Does Mode Matter For Modeling Political Choice? Evidence From the 2005 British Election Study

Harold D. Clarke and Marianne C. Stewart School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75083 e-mail: hclarke{at}utdallas.edu e-mail: mstewart{at}utdallas.edu Paul Whiteley Department of Government, University of Essex, Colchester, England CO4 3SQ e-mail: whiteley{at}essex.ac.uk e-mail: sanders{at}essex.ac.uk (Corresponding author) Although political scientists have begun to investigate theproperties of Internet surveys, much remains to be learned aboutthe utility of the Internet mode for conducting major surveyresearch projects such as national election studies. This paperaddresses this topic by presenting the results of an extensivesurvey comparison experiment conducted as part of the 2005 BritishElection Study. Analyses show statistically significant, butgenerally small, differences in distributions of key explanatoryvariables in models of turnout and party choice. Estimatingmodel parameters reveals that there are few statistically significantdifferences between coefficients generated using the in-personand Internet data, and the relative explanatory power of rivalmodels is virtually identical for the two types of data. Ingeneral, the in-person and Internet data tell very similar storiesabout what matters for turnout and party preference in Britain.Determining if similar findings obtain in other countries shouldhave high priority on the research agenda for national electionstudies. Authors' note: We thank the U.K. Economics and Social ResearchCouncil (ESRC) and Gary Williams, Senior Science Manager atthe ESRC, for their generous support of, and interest in, thisproject. We also thank the editor and anonymous reviewers forhelpful comments and suggestions.     

Parties in Elections, Parties in Government, and Partisan Bias

Adam Meirowitz Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: ameirowi{at}princeton.edu Thomas Romer Department of Politics and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544 e-mail: romer{at}princeton.edu Political parties are active when citizens choose among candidatesin elections and when winning candidates choose among policyalternatives in government. But the inextricably linked institutions,incentives, and behavior that determine these multistage choicesare substantively complex and analytically unwieldy, particularlyif modeled explicitly and considered in total, from citizenpreferences through government outcomes. To strike a balancebetween complexity and tractability, we modify standard spatialmodels of electoral competition and governmental policy-makingto study how components of partisanship—such as candidateplatform separation in elections, party ID-based voting, nationalpartisan tides, and party-disciplined behavior in the legislature—arerelated to policy outcomes. We define partisan bias as the distancebetween the following two points in a conventional choice space:the ideal point of the median voter in the median legislativedistrict and the policy outcome selected by the elected legislature.The study reveals that none of the party-in-electorate conditionsis capable of producing partisan bias independently. Specifiedcombinations of conditions, however, can significantly increasethe bias and/or the variance of policy outcomes, sometimes insubtle ways.     

Ignoramus, Ignorabimus? On Uncertainty in Ecological Inference

Thomas Gschwend Center for Doctoral Studies in Social and Behavioral Sciences, University of Mannheim, D7, 27, 68131 Mannheim, Germany e-mail: gschwend{at}uni-mannheim.de Ron J. Johnston School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK e-mail: r.johnston{at}bristol.ac.uk e-mail: elff{at}sowi.uni-mannheim.de (corresponding author) Models of ecological inference (EI) have to rely on crucialassumptions about the individual-level data-generating process,which cannot be tested because of the unavailability of thesedata. However, these assumptions may be violated by the unknowndata and this may lead to serious bias of estimates and predictions.The amount of bias, however, cannot be assessed without informationthat is unavailable in typical applications of EI. We thereforeconstruct a model that at least approximately accounts for theadditional, nonsampling error that may result from possiblebias incurred by an EI procedure, a model that builds on thePrinciple of Maximum Entropy. By means of a systematic simulationexperiment, we examine the performance of prediction intervalsbased on this second-stage Maximum Entropy model. The resultsof this simulation study suggest that these prediction intervalsare at least approximately correct if all possible configurationsof the unknown data are taken into account. Finally, we applyour method to a real-world example, where we actually know thetrue values and are able to assess the performance of our method:the prediction of district-level percentages of split-ticketvoting in the 1996 General Election of New Zealand. It turnsout that in 95.5% of the New Zealand voting districts, the actualpercentage of split-ticket votes lies inside the 95% predictionintervals constructed by our method. Authors' note: We thank three anonymous reviewers for helpfulcomments and suggestions on earlier versions of this paper.An appendix giving some technical background information concerningour proposed method, as well as data, R code, and C code toreplicate analyses presented in this paper are available fromthe Political Analysis Web site. Later versions of the codewill be packaged into an R library and made publicly availableon CRAN (http://cran.r-project.org) and on the correspondingauthor's Web site.     

Bayesian and Likelihood Inference for 2 x 2 Ecological Tables: An Incomplete-Data Approach

Ying Lu Department of Sociology, University of Colorado at Boulder, Boulder, CO 80309 e-mail: ying.lu{at}colorado.edu Aaron Strauss Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: abstraus{at}princeton.edu e-mail: kimai{at}princeton.edu (corresponding author) Ecological inference is a statistical problem where aggregate-leveldata are used to make inferences about individual-level behavior.In this article, we conduct a theoretical and empirical studyof Bayesian and likelihood inference for 2 x 2 ecological tablesby applying the general statistical framework of incompletedata. We first show that the ecological inference problem canbe decomposed into three factors: distributional effects, whichaddress the possible misspecification of parametric modelingassumptions about the unknown distribution of missing data;contextual effects, which represent the possible correlationbetween missing data and observed variables; and aggregationeffects, which are directly related to the loss of informationcaused by data aggregation. We then examine how these threefactors affect inference and offer new statistical methods toaddress each of them. To deal with distributional effects, wepropose a nonparametric Bayesian model based on a Dirichletprocess prior, which relaxes common parametric assumptions.We also identify the statistical adjustments necessary to accountfor contextual effects. Finally, although little can be doneto cope with aggregation effects, we offer a method to quantifythe magnitude of such effects in order to formally assess itsseverity. We use simulated and real data sets to empiricallyinvestigate the consequences of these three factors and to evaluatethe performance of our proposed methods. C code, along withan easy-to-use R interface, is publicly available for implementingour proposed methods (Imai, Lu, and Strauss, forthcoming). Authors' note: This article is in the part based on two workingpapers by Imai and Lu, "Parametric and Nonparamateric BayesianModels for Ecological Inference in 2 x 2 Tables" and "QuantifyingMissing Information in Ecological Inference." Various versionsof these papers were presented at the 2004 Joint StatisticalMeetings, the Second Cape Cod Monte Carlo Workshop, the 2004Annual Political Methodology Summer Meeting, and the 2005 AnnualMeeting of the American Political Science Association. We thankanonymous referees, Larry Bartels, Wendy Tam Cho, Jianqing Fan,Gary King, Xiao-Li Meng, Kevin Quinn, Phil Shively, David vanDyk, Jon Wakefield, and seminar participants at New York University(the Northeast Political Methodology conference), at PrincetonUniversity (Economics Department and Office of Population Research),and at the University of Virginia (Statistics Department) forhelpful comments.     

A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Joseph Bafumi Department of Government, Dartmouth College,6108 Silsby HallHanover, NH 03755 e-mail: joseph.bafumi{at}dartmouth.edu Luke Keele Department of Political Science, Ohio State University,2137 Derby Hall, 154 N Oval Mall, Columbus, OH 43210 e-mail: keele.4{at}polisci.osu.edu David Park Department of Political Science, George Washington University,1922 F Street, N.W. 414C, Washington, DC 20052 e-mail: dkp{at}gwu.edu e-mail: bshor{at}uchicago.edu (corresponding author) The analysis of time-series cross-sectional (TSCS) data hasbecome increasingly popular in political science. Meanwhile,political scientists are also becoming more interested in theuse of multilevel models (MLM). However, little work existsto understand the benefits of multilevel modeling when appliedto TSCS data. We employ Monte Carlo simulations to benchmarkthe performance of a Bayesian multilevel model for TSCS data.We find that the MLM performs as well or better than other commonestimators for such data. Most importantly, the MLM is moregeneral and offers researchers additional advantages. Authors' note: A previous version of this article was presentedat the 2005 Midwest Political Science Meeting. We would liketo thank the following for comments and advice in writing thispaper: Andrew Gelman, Nathaniel Beck, Greg Wawro, Sam Cooke,John Londregan, David Brandt. Any errors are our own.     

In Defense of Comparative Statics: Specifying Empirical Tests of Models of Strategic Interaction

Christopher Zorn Department of Political Science, University of South Carolina, Columbia, SC 29208 e-mail: zorn{at}sc.edu e-mail: ccarrub{at}emory.edu (corresponding author) Beginning in 1999, Curtis Signorino challenged the use of traditionallogits and probits analysis for testing discrete-choice, strategicmodels. Signorino argues that the complex parametric relationshipsgenerated by even the simplest strategic models can lead towildly inaccurate inferences if one applies these traditionalapproaches. In their stead, Signorino proposes generating stochasticformal models, from which one can directly derive a maximumlikelihood estimator. We propose a simpler, alternative methodologyfor theoretically and empirically accounting for strategic behavior.In particular, we propose carefully and correctly deriving one'scomparative statics from one's formal model, whether it is stochasticor deterministic does not particularly matter, and using standardlogit or probit estimation techniques to test the predictions.We demonstrate that this approach performs almost identicallyto Signorino's more complex suggestion. Authors' note: We would like to thank Randy Calvert, Mark Hallerberg,Andrew Martin, Eric Reinhardt, Chris Stanton, and Craig Voldenfor their valuable feedback on this project. All remaining errorsare our own. Replication materials are available at the PoliticalAnalysis Web site.     

Shirking in the Contemporary Congress: A Reappraisal

Michael H. Crespin Department of Political Science, Michigan State University, 303 S. Kedzie Hall, East Lansing, MI 48824 e-mail: e-mail: crespinm{at}msu.edu Jeffery A. Jenkins Department of Political Science, Northwestern University, 601 University Place, Evanston, IL 60208 e-mail: e-mail: j-jenkins3{at}northwestern.edu Ryan J. Vander Wielen Department of Political Science, Washington University in St. Louis, Campus Box 1027, One Brookings Drive, St. Louis, MO 63130 e-mail: e-mail: rjvander{at}artsci.wustl.edu This paper replicates the findings that appeared in the article"Severing the Electoral Connection: Shirking in the ContemporaryCongress," published in the American Journal of Political Science(44:316–325), in which Lawrence Rothenberg and MitchellSanders incorporated a new research design and, contrary toall previous studies, found evidence of ideological shirkingin the U.S. House of Representatives. We investigate the robustnessof their results by reestimating their model with Congress-specificfixed effects and find that their results no longer hold.     

Event Dependence and Heterogeneity in Duration Models: The Conditional Frailty Model

Suzanna De Boef and Kyle A. Joyce Department of Political Science, 219 Pond Laboratory, The Pennsylvania State University, University Park, PA 16802 e-mail: sdeboef{at}psu.edu e-mail: kjoyce{at}psu.edu e-mail: jboxstef+{at}osu.edu (corresponding author) We introduce the conditional frailty model, an event historymodel that separates and accounts for both event dependenceand heterogeneity in repeated events processes. Event dependenceand heterogeneity create within-subject correlation in eventtimes thereby violating the assumptions of standard event historymodels. Simulations show the advantage of the conditional frailtymodel. Specifically they demonstrate the model's ability todisentangle the sources of within-subject correlation as wellas the gains in both efficiency and bias of the model when comparedto the widely used alternatives, which often produce conflictingconclusions. Two substantive political science problems illustratethe usefulness and interpretation of the model: state policyadoption and terrorist attacks. Authors' note: Three anonymous reviewers gave valuable advice.Replication materials and an online appendix are available onthe Political Analysis Web site. Any errors are our own responsibility.     

Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court

Timothy R. Johnson Department of Political Science, University of Minnesota, Twin Cities, 1414 Social Sciences Building, 267 19th Ave. South, Minneapolis, MN 55455 e-mail: trj{at}umn.edu James F. Spriggs, II Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brookings Drive, St Louis, MO 63130 e-mail: jspriggs{at}artsci.wustl.edu Sangick Jeon Department of Political Science, Stanford University, 616 Serra Street, Encina Hall West, Room 100, Stanford, CA 94305-6044 e-mail: sjeon{at}stanford.edu Paul J. Wahlbeck Department of Political Science, George Washington University, 1922 F Street, N.W. Suite 401, Washington, DC 20052 e-mail: wahlbeck{at}gwu.edu e-mail: jhfowler{at}ucsd.edu (corresponding author) We construct the complete network of 26,681 majority opinionswritten by the U.S. Supreme Court and the cases that cite themfrom 1791 to 2005. We describe a method for using the patternsin citations within and across cases to create importance scoresthat identify the most legally relevant precedents in the networkof Supreme Court law at any given point in time. Our measuresare superior to existing network-based alternatives and, forexample, offer information regarding case importance not evidentin simple citation counts. We also demonstrate the validityof our measures by showing that they are strongly correlatedwith the future citation behavior of state courts, the U.S.Courts of Appeals, and the U.S. Supreme Court. In so doing,we show that network analysis is a viable way of measuring howcentral a case is to law at the Court and suggest that it canbe used to measure other legal concepts. Authors' note: We appreciate the suggestions of Randy Calvert,Frank Cross, Pauline Kim, Andrew Martin, Richard Pacelle, JimRogers, Margo Schlanger, Amy Steigerwalt, and participants inthe Workshop on Empirical Research in the Law at WashingtonUniversity in St Louis School of Law. We presented former versionsof this article at the 2006 meeting of the Midwest PoliticalScience Association, Chicago, April 20–23; the 2006 meetingof the Southern Political Science Association, Atlanta, GA,January 5–7; and the 2006 Empirical Legal Studies Conference,Austin, TX, October 27–28.     

The Uncovered Set and the Limits of Legislative Action

Ivan Jeliazkov Department of Economics, University of California, Irvine,Irvine, CA Itai Sened Department of Political Science, Washington University in St. Louis, St. Louis, MO We present a simulation technique for sorting out the size,shape, and location of the uncovered set to estimate the setof enactable outcomes in "real-world" social choice situations,such as the contemporary Congress. The uncovered set is a well-knownbut underexploited solution concept in the literature on spatialvoting games and collective choice mechanisms. We explain thissolution concept in nontechnical terms, submit some theoreticalobservations to improve our theoretical grasp of it, and providea simulation technique that makes it possible to estimate thisset and thus enable a series of tests of its empirical relevance.     

Splitting the Difference? Causal Inference and Theories of Split-party Delegations

Matthew J. Butler Department of Economics, 549 Evans Hall, University of California, Berkeley, CA 94720 e-mail: butler{at}econ.berkeley.edu e-mail: daniel_butler{at}stanford.edu (corresponding author) We provide an introduction to the regression discontinuity design(RDD) and use the technique to evaluate models of sequentialSenate elections predicting that the winning party for one Senateseat will receive fewer votes in the next election for the otherseat. Using data on U.S. Senate elections from 1946 to 2004,we find strong evidence that the outcomes of the elections forthe two Senate seats are independent.     

UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of Commons

Iain McLean Nuffield College, Oxford University, Oxford OX1 1NF, United Kingdom. e-mail: iain.mclean{at}nuffield.ox.ac.uk e-mail: spln{at}mail.rochester.edu (corresponding author) Poole's (2000, Non-parametric unfolding of binary choice data.Political Analysis 8:211–37) nonparametric Optimal Classificationprocedure for binary data produces misleading rank orderingswhen applied to the modern House of Commons. With simulationsand qualitative evidence, we show that the problem arises fromthe government-versus-opposition nature of British (Westminster)parliamentary politics and the strategic voting that is entailedtherein. We suggest that political scientists think seriouslyabout strategic voting in legislatures when interpreting resultsfrom such techniques.     

Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

Andrew Gelman Departments of Statistics and Political Science, Columbia University, New York, NY 10027 e-mail: gelman{at}stat.columbia.edu Joseph Bafumi Department of Political Science, Columbia University, New York, NY 10027 We fit a multilevel logistic regression model for the mean ofa binary response variable conditional on poststratificationcells. This approach combines the modeling approach often usedin small-area estimation with the population information usedin poststratification (see Gelman and Little 1997, Survey Methodology23:127–135). To validate the method, we apply it to U.S.preelection polls for 1988 and 1992, poststratified by state,region, and the usual demographic variables. We evaluate themodel by comparing it to state-level election outcomes. Themultilevel model outperforms more commonly used models in politicalscience. We envision the most important usage of this methodto be not forecasting elections but estimating public opinionon a variety of issues at the state level.     

Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models

Curtis S. Signorino 303 Harkness Hall, Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: curt.signorino{at}rochester.edu Robert W. Walker Department of Political Science, Center for Applied Statistics, Washington University in Saint Louis, Campus Box 1063, One Brookings Drive, St. Louis, MO 63130 e-mail: rww{at}wustl.edu e-mail: mbas{at}gov.harvard.edu (corresponding author) We present a simple method for estimating regressions basedon recursive extensive-form games. Our procedure, which canbe implemented in most standard statistical packages, involvessequentially estimating standard logits (or probits) in a manneranalogous to backwards induction. We demonstrate that the techniqueproduces consistent parameter estimates and show how to calculateconsistent standard errors. To illustrate the method, we replicateLeblang's (2003) study of speculative attacks by financial marketsand government responses to these attacks. Authors' note: Our thanks to Kevin Clarke, John Londregan, JeffRitter, Ahmer Tarar, and Kuzey Yilmaz for helpful discussionsconcerning this paper. A previous version was presented at the2002 Political Methodology Summer Meeting.     

How Smart Is my Dummy? Time Series Tests for the Influence of Politics

Kevin Grier Department of Economics, 335 Hester Hall, University of Oklahoma, Norman, OK 73019 e-mail: angus{at}ou.edu Of necessity, many tests for political influence on policiesor outcomes involve the use of dummy variables. However, itis often the case that the hypothesis against which the politicaldummies are tested is the null hypothesis that the interceptis otherwise constant throughout the sample. This simple nullcan cause inference problems if there are (nonpolitical) interceptshifts in the data and the political dummies are correlatedwith these unmodeled shifts. Here we present a method for morerigorously testing the significance of political dummy variablesin single equation models estimated with time series data. Ourmethod is based on recent work on detecting multiple regimeshifts by Bai and Perron. The article illustrates the potentialproblem caused by an overly simple null hypothesis, expositsthe Bai and Perron model, gives a proposed methodology for testingthe significance of political dummy variables, and illustratesthe method with two examples.

Before the curse of statisticsfell upon mankind we lived a happy, innocent life —HilaireBelloc, On Statistics

     

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Kosuke Imai Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: kimai{at}princeton.edu Gary King Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 Elizabeth A. Stuart Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205 e-mail: estuart{at}jhsph.edu e-mail: king{at}harvard.edu (corresponding author) Although published works rarely include causal estimates frommore than a few model specifications, authors usually choosethe presented estimates from numerous trial runs readers neversee. Given the often large variation in estimates across choicesof control variables, functional forms, and other modeling assumptions,how can researchers ensure that the few estimates presentedare accurate or representative? How do readers know that publicationsare not merely demonstrations that it is possible to find aspecification that fits the author's favorite hypothesis? Andhow do we evaluate or even define statistical properties likeunbiasedness or mean squared error when no unique model or estimatoreven exists? Matching methods, which offer the promise of causalinference with fewer assumptions, constitute one possible wayforward, but crucial results in this fast-growing methodologicalliterature are often grossly misinterpreted. We explain howto avoid these misinterpretations and propose a unified approachthat makes it possible for researchers to preprocess data withmatching (such as with the easy-to-use software we offer) andthen to apply the best parametric techniques they would haveused anyway. This procedure makes parametric models producemore accurate and considerably less model-dependent causal inferences. Authors' note: Our thanks to Dan Carpenter and Jeff Koch fordata; Alberto Abadie, Neal Beck, Sam Cook, Alexis Diamond, BenHansen, Guido Imbens, Olivia Lau, Gabe Lenz, Paul Rosenbaum,Don Rubin, and Jas Sekhon for many helpful comments; and theNational Institutes of Aging (P01 AG17625-01), the NationalInstitute of Mental Health (MH066247), the National ScienceFoundation (SES-0318275, IIS-9874747, SES-0550873), and thePrinceton University Committee on Research in the Humanitiesand Social Sciences for research support. Software to implementthe methods in this paper is available at http://GKing.Harvard.edu/matchitand a replication data file is available as Ho et al. (2006).     

The Constrained Instability of Majority Rule: Experiments on the Robustness of the Uncovered Set

Michael S. Lynch Department of Political Science, University of Kansas, 504 Blake Hall, Lawrence, KS 66044 e-mail: mlynch{at}ku.edu Gary J. Miller and Itai Sened Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brooking Drive, St. Louis, MO 63130 e-mail: gjmiller{at}wustl.edu e-mail: sened{at}wustl.edu (corresponding author) The uncovered set has frequently been proposed as a solutionconcept for majority rule settings. This paper tests this propositionusing a new technique for estimating uncovered sets and a seriesof experiments, including five-player computer-mediated experimentsand 35-player paper-format experiments. The results supportthe theoretic appeal of the uncovered set. Outcomes overwhelminglylie in or near the uncovered set. Furthermore, when preferencesshift, outcomes track the uncovered set. Although outcomes tendto occur within the uncovered set, they are not necessarilystable; majority dominance relationships still produce instability,albeit constrained by the uncovered set. Authors' note: We thank Matthew M. Schneider for research assistance.We thank James Holloway, Tse-Min Lin, Jim Granato, Randall L.Calvert, Rick K. Wilson, faculty and students of the Juan MarchInstitute, and reviewers of Political Analysis for their veryhelpful comments and suggestions.     

Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response

Langche Zeng Department of Political Science, University of California, San Diego, La Jolla, CA 92093 e-mail: lazeng{at}ucsd.edu In response to the data-based measures of model dependence proposedin King and Zeng (2006), Sambanis and Michaelides (2008) proposealternative measures that rely upon assumptions untestable inobservational data. If these assumptions are correct, then theirmeasures are appropriate and ours, based solely on the empiricaldata, may be too conservative. If instead, and as is usuallythe case, the researcher is not certain of the precise functionalform of the data generating process, the distribution from whichthe data are drawn, and the applicability of these modelingassumptions to new counterfactuals, then the data-based measuresproposed in King and Zeng (2006) are much preferred. After all,the point of model dependence checks is to verify empirically,rather than to stipulate by assumption, the effects of modelingassumptions on counterfactual inferences. Author’s note: Easy-to-use software to implement the methodsdiscussed here, called "WhatIf: Software for Evaluating Counterfactuals,"is available at http://gking.harvard.edu/whatif. All informationnecessary to replicate the analyses herein can be found in King and Zeng (2008).Conflict of interest statement. None declared.     

The Introduction of Voter Registration and Its Effect on Turnout

David M. Konisky Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room E53-386, Cambridge, MA 02139 e-mail: sda{at}mit.edu e-mail: konisky{at}mit.edu (corresponding author) Studies of voter turnout across states find that those withmore facilitative registration laws have higher turnout rates.Eliminating registration barriers altogether is estimated toraise voter participation rates by up to 10%. This article presentspanel estimates of the effects of introducing registration thatexploits changes in registration laws and turnout within states.New York and Ohio imposed registration requirements on all oftheir counties in 1965 and 1977, respectively. We find thatthe introduction of registration to counties that did not previouslyrequire registration decreased participation over the long termby three to five percentage points. Though significant, thisis lower than estimates of the effects of registration fromcross-sectional studies and suggests that expectations aboutthe effects of registration reforms on turnout may be overstated.     

A Robust Transformation Procedure for Interpreting Political Text

Georg Vanberg Department of Political Science, University of North Carolina, Chapel Hill, NC 27599-3265 e-mail: gvanberg{at}unc.edu (corresponding author) In a recent article in the American Political Science Review,Laver, Benoit, and Garry (2003, "Extracting policy positionsfrom political texts using words as data," 97:311–331)propose a new method for conducting content analysis. TheirWordscores approach, by automating text-coding procedures, representsan advance in content analysis that will potentially have alarge long-term impact on research across the discipline. Toallow substantive interpretation, the scores produced by theWordscores procedure require transformation. In this note, weaddress several shortcomings in the transformation procedureintroduced in the original program. We demonstrate that theoriginal transformation distorts the metric on which contentscores are placed—hindering the ability of scholars tomake meaningful comparisons across texts—and that it isvery sensitive to the texts that are scored—opening upthe possibility that researchers may generate, inadvertentlyor not, results that depend on the texts they choose to includein their analyses. We propose a transformation procedure thatsolves these problems. Authors' note: We would like to thank Ken Benoit, Michael Laver,three anonymous referees, and the editor for comments on earlierversions of this article.