1 to 5 of 5 Results
Jan 5, 2015
Moore, Ryan T., 2014, "Replication data for: Overcoming Barriers to Heterogeneous-Group Learning in the Political Science Classroom", https://doi.org/10.7910/DVN/26685, Harvard Dataverse, V2, UNF:5:eW5o4A7CXYkLrS9ZtaKvmg== [fileUNF]
The included file replication.R performs calculations and simulations to replicate the results of Moore, Ryan T. ``Overcoming Barriers to Heterogeneous-Group Learning in the Political Science Classroom''. PS: Political Science & Politics, 48(1):149-156, 2015. |
Oct 2, 2014 - Political Analysis Dataverse
Ryan Moore, 2012, "Replication data for: Multivariate Continuous Blocking to Improve Political Science Experiments", https://doi.org/10.7910/DVN/I8RK9Q, Harvard Dataverse, V2, UNF:5:wSC3BTd3JRzXJO9M4SEMOw== [fileUNF]
Political scientists use randomized treatment assignments to aid causal inference in field experiments, psychological laboratories, and survey research. Political research can do considerably better than completely randomized designs, but few political science experiments combine random treatment assignment with blocking on a rich set of background... |
Gary King; Emmanuela Gakidou; Kosuke Imai; Jason Lakin; Ryan T. Moore; Clayton Nall; Nirmala Ravishankar; Manett Vargas; Martha María Téllez-Rojo; Juan Eugenio Hernández-Ávila; Mauricio Hernández-Ávila; Hector Hernández Llamas, 2009, "Replication data for: Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme", https://doi.org/10.7910/DVN/P6NC0M, Harvard Dataverse, V6, UNF:3:jeUN9XODtYUp2iUbe8gWZQ== [fileUNF]
Background: We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans. Methods: We randomly assigned treatment within 74 matched pairs of health clusters–-i.e., health facility catchment areas–-representing 118,569 hous... |
Sep 8, 2013
Moore, Ryan T; Powell, Eleanor Neff; Reeves, Andrew, 2013, "Replication data for: Driving Support: Workers, PACs, and Congressional Support of the Auto Industry", https://doi.org/10.7910/DVN/DWNIJY, Harvard Dataverse, V1
These files provide a guide to the code and data objects associated with Moore, Ryan T., Eleanor Neff Powell, and Andrew Reeves. "Driving Support: Workers, PACs, and Congressional Support of the Auto Industry". Business and Politics, 15(2):137-162, 2013. |
Aug 21, 2013
Moore, Ryan T; Ravishankar, Nirmala, 2013, "Replication data for: Who Loses in Direct Democracy?", https://doi.org/10.7910/DVN/GUDI3J, Harvard Dataverse, V1
We examine the success of California's black, Latino, and Asian voters in ballot proposition elections, showing that minority voters lose more often than whites across all ballot propositions, and that this disadvantage is not limited to a small subset of racially-targeted propositions. Minority voters are 2–5 percentage points less likely than oth... |
