Skip to main content
Political Analysis Dataverse (Cambridge University Press)
Share Dataverse

Share this dataverse on your favorite social media networks.

Political Analysis is the official journal of the Society for Political Methodology. We publish articles that provide original and significant advances in the general area of political methodology, including both quantitative and qualitative methodological approaches.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Cannot publish dataverse.

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Find Advanced Search

1 to 10 of 311 Results
Sep 7, 2017
Blackwell, Matthew, 2017, "Replication Data for: Game Changers: Detecting Shifts in Overdispersed Count Data", doi:10.7910/DVN/SKGWTS, Harvard Dataverse, V1, UNF:6:jIVKdWOAWyhNOhoO2KYdNQ==
Data and code to replicate findings in "Game Changers: Detecting Shifts in Overdispersed Count Data," forthcoming in Political Analysis.
Sep 5, 2017
Peterson, Andrew Jerel, 2017, "Replication Data for: Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster Systems", doi:10.7910/DVN/YTPJ1N, Harvard Dataverse, V1, UNF:6:QpRY+fAqlN8OmOs9E74Ucg==
Replication files for "Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster Systems"
Aug 21, 2017
Bansak, Kirk; Hainmueller, Jens; Hopkins, Daniel J.; Yamamoto, Teppei, 2017, "Replication Data for: The Number of Choice Tasks and Survey Satisficing in Conjoint Experiments", doi:10.7910/DVN/TLPMVI, Harvard Dataverse, V1, UNF:6:DuDnb59vl0m5kHUWRTnYjg==
In recent years, political and social scientists have made increasing use of conjoint survey designs to study decision-making. Here, we study a consequential question which researchers confront when implementing conjoint designs: how many choice tasks can respondents perform befo...
Aug 14, 2017
Ruhe, Constantin, 2017, "Replication Data for: Quantifying change over time: Interpreting time-varying effects in duration analyses", doi:10.7910/DVN/4J48AX, Harvard Dataverse, V1, UNF:6:NqnTUN7yBqIg7xJpgdfAmw==
Duration analyses in political science often model non-proportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a usef...
Aug 7, 2017
Braumoeller, Bear; Marra, Giampiero; Radice, Rosalba; Bradshaw, Aisha, 2017, "Replication Data for: Flexible Causal Inference for Political Science", doi:10.7910/DVN/DXDQV3, Harvard Dataverse, V1
Replication data for the analyses and simulations contained in "Flexible Causal Inference for Political Science"
Aug 7, 2017
Ahlquist, John, 2017, "Replication Data for: List Experiment Design, Non-Strategic Respondent Error, and Item Count Technique Estimators", doi:10.7910/DVN/ELHTWJ, Harvard Dataverse, V1, UNF:6:H4A7EKMTcworEZRXsldnpg==
R code and data to run the Monte Carlo simulations and conduct the analysis reported in "List Experiment Design, Non-Strategic Respondent Error, and Item Count Technique Estimators" by John S. Ahlquist
Jul 21, 2017
Tsai, Tsung-han; Lin, Chang-chih, 2017, "Replication Data for: Modeling Guessing Components in the Measurement of Political Knowledge", doi:10.7910/DVN/80WUQB, Harvard Dataverse, V1, UNF:6:OLHx9ShFd1/kZz7kxPYXZQ==
Due to the crucial role of political knowledge in democratic participation, the measurement of political knowledge has been a major concern in the discipline of political science. Common formats used for political knowledge questions include multiple-choice items and open-ended i...
Jun 9, 2017
Rohlfing, Ingo, 2017, "Replication Data for: Power and false negatives in Qualitative Comparative Analysis (QCA)", doi:10.7910/DVN/UNHYMP, Harvard Dataverse, V1, UNF:6:4QDrI2fEelRJmjDVYJ5hFw==
Files for reproducing figures and results in "Power and false negatives in Qualitative Comparative Analysis (QCA): Foundations, Simulation and Estimation for Empirical Studies"
May 25, 2017
Fachamps, Marcel ; Labonne, Julien, 2017, "Replication Data for "Using Split Samples to Improve Inference on Causal Effects"", doi:10.7910/DVN/Q0IXQY, Harvard Dataverse, V1
Replication Data for Fafchamps and Labonne (2017) "Using Split Samples to Improve Inference on Causal Effects"
May 11, 2017
Rosenberg, Andrew S.; Knuppe, Austin J.; Braumoeller, Bear F., 2017, "Replication Data for: Unifying the Study of Asymmetric Hypotheses", doi:10.7910/DVN/9NXGHP, Harvard Dataverse, V1, UNF:6:+SZmSIvjHdr1SOlsP0TKXQ==
This is a replication folder for “Unifying the Study of Asymmetric Hypotheses." This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric...
Add Data

You need to Sign Up or Log In to create a dataverse or add a dataset.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact Harvard Dataverse Support

Harvard Dataverse Support

Please fill this out to prove you are not a robot.

+ =
Send Message