s it has spread to the study of both comparative politics and international relations. In comparative politics we see in the 20 most cited Review articles Hibbs’s (1977) and Cameron’s (1978) quantitative analyses of the political economy of advanced industrial societies; in international relations we see Maoz and Russett’s (1993) analysis of the democratic peace; and these studies have been followed by myriad others. Our article contributed to the methodology for analyzing what has become the principal type of data used in the study of comparative politics; a related article (Beck, Katz, and Tucker 1998), which has also had a good citation history, dealt with analyzing this type of data with a binary dependent variable, data heavily used in conflict studies similar to that of Maoz and Russett’s. Thus the citations to our methodological discussions reflect the huge amount of work now being done in the quantitative analysis of both comparative politics and international relations

Random Coefficient Models for Time-Series-Cross-Section Data: Monte Carlo Experiments

This article considers random coefficient models (RCMs) for time-series–cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so

Throwing Out the Baby with the Bath Water: A Comment on Green, Yoon and Kim

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Modeling Dynamics in Time-Series–Cross-Section Political Economy Data

This paper deals with a variety of dynamic issues in the analysis of time- series–cross-section (TSCS) data. While the issues raised are more general, we focus on applications to political economy. We begin with a discussion of specification and lay out the theoretical differences implied by the various types of time series models that can be estimated. It is shown that there is nothing pernicious in using a lagged dependent variable and that all dynamic models either implicitly or explicitly have such a variable; the differences between the models relate to assumptions about the speeds of adjustment of measured and unmeasured variables. When adjustment is quick it is hard to differentiate between the various models; with slower speeds of adjustment the various models make sufficiently different predictions that they can be tested against each other. As the speed of adjustment gets slower and slower, specification (and estimation) gets more and more tricky. We then turn to a discussion of estimation. It is noted that models with both a lagged dependent variable and serially correlated errors can easily be estimated; it is only OLS that is inconsistent in this situation. We then show, via Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels)

Improving Quantitative Studies of International Conflict: A Conjecture

We address a well-known but inffequently discussed problem in the quantitative study of international conflict: Despite immense data collections, prestigious journals, and sophisticated analyses, empirical findings in the literature on international conflict are often unsatisfying. Many statistical results change from article to article and specification to specification. Accurate forecasts are nonexistent. In this article we offer a conjecture about one source of this problem: The causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that includes its critical features. Our approach, aversion of a "neural network" model, uncovers some interesting structural features of international conflict and, as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this improvement comes at little cost, and it is easy to evaluate whether the model is a statistical improvement over the simpler models commonly used.Governmen