This study asks whether foreign direct investment (FDI) entering countries after civil conflict helps economic recovery and, through that recovery, reduces the chance of conflict recurring. Using country-level intrastate conflict data spanning 1970–2019, the authors estimate mediation models that link post-conflict FDI to economic reconstruction and then to subsequent conflict risk, and they provide replication files for their analysis.
A formal model of the publication process is developed that captures the interaction between an initial study and a subsequent replication. The model identifies how editorial and authorial choices about what to submit and what to accept shape published evidence across these two-stage research sequences.
Recent attention to urban–rural gaps in American politics has outpaced clear agreement on who counts as rural. Conceptual ambiguity and inconsistent place-based measures can alter empirical conclusions and obscure the political role of rural identity.
Multiplicative interaction models are commonly used to ask whether the relationship between an outcome and an independent variable changes with a moderator. Two routine assumptions are often overlooked: that the interaction is linear (changing at a constant rate with the moderator) and that there is adequate common support of the moderator to estimate conditional effects reliably.
An emerging pattern shows older Internet users interact with and respond to social media very differently from younger users. As older Americans become the fastest-growing group of Internet and social media users, that heterogeneity will become central to online politics. Many influential online experiments, however, were run on samples with too few older adults to represent today’s Internet population — a problem that grows for studies conducted even a few years ago.
Nyhan and Reifler (2010, 2015) documented a "backfire effect"—a phenomenon in which attempts to correct factual misperceptions actually increase the prevalence of the false belief. Their findings have become widely cited both inside and outside political science.
This article evaluates Google Consumer Surveys (GCS) as a low-cost option for rigorous social science. The platform’s relative strengths and weaknesses make it most useful for researchers who identify causality through randomization to treatment groups rather than through selection on observables. The real cost advantage of GCS over alternatives is largely confined to short surveys with only a few questions.
Researchers send their dataset to an independent third party that randomly creates a training sample and a withheld testing sample. All model building, hypothesis selection, and revisions occur using the training sample, allowing feedback from colleagues, editors, and referees. Once the paper is accepted, the pre-specified analysis is applied to the testing sample, and those testing-sample results are the ones published.