Publications

Working Paper
Altman M, Amos B, McDonald MP, Smith D.

Revealing Preferences: Why Gerrymanders are Hard to Prove, and What to Do about It

. Social Science Research Network [Internet]. Working Paper. Publisher's VersionAbstract
Gerrymandering requires illicit intent. We classify six proposed methods to infer the intent of a redistricting authority using a formal framework for causal inferences that encompasses the redistricting process from the release of census data to the adoption of a final plan. We argue all proposed techniques to detect gerrymandering can be classified within this formal framework. Courts have, at one time or another, weighed evidence using one or more of these methods to assess racial or partisan gerrymandering claims. We describe the assumptions underlying each method, raising some heretofore unarticulated critiques revealed by laying bare their assumptions. We then review how these methods were employed in the 2014 Florida district court ruling that the state legislature violated a state constitutional prohibition on partisan gerrymandering, and propose standards that advocacy groups and courts can impose upon redistricting authorities to ensure they are held accountable if they adopt a partisan gerrymander.
Altman M, Amos B, McDonald MP, Smith DA.

Revealing Preferences: Why Gerrymanders are Hard to Prove, and What to Do about It

 

. [Internet]. Working Paper. Download Paper from SSRNAbstract
Gerrymandering requires illicit intent. We classify six proposed methods to infer the intent of a redistricting authority using a formal framework for causal inferences that encompasses the redistricting process from the release of census data to the adoption of a final plan. We argue all proposed techniques to detect gerrymandering can be classified within this formal framework. Courts have, at one time or another, weighed evidence using one or more of these methods to assess racial or partisan gerrymandering claims. We describe the assumptions underlying each method, raising some heretofore unarticulated critiques revealed by laying bare their assumptions. We then review how these methods were employed in the 2014 Florida district court ruling that the state legislature violated a state constitutional prohibition on partisan gerrymandering, and propose standards that advocacy groups and courts can impose upon redistricting authorities to ensure they are held accountable if they adopt a partisan gerrymander.
2011
Altman M. Computational Modeling. In Kurian GT The Encyclopedia of Political Science CQ Press; 2011. pp. 291–292. Publisher's VersionAbstract
In political economy, computational models are used to simulate the behavior of institutions or individuals. Researchers use these models to explore emergent patterns in the behavior of individuals and institutions over time. Computational models are used as a complement to mathematical models -and as a form of independent theory construction in their own right.
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. Research Design. In Kurian GT The Encyclopedia of Political Science CQ Press; 2011. pp. 1473–1474. Publisher's VersionAbstract
Research design is the process of creating a scientific plan for answering research questions through sampling, measurement, and analysis. Research design is both a formal and creative process which involves comparing competing theories, enables one to make inferences, and yields discoveries about the world. The hallmark of social-scientific research design is a rigorous attention to inference, sampling, and measurement.
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.
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.
2005
Altman M, Gill J, McDonald MP. A Comparison of the Numerical Properties of EI Methods. In King G, Rosen O, Tanner MA Ecological Inference: New Methodological Strategies Cambridge: Cambridge University Press; 2005. pp. 383–409. Publisher's VersionAbstract
All statistical techniques place limitations on the types of data and the range of inferences that can be accomodated. All computional implementations of these statistical techniques impose further limitations due to algorithmic and low-level computational implementations. Failure to understand these issues can lead to gross misperceptions and seriously incorrect inferences. In this work we examine the numerical accuracy of King's (1997) approach, to ecological inference by using data perturbation, error analysis, and comparative reliability assessment.
2004
Altman M. Software. In Kempf-Leonard K Encyclopedia of Social Measurement New York: Academic Press; 2004. pp. 569–579. Publisher's VersionAbstract
The use of software in social science research is extensive and wide-ranging. Software is used at every stage of the research process – from collecting, organizing, and analyzing information, to writing and disseminating results. The mathematically demanding nature of modern statistical analysis makes the use of relatively sophisticated statistical software a prerequisite for almost all of quantitative research. Moreover, the combination of increasingly powerful computers, ubiquitous computer networks, and the widespread availability of the software necessary to take advantage of both, have made practical on a hitherto unprecedented scale the application of many complex methods such as maximum likelihood estimation, agent-based modeling, analytic cartography, experimental economics.
2003
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.