Micah Altman

Modeling the effect of mandatory district compactness on partisan gerrymanders

Abstract Geographic compactness standards have been offered as neutral and effective standards constraining redistricting. In this paper, we test this allegation. Redistricting is treated as a combinatoric optimization problem that is constrained by compactness rules. Computer models are used to analyze the results of applying compactness standards when political groups are geographically concentrated. Several population models are used to generate populations of voters, and arbitrary plans are created with combinatoric optimization algorithms. We find that compactness standards can be used to limit gerrymandering, but only if such standards require severe compactness. Compactness standards are not politically neutral—a geographically concentrated minority party will be affected by compactness standards much differently than a party supported by a geographically diffuse population. The particular effects of compactness standards depend on the institutional mechanism that creates districts.

Beyond authorship: attribution, contribution, collaboration, and credit

As the number of authors on scientific publications increases, ordered lists of author names are proving inadequate for the purposes of attribution and credit. A multi‐stakeholder group has produced a contributor role taxonomy for use in scientific publications. Identifying specific contributions to published research will lead to appropriate credit, fewer author disputes, and fewer disincentives to collaboration and the sharing of data and code.

A Digital Library for the Dissemination and Replication of Quantitative Social Science Research: The Virtual Data Center

The Virtual Data Center software is an open-source, digital library system for quantitative data. The authors discuss what the software does, how it provides an infrastructure for the management and dissemination of distributed collections of quantitative data, and the replication of results derived from these data.

Choosing Reliable Statistical Software

Should we trust the results of our statistical computations? Closely following the development of the mainframe computer, Longley (1967) criticized the accuracy of the first regression programs. Approximately every 10 years thereafter, similar comments echoed for each new generation of statistical software. In a recent criticism, McCullough and Vinod (1999) argue that commonly used statistical packages may give “horrendously inaccurate” results, which have gone largely unnoticed (635–37). Moreover, they argue that in consequence of these inaccuracies, past inferences are in question, and future work must document and archive statistical software alongside statistical models (660–62). When political scientists discuss accuracy in computer-intensive quantitative analysis, however, we are relatively sanguine. Numerical accuracy is almost never discussed in articles or even in textbooks geared toward the most sophisticated and computationally intensive techniques (e.g., King 1989; Mooney 1997). Notable exceptions are a forthcoming APSR controversy that depends on the meaning and evaluation of numerical accuracy in ecological inference (King 2001; Tam Cho and Gaines 2001) and a study of numerical accuracy issues in replication (Altman and McDonald 2001).

From Crayons to Computers

Following the most recent round of redistricting, observers across the political spectrum warned that computing technology had fundamentally changed redistricting, for the worse. They are concerned that computers enable the creation of finely crafted redistricting plans that promote partisan and career goals, to the detriment of electoral competition, and that, ultimately, thwart voters’ ability to express their will through the ballot box. In this article, we provide an overview of the use of computers in redistricting, from the earliest reports of their utilization, through today. We then report responses to our survey of state redistricting authorities’computer use in 1991 and 2001. With these data, we assess the use of computers in redistricting, and the fundamental capabilities of computer redistricting systems.

From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data

Social science data are an unusual part of the past, present, and future of digital preservation. They are both an unqualified success, due to long-lived and sustainable archival organizations, and in need of further development because not all digital content is being preserved. This article is about the Data Preservation Alliance for the Social Sciences (Data-PASS), a project supported by the National Digital Information Infrastructure and Preservation Program (NDIIPP), which is a partnership of five major U.S. social science data archives. Broadly speaking, Data-PASS has the goal of ensuring that at-risk social science data are identified, acquired, and preserved, and that we have a future-oriented organization that could collaborate on those preservation tasks for the future. Throughout the life of the Data-PASS project we have worked to identify digital materials that have never been systematically archived, and to appraise and acquire them. As the project has progressed, however, it has increasingly turned its attention from identifying and acquiring legacy and at-risk social science data to identifying ongoing and future research projects that will produce data. This article is about the projects history, with an emphasis of the issues that underlay the transition from looking backward to looking forward.

Current Research in Voting, Elections, and Technology

The articles in this special issue raise and refine questions about our understanding of the use of, state of the art in, and challenges associated with voting and election technology, broadly conceived. Although researchers have yet to achieve consensus on the broad impact of information technology on our understanding of the practice of politics, the broad outlines of a research agenda are emerging. In this overview, we discuss the current work and identify important research questions that remain to be addressed.

Traditional Districting Principles: Judicial Myths vs. Reality

One person, one vote. With this principle, the Court permanently changed representation in the United States. Equal population requirements changed the face of legislative redistricting in the 1960s, when the Supreme Court applied it to congressional districts in Wesberry v. Sanders , 376 U.S. 1 (1964), and to state legislatures in Reynolds v. Sims , 84 S. Ct. 1362 (1964). Equality in district population was valued not only as instrumental to other goals but also for itself, as Justice Black explained in Wesberry: “As nearly as practicable one man’s vote in a congressional election is to be worth as much as another’s. . . . To say that a vote is worth more in one district than another would . . . run counter to our fundamental ideas of democratic government.” As Justice Brennan made clear, the Court based its decision in large part on a particular understanding of the historical meaning of the Fourteenth Amendment and of article 1, section 2, of the Constitution. And as widely accepted as this principle has come to be, it has been subject to severe historical criticism, criticism that has never been resolved. For example, Berger (1977) claims that malapportionment was historically present and accepted before and during the creation of the Fourteenth Amendment and hence that the equal protection clause could not have implied the equal population principle (from chapter 5): “Certainly there was no disclosure that such intrusion [on apportionment] was contemplated; there is in fact striking evidence that malapportionment was an accepted practice.”

Numerical Issues in Statistical Computing for the Social Scientist: Altman/Statistical Computing

Preface. 1. Introduction: Consequences of Numerical Inaccuracy. 2. Sources of Inaccuracy in Statistical Computation. 3. Evaluating Statistical Software. 4. Robust Inference. 5. Numerical Issues in Markov Chain Monte Carlo Estimation. 6. Numerical Issues Involved in Hessian Matrices (Jeff Gill & Gary King). 7. Numerical Behavior of Kings EI Method. 8. Some Details of Nonlinear Estimation (B. D. McCullough). 9. Spatial Regression Models (James P. LeSage). 10. Convergence Problems in Logistic Regression (Paul Allison). 11. Recommendations for Replication and Accurate Analysis. Bibliography. Author Index. Subject Index.

Transformative Effects of NDIIPP, the Case of the Henry A. Murray Archive

This article comprises reflections on the changes to the Henry A. Murray Research Archive, catalyzed by involvement with the National Digital Information Infrastructure and Preservation Program (NDIIPP) partnership, and the accompanying introduction of next generation digital library software.Founded in 1976 at Radcliffe, the Henry A. Murray Research Archive is the endowed, permanent repository for quantitative and qualitative research data at the Institute for Quantitative Social Science, in Harvard University. The Murray preserves in perpetuity all types of data of interest to the research community, including numerical, video, audio, interview notes, and other types. The center is unique among data archives in the United States in the extent of its holdings in quantitative, qualitative, and mixed quantitative-qualitative research.The Murray took part in an NDIIPP-funded collaboration with four other archival partners, Data-PASS, for the purpose of the identification and acquisition of data at risk, and the joint development of best practices with respect to shared stewardship, preservation, and exchange of these data. During this time, the Dataverse Network (DVN) software was introduced, facilitating the creation of virtual archives. The combination of institutional collaboration and new technology lead the Murray to re-engineer its entire acquisition process; completely rewrite its ingest, dissemination, and other licensing agreements; and adopt a new model for ingest, discovery, access, and presentation of its collections.Through the Data-PASS project, the Murray has acquired a number of important data collections. The resulting changes within the Murray have been dramatic, including increasing its overall rate of acquisitions by fourfold; and disseminating acquisitions far more rapidly. Furthermore, the new licensing and processing procedures allow a previously undreamed of level of interoperability and collaboration with partner archives, facilitating integrated discovery and presentation services, and joint stewardship of collections.