Rainey, Carlisle. 2014. "Arguing for a Negligible Effect." American Journal of Political Science 58(4): 1083-1091. [Paper] [Journal] [Appendix] [Slides] [Dataverse] [GitHub]

Barabas, Jason, Jennifer Jerit, William Pollock, and Carlisle Rainey.  2014. "The Question(s) of Political Knowledge."  American Political Science Review 108(4): 840-855. [Paper] [Journal] [Dataverse] [GitHub]

Barrilleaux, Charles and Carlisle Rainey. 2014. "The Politics of Need: Examining Governors' Decisions to Oppose the 'Obamacare' Medicaid Expansion." State Politics and Policy Quarterly. 14(4): 437-460.  [Paper] [Journal] [Appendix] [GitHub]


Rainey, Carlisle. "Strategic Mobilization: Why Proportional Representation Decreases Voter Mobilization."Electoral Studies. Forthcoming. [Paper] [Journal] [Appendix] [Slides] [Dataverse] [GitHub]

Clifford, Scott, Jennifer Jerit, Matt Motyl, and Carlisle Rainey. "Moral Concerns and Culture War Attitudes: Investigating the Influence of Elite Rhetoric." Political Communication. Forthcoming. [Paper]

Rainey, Carlisle. "Compression and Conditional Effects: A Product Term Is Essential When Using Logistic Regression to Test for Interaction." Political Science Research and Methods. Accepted, conditional on replication. [Paper] [Appendix] [Poster] [Dataverse] [GitHub]


compactr : a set of functions that mimic base graphics but with more compact axis notation. [Project Page] [CRAN] [GitHub]

Under Review

"Does District Magnitude Matter: The Case of Taiwan." Revised and resubmitted to Electoral Studies. [Paper] [GitHub]

"Unreliable Inferences about Unobservable Processes: A Critique of Partial Observability Models." With Robert Jackson. Under review. [Paper] [GitHub]

"Modeling Misreports of Vote Choice: A Comparison of Alternative Approaches." With Robert Jackson. Under review. [Paper] [GitHub]

"Meaningful Inferences: The Importance of Explicit Statistical Arguments for Substantive Significance." With Kelly McCaskey. Under review. [Paper] [GitHub]

Working Papers

"Dealing with Separation in Logistic Regression Models." [Paper] [GitHub] [Software]