James Honaker
Dados Biográficos
| ID | 318681 |
|---|---|
| NOME | James Honaker |
| PRENOMES | James |
| SOBRENOME | Honaker |
| ASSINATURA | HONAKER J |
| AFILIAÇÕES | Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 8 |
| TOTAL DE CITAÇÕES | 765 |
| TOTAL COMO AUTOR | 8 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2001 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2019 |
| ÍNDICE H | 6 |
Measuring Complex State Policies
Welfare policy is multidimensional because of the political compromises, competing goals, and federalist structure underpinning it. This complexity has hindered measurement and, therefore, the comparability of research on race and welfare policy. This paper describes a measurement strategy that is transparent, replicable, and attuned to matching the assumptions of statistical models to the policy process. We demonstrate that this strategy leads t…
A Unified Approach to Measurement Error and Missing Data
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
A Unified Approach to Measurement Error and Missing Data
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 dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
Automating Open Science for Big Data
The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher's computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data-including large-scale streaming data-is starting to transform research…
Amelia II
Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, there…
What to Do about Missing Values in Time‐Series Cross‐Section Data
Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international…
A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data
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 revealed a variety of new features of incumbency advantage and sources of party support. Although the mat…
Analyzing Incomplete Political Science Data
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The discrepancy occurs because the computational …
Analyzing Incomplete Political Science Data
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The discrepancy occurs because the computational …
What to Do about Missing Values in Time‐Series Cross‐Section Data
Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international…
A Unified Approach to Measurement Error and Missing Data
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 dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data
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 revealed a variety of new features of incumbency advantage and sources of party support. Although the mat…
A Unified Approach to Measurement Error and Missing Data
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
Measuring Complex State Policies
Welfare policy is multidimensional because of the political compromises, competing goals, and federalist structure underpinning it. This complexity has hindered measurement and, therefore, the comparability of research on race and welfare policy. This paper describes a measurement strategy that is transparent, replicable, and attuned to matching the assumptions of statistical models to the policy process. We demonstrate that this strategy leads t…
Automating Open Science for Big Data
The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher's computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data-including large-scale streaming data-is starting to transform research…
Analyzing Incomplete Political Science Data
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The discrepancy occurs because the computational …
A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data
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 revealed a variety of new features of incumbency advantage and sources of party support. Although the mat…
What to Do about Missing Values in Time‐Series Cross‐Section Data
Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international…
Amelia II
Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, there…
Automating Open Science for Big Data
The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher's computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. The trend toward big data-including large-scale streaming data-is starting to transform research…
A Unified Approach to Measurement Error and Missing Data
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measur…
A Unified Approach to Measurement Error and Missing Data
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 dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop a…
Measuring Complex State Policies
Welfare policy is multidimensional because of the political compromises, competing goals, and federalist structure underpinning it. This complexity has hindered measurement and, therefore, the comparability of research on race and welfare policy. This paper describes a measurement strategy that is transparent, replicable, and attuned to matching the assumptions of statistical models to the policy process. We demonstrate that this strategy leads t…
Computer Science (8 obras) · Data mining (6 obras) · Econometrics (6 obras) · Machine learning (6 obras) · Mathematics (6 obras) · Missing data (5 obras) · Data science (4 obras) · Electoral Systems and Political Participation (4 obras) · Statistical Methods and Bayesian Inference (4 obras) · Statistical Methods and Inference (4 obras)