11 to 20 of 69 Results
Jan 28, 2019 - Political Analysis Dataverse
Nielsen, Richard; King, Gary, 2019, "Replication Data for: Why Propensity Scores Should Not Be Used for Matching", https://doi.org/10.7910/DVN/A9LZNV, Harvard Dataverse, V1
Abstract: We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal — thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, ra... |
Jan 7, 2019 - The Journal of Politics Dataverse
Imai, Kosuke; King, Gary; Velasco Rivera, Carlos, 2019, "Replication Data for: "Do Nonpartisan Programmatic Policies Generate Partisan Electoral Effects? Evidence from Two Large Scale Experiments"", https://doi.org/10.7910/DVN/70SNIS, Harvard Dataverse, V1
These files replicate all the results in Kosuke Imai, Gary King, and Carlos Velasco Rivera "Do Nonpartisan Programmatic Policies Have Partisan Electoral Effects? Evidence from Two Large Scale Experiments." To replicate all the analyses reported in the main manuscript and supplementary appendix, simply follow the next steps: 0. create a folder in yo... |
King, Gary; Schneer, Benjamin; White, Ariel, 2017, "Replication Data for: How the News Media Activates Public Expression and Influences National Agendas", https://doi.org/10.7910/DVN/1EMHTK, Harvard Dataverse, V1
We demonstrate that the news media causes Americans to take public stands on issues, join national policy conversations, and express themselves publicly more often than they would otherwise --- all key components of democratic politics. We recruited 48 mostly small media outlets that allowed us to choose groups of outlets to write and publish artic... |
King, Gary; Pan, Jennifer; Roberts, Margaret E., 2013, "Replication data for: How Censorship in China Allows Government Criticism but Silences Collective Expression", https://doi.org/10.7910/DVN1/22691, Harvard Dataverse, V4
We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services al... |
Mar 14, 2016 - Christopher Lucas Dataverse
King, Gary, Christopher Lucas, and Richard Nielsen, 2016, "Replication Data for: The Balance-Sample Size Frontier in Matching Methods for Causal Inference", https://doi.org/10.7910/DVN/TRTXLP, Harvard Dataverse, V1, UNF:6:jqAk5bLEzcvHoUHxQqWcag== [fileUNF]
Replication Data for: The Balance-Sample Size Frontier in Matching Methods for Causal Inference |
Dec 2, 2015
King, Gary; Roberts, Margaret, 2014, "Replication data for: How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It", https://doi.org/10.7910/DVN/26935, Harvard Dataverse, V7, UNF:5:Bc1yVsbYLpjnS0Bx6FDnNA== [fileUNF]
"Robust standard errors" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is... |
May 22, 2015
King, Gary; Pan, Jennifer; Roberts, Margaret, E., 2014, "Replication data for: Reverse Engineering Chinese Censorship: Randomized Experimentation and Participant Observation", https://doi.org/10.7910/DVN/26212, Harvard Dataverse, V5, UNF:5:K/LGmB0vjskGYBobxbT+8g== [fileUNF]
Chinese government censorship of social media constitutes the largest coordinated selective suppression of human communication in recorded history. Although existing research on the subject has revealed a great deal, it is based on passive, observational methods, with well known inferential limitations. For example, these methods can reveal nothing... |
Kashin, Konstantin; King, Gary; Soneji, Samir, 2015, "Replication data for: Systematic Bias and Nontransparency in US Social Security Administration Forecasts", https://doi.org/10.7910/DVN/28122, Harvard Dataverse, V1, UNF:5:1oerGFXQ0Bu9bcMFU5/t2A== [fileUNF]
We offer an evaluation of the Social Security Administration demographic and financial forecasts used to assess the long-term solvency of the Social Security Trust Funds. This same forecasting methodology is also used in evaluating policy proposals put forward by Congress to modify the Social Security program. Ours is the first evaluation to compar... |
Kashin, Konstantin; King, Gary; Soneji, Samir, 2015, "Replication data for: Explaining Systematic Bias and Nontransparency in US Social Security Administration Forecasts", https://doi.org/10.7910/DVN/28323, Harvard Dataverse, V1, UNF:6:967llFHgiywsHWWp1cVg9A== [fileUNF]
The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, other government programs, industry decision making, and the evidence base of many scholarly articles. Because SSA makes public little replication information and uses qualitative and antiquated statistical f... |
Mar 23, 2015
Blackwell, Matthew; Honaker, James; King, Gary, 2015, "Replication data for: A Unified Approach To Measurement Error And Missing Data: Overview", https://doi.org/10.7910/DVN/29606, Harvard Dataverse, V1, UNF:5:n/rveBXUX+nOxE6Z5xsWNg== [fileUNF]
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dep... |
