Publications

2011
Altman M, Jackman S. Nineteen Ways of Looking at Statistical Software. Journal Of Statistical Software [Internet]. 2011;42:1–12. Publisher's VersionAbstract
In this paper we reflect on on the crucial contributions that innovations in statistical software have made to political methodology, and we identify principles for writing statistical software with maximum benefit to the scholarly community.
Altman M, Fox J, Jackman S. An Introduction to the Special Volume on Political Methodology. Journal Of Statistical Software [Internet]. 2011;42:1–5. Publisher's VersionAbstract
This special volume of the Journal of Statistical Software on political methodology includes 14 papers, with wide-ranging software contributions of political scientists to their own field, and more generally to statistical data analysis in the the social sciences and beyond. Special emphasis is given to software that is written in or can cooperate with the R system for statistical computing.
Altman M, Fox J, Jackman S, Zeileis A. An Introduction to the Special Volume on "Political Methodology". Journal of Statistical Software. 2011;42(1):1-5.Abstract
This special volume of the Journal of Statistical Software on political methodology includes 14 papers, with wide-ranging software contributions of political scientists to their own field, and more generally to statistical data analysis in the the social sciences and beyond. Special emphasis is given to software that is written in or can cooperate with the R system for statistical computing.
2007
Altman M, Gill J, McDonald MP. Accuracy: tools for accurate and reliable statistical computing. Journal of Statistical Software [Internet]. 2007;21:1–30. Publisher's VersionAbstract
Most empirical social scientists are surprised that low-level numerical issues in software can have deleterious effects on the estimation process. Statistical analyses that appear to be perfectly successful can be invalidated by concealed numerical problems. We have developed a set of tools, contained in accuracy, a package for R and S-plus, to diagnose problems stemming from numerical and measurement error and to improve the accuracy of inferences. The tools included in accuracy include a framework for gauging the computational stability of model results, tools for comparing model results, optimization diagnostics, and tools for collecting entropy for true random numbers generation.
2006
Altman M, McDonald MP. How to set a random clock: remarks on Earnest (2006). PS: Political Science and Politics [Internet]. 2006;39(1):795. Publisher's VersionAbstract
We read with interest David C. Earnest's recent (July 2006) PS article about the pedagogical challenges surrounding the statistical computation of pseudo-random numbers (PRNGs). We write to clarify some issues regarding the testing and setting of PRNG seeds, and to direct readers' attention to a set of resources for configuring computationally accurate simulations and statistical analyses.
Altman M. URN: An R-Package for Resampling without Replacement, software version 1.0. [Internet]. 2006. Publisher's VersionAbstract
R software pacage. Functions for sampling without replacement. (Simulated Urns).
2004
Altman M. Statistical Package. In Lewis-Beck M, Bryman AE, Liao TF Encyclopedia of Social Science Research Methods Thousand Oaks: Sage Publications, Inc.; 2004. pp. 1078–1080. Publisher's VersionAbstract
Statistical Packages are collections of software designed to aid in statistical analysis and data exploration. The vast majority of quantitative and statistical analysis relies upon statistical packages for their execution. An understanding of statistical packages is essential to correct and efficient application of many quantitative and statistical methods.
2003
Altman M, Gill J, McDonald M. Numerical issues in statistical computing for the social scientist. New York: John Wiley & Sons; 2003. Publisher's VersionAbstract
A great many empirical researchers in the social sciences take computational factors for granted: For the social scientist, software is a tool, not an end in itself. Although there is an extensive literature on statistical computing in statistics, applied mathematics, and embedded within various natural science fields, there is currently no such guide tailored directly to the needs of the social sciences. There is also an abundance of package-specific literature, and a small amount of work at the basic, introductory level. What is lacking is a text that gives social scientists modern tools, tricks, and advice, yet remains accessible through explanation and example. The overall purpose of this work is to address what we see as a serious deficiency in statistical work in the social and behavioral sciences, broadly defined. Quantitative researchers in these fields rely upon statistical and mathematical computation as much as any of their colleagues in the natural sciences, yet there is less appreciation for the problems and issues in numerical computation. This book seeks to rectify this discrepancy by providing a rich set of monographs on important aspects of social science statistical computing that will guide empirical social scientists past the traps and mines of modern statistical computing.
Altman M, McDonald MP. Replication with Attention to Numerical Accuracy. Political Analysis [Internet]. 2003;11:302–307. Publisher's VersionAbstract
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 of our replications, we uncover other pitfalls that may prevent accurate replication, and make recommendations to ensure the ability for future researchers to replicate results.
1999
Altman M, McDonald MP. Resources for the Testing and Enhancement of Statistical Software. The Political Methodologist [Internet]. 1999;9:12–14. Publisher's VersionAbstract
We offer recommendations to help users of statistical software avoid the pitfalls of computational abstractions and offer guidelines to aid replication.