Gary King Dataverse

Gary King is the Albert J. Weatherhead III University Professor at Harvard University -- one of 25 with Harvard's most distinguished faculty title -- and Director of the Institute for Quantitative Social Science. King develops and applies empirical methods in many areas of social science, focusing on innovations that span the range from statistical theory to practical application.

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Gov2001
Readme2 Replication
ROAD

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61 to 69 of 69 Results

Record of American Democracy, All Key Data Files Oct 2, 2013 - Record of American Democracy Dataverse King, Gary; Palmquist, Bradley; Adams, Greg; Altman, Micah; Benoit, Kenneth; Gay, Claudine; Lewis, Jeffrey B.; Mayer, Russ; and Reinhardt, Eric, 2007, "Record of American Democracy, All Key Data Files", https://doi.org/10.7910/DVN/JN2MOV, Harvard Dataverse, V2, UNF:3:Of9gjJiO37kI9yvidAQncA== [fileUNF] The Record of American Democracy (ROAD) data provide election returns, socioeconomic summaries, and demographic details about the American public at unusually low levels of geographic aggregation. The NSF-supported ROAD project spans every state in the country from 1984 through 1990 (including some off-year elections). These data enable research on...
Replication data for: What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation." May 8, 2011 - Jeff Gill Dataverse Jeff Gill; Gary King, 2011, "Replication data for: What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation."", https://doi.org/10.7910/DVN/0LRZN6, Harvard Dataverse, V1
Replication data for: Logistic Regression in Rare Events Data Mar 3, 2010 - Political Analysis Dataverse Gary King; Langche Zeng, 2010, "Replication data for: Logistic Regression in Rare Events Data", https://doi.org/10.7910/DVN/SPAFJK, Harvard Dataverse, V1 We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources....
Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data Mar 3, 2010 - Political Analysis Dataverse James E. Alt; Gary King; Curtis S. Signorino, 2010, "Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data", https://doi.org/10.7910/DVN/UDRSQ6, Harvard Dataverse, V1 Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for wh...
Replication data for: A Fast, Easy, & Efficient Estimator for Multiparty Electoral Data Feb 17, 2010 - Political Analysis Dataverse James Honaker; Jonathan N. Katz; Gary King, 2010, "Replication data for: A Fast, Easy, & Efficient Estimator for Multiparty Electoral Data", https://doi.org/10.7910/DVN/F06OSQ, Harvard Dataverse, V1 Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revea...
Replication data for: No Evidence on Directional Vs. Proximity Voting Feb 15, 2010 - Political Analysis Dataverse Jeffrey B. Lewis; Gary King, 2010, "Replication data for: No Evidence on Directional Vs. Proximity Voting", https://doi.org/10.7910/DVN/TS0UJQ, Harvard Dataverse, V1 The directional and proximity models offer dramatically different theories for how voters make decisions and fundamentally divergent views of the supposed microfoundations on which vast bodies of literature in theoretical rational choice and empirical political behavior have been built. We demonstrate here that the empirical tests in the large and...
On Political Methodology Dec 20, 2009 - Political Analysis Dataverse Gary King, 2009, "On Political Methodology", https://doi.org/10.7910/DVN/4C5GJN, Harvard Dataverse, V1 This item requires a subscription to Political Analysis Online.
Replication data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder Jul 28, 2009 - Clayton Nall Dataverse Kosuke Imai; Gary King; Clayton Nall, 2009, "Replication data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder", https://doi.org/10.7910/DVN/AFTR6W, Harvard Dataverse, V1, UNF:3:CKs4T0iVYxP36LQSMgAkuw== [fileUNF]
Replication data for: When Can History Be Our Guide? The Pitfalls of Counterfactual Inference Nov 27, 2007 - International Studies Quarterly Dataverse Gary King; Langche Zeng, 2007, "Replication data for: When Can History Be Our Guide? The Pitfalls of Counterfactual Inference", https://doi.org/10.7910/DVN/9L6A8X, Harvard Dataverse, V1 Inferences about counterfactuals are essential for prediction, answering “what if” questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than e...

Record of American Democracy, All Key Data Files

Oct 2, 2013 - Record of American Democracy Dataverse

King, Gary; Palmquist, Bradley; Adams, Greg; Altman, Micah; Benoit, Kenneth; Gay, Claudine; Lewis, Jeffrey B.; Mayer, Russ; and Reinhardt, Eric, 2007, "Record of American Democracy, All Key Data Files", https://doi.org/10.7910/DVN/JN2MOV, Harvard Dataverse, V2, UNF:3:Of9gjJiO37kI9yvidAQncA== [fileUNF]

The Record of American Democracy (ROAD) data provide election returns, socioeconomic summaries, and demographic details about the American public at unusually low levels of geographic aggregation. The NSF-supported ROAD project spans every state in the country from 1984 through 1990 (including some off-year elections). These data enable research on...

Replication data for: What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation."

May 8, 2011 - Jeff Gill Dataverse

Jeff Gill; Gary King, 2011, "Replication data for: What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation."", https://doi.org/10.7910/DVN/0LRZN6, Harvard Dataverse, V1

Replication data for: Logistic Regression in Rare Events Data

Mar 3, 2010 - Political Analysis Dataverse

Gary King; Langche Zeng, 2010, "Replication data for: Logistic Regression in Rare Events Data", https://doi.org/10.7910/DVN/SPAFJK, Harvard Dataverse, V1

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources....

Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data

Mar 3, 2010 - Political Analysis Dataverse

James E. Alt; Gary King; Curtis S. Signorino, 2010, "Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data", https://doi.org/10.7910/DVN/UDRSQ6, Harvard Dataverse, V1

Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for wh...

Replication data for: A Fast, Easy, & Efficient Estimator for Multiparty Electoral Data

Feb 17, 2010 - Political Analysis Dataverse

James Honaker; Jonathan N. Katz; Gary King, 2010, "Replication data for: A Fast, Easy, & Efficient Estimator for Multiparty Electoral Data", https://doi.org/10.7910/DVN/F06OSQ, Harvard Dataverse, V1

Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revea...

Replication data for: No Evidence on Directional Vs. Proximity Voting

Feb 15, 2010 - Political Analysis Dataverse

Jeffrey B. Lewis; Gary King, 2010, "Replication data for: No Evidence on Directional Vs. Proximity Voting", https://doi.org/10.7910/DVN/TS0UJQ, Harvard Dataverse, V1

The directional and proximity models offer dramatically different theories for how voters make decisions and fundamentally divergent views of the supposed microfoundations on which vast bodies of literature in theoretical rational choice and empirical political behavior have been built. We demonstrate here that the empirical tests in the large and...

On Political Methodology

Dec 20, 2009 - Political Analysis Dataverse

Gary King, 2009, "On Political Methodology", https://doi.org/10.7910/DVN/4C5GJN, Harvard Dataverse, V1

This item requires a subscription to Political Analysis Online.

Replication data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder

Jul 28, 2009 - Clayton Nall Dataverse

Kosuke Imai; Gary King; Clayton Nall, 2009, "Replication data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder", https://doi.org/10.7910/DVN/AFTR6W, Harvard Dataverse, V1, UNF:3:CKs4T0iVYxP36LQSMgAkuw== [fileUNF]

Replication data for: When Can History Be Our Guide? The Pitfalls of Counterfactual Inference

Nov 27, 2007 - International Studies Quarterly Dataverse

Gary King; Langche Zeng, 2007, "Replication data for: When Can History Be Our Guide? The Pitfalls of Counterfactual Inference", https://doi.org/10.7910/DVN/9L6A8X, Harvard Dataverse, V1

Inferences about counterfactuals are essential for prediction, answering “what if” questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than e...

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