Numerical issues matter in statistical analysis. Small errors occur when numbers are translated from paper and pencil into the binary world of computers. Surprisingly, these errors may be propagated and magnified through binary calculations, eventually producing statistical estimates far from the truth. In this replication and extension article, we look at one method of verifying the accuracy of statistical estimates by running these same data and models on multiple statistical packages. We find that for two published articles, Nagler (1994, American Journal of Political Science 38:230–255) and Alvarez and Brehm (1995, American Journal of Political Science 39:1055–1089), results are dependent on the statistical package used. In the course o...
The reproducibility of published academic work is increasingly important across a wide array of fiel...
Although published works rarely include causal estimates from more than a few model specifications, ...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...
Social Scientists rarely take full advantage of the information available in their statistical resul...
"Robust standard errors" are used in a vast array of scholarship to correct standard errors for mode...
Replication data for our manuscript "A New Multinomial Accuracy Measure for Polling Bias". We provid...
Most empirical social scientists are surprised that low-level numerical issues in software can have ...
Data archives, libraries, and publishers are extending their services to support computational repro...
The self-correcting nature of science has been questioned repeatedly in the decade since Ioan-nidis ...
Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of...
This article illustrates the difficulty of replicating results in the area of sports economics. This...
This article presents two methods of estimating a study’s replicability that researchers should cons...
We map the state-of-the-science regarding experimental validity threats using a representative sampl...
Results of simulation studies evaluating the performance of statistical methods can have a major imp...
To speedily weed out error, scientific research must be reproduced. Because of the difficulty of rep...
The reproducibility of published academic work is increasingly important across a wide array of fiel...
Although published works rarely include causal estimates from more than a few model specifications, ...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...
Social Scientists rarely take full advantage of the information available in their statistical resul...
"Robust standard errors" are used in a vast array of scholarship to correct standard errors for mode...
Replication data for our manuscript "A New Multinomial Accuracy Measure for Polling Bias". We provid...
Most empirical social scientists are surprised that low-level numerical issues in software can have ...
Data archives, libraries, and publishers are extending their services to support computational repro...
The self-correcting nature of science has been questioned repeatedly in the decade since Ioan-nidis ...
Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of...
This article illustrates the difficulty of replicating results in the area of sports economics. This...
This article presents two methods of estimating a study’s replicability that researchers should cons...
We map the state-of-the-science regarding experimental validity threats using a representative sampl...
Results of simulation studies evaluating the performance of statistical methods can have a major imp...
To speedily weed out error, scientific research must be reproduced. Because of the difficulty of rep...
The reproducibility of published academic work is increasingly important across a wide array of fiel...
Although published works rarely include causal estimates from more than a few model specifications, ...
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and es...