In Search of the Uncovered Set

e-mail: nmiller{at}umbc.edu This paper pursues a number of theoretical explorations andconjectures pertaining to the uncovered set in spatial votinggames. It was stimulated by the article "The Uncovered Set andthe Limits of Legislative Action" by W. T. Bianco, I. Jeliazkov,and I. Sened (2004, Political Analysis 12:256–78) thatemployed a grid-search computational algorithm for estimatingthe size, shape, and location of the uncovered set, and it hasbeen greatly facilitated by access to the CyberSenate spatialvoting software being developed by Joseph Godfrey. I bring tolight theoretical considerations that account for importantfeatures of the Bianco, Jeliazkov, and Sened results (e.g.,the straight-line boundaries of uncovered sets displayed insome of their figures, the "unexpectedly large" uncovered setsdisplayed in other figures, and the apparent sensitivity ofthe location of uncovered sets to small shifts in the relativesizes of party caucuses) and present theoretical insights ofmore general relevance to spatial voting theory.     

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.     

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.     

The Search for Comparability: Response to Binder

Lawrence S. Rothenberg Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: lrot{at}mail.rochester.edu (corresponding author) Binder (n.d., Taking the measure of Congress: Reply to Chiouand Rothenberg. Political Analysis. Forthcoming) highlightsareas of agreement and disagreement with our discussion of preferencemeasurement and legislative gridlock. We now both agree thatW-NOMINATE scores—employed in Binder (1999, The dynamicsof legislative gridlock. American Political Science Review 9:519–33)to measure key independent variables, including bicameral differences—shouldnot be used when examining multichamber legislatures over time.We continue to disagree over whether Common Space scores orBinder's conference vote measure is superior. In this response,we show that, although several of the theoretical and statisticalobjections that Binder (n.d.) raises to our Common Space measuredo not apply, they are all relevant for her conference voteanalog. Additionally, we detail how, despite protests to thecontrary, the conference vote measure is plagued by insufficientdata. Finally, we demonstrate how new efforts to show that Binder's (1999)results continue to hold are not robust.     

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.     

Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data

Jude C. Hays Department of Political Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801 e-mail: jchays{at}uiuc.edu e-mail: franzese{at}umich.edu (corresponding author) In this paper, we demonstrate the econometric consequences ofdifferent specification and estimation choices in the analysisof spatially interdependent data and show how to calculate andpresent spatial effect estimates substantively. We considerfour common estimators—nonspatial OLS, spatial OLS, spatial2SLS, and spatial ML. We examine analytically the respectiveomitted-variable and simultaneity biases of nonspatial OLS andspatial OLS in the simplest case and then evaluate the performanceof all four estimators in bias, efficiency, and SE accuracyterms under more realistic conditions using Monte Carlo experiments.We provide empirical illustration, showing how to calculateand present spatial effect estimates effectively, using dataon European governments' active labor market expenditures. Ourmain conclusions are that spatial OLS, despite its simultaneity,performs acceptably under low-to-moderate interdependence strengthand reasonable sample dimensions. Spatial 2SLS or spatial MLmay be advised for other conditions, but, unless interdependenceis truly absent or minuscule, any of the spatial estimatorsunambiguously, and often dramatically, dominates on all threecriteria the nonspatial OLS commonly used currently in empiricalwork in political science. Authors' note: This research was supported in part by NationalScience Foundation grant no. 0318045. We thank Chris Achen,Jim Alt, Kenichi Ariga, Neal Beck, Jake Bowers, Kerwin Charles,Bryce Corrigan, Tom Cusack, David Darmofal, Jakob de Haan, JohnDinardo, Zach Elkins, John Freeman, Fabrizio Gilardi, KristianGleditsch, Mark Hallerberg, John Jackson, Aya Kachi, JonathanKatz, Mark Kayser, Achim Kemmerling, Gary King, Hasan Kirmanoglu,James Kuklinski, Tse-Min Lin, Xiaobo Lu, Walter Mebane, CovadongaMeseguer, Michael Peress, Thomas Pluemper, Dennis Quinn, MeganReif, Frances Rosenbluth, Ken Scheve, Phil Schrodt, Beth Simmons,Duane Swank, Wendy Tam Cho, Craig Volden, Michael Ward, andGregory J. Wawro for useful comments on this and/or other workin our broader project on spatial econometric models in politicalscience. Bryce Corrigan, Aya Kachi, and Xiaobo Lu each providedexcellent research assistance and Kristian Gleditsch, Mark Hallerberg,and Duane Swank also generously shared data. We alone are responsiblefor any errors.     

Context and the Economic Vote: A Multilevel Analysis

Randy Stevenson Department of Political Science, Rice University, P.O. Box 1892, Houston, Texas 77251–1892 e-mail: rduch{at}uh.edu (corresponding author) e-mail: stevenso{at}ruf.rice.edu Voters use observed economic performance to infer the competenceof incumbent politicians. These economic perceptions enter thevoter's utility calculations modified by a weight that is minimizedwhen the variance in exogenous shocks to the economy is verylarge relative to the variance in economic outcomes associatedwith the competence of politicians. Cross-national variationsin the political and economic context systematically increaseor undermine the voter's ability to ascertain the competencyof incumbents. We test one hypothesis: As policy-making responsibilityis shared more equally among parties, economic evaluations willbe more important in the vote decision. We employ two multilevelmodeling procedures for estimating the contextual variationsin micro-level economic voting effects: a conventional pooledapproach and a two-stage procedure. We compare the multivariateresults of a pooled method with our two-stage estimation procedureand conclude that they are similar. Our empirical efforts usedata from 163 national surveys from 18 countries over a 22-yearperiod.     

A Simple Distribution-Free Test for Nonnested Model Selection

e-mail: kevin.clarke{at}rochester.edu This paper considers a simple distribution-free test for nonnestedmodel selection. The new test is shown to be asymptoticallymore efficient than the well-known Vuong test when the distributionof individual log-likelihood ratios is highly peaked. MonteCarlo results demonstrate that for many applied research situations,this distribution is indeed highly peaked. The simulation furtherdemonstrates that the proposed test has greater power than theVuong test under these conditions. The substantive applicationaddresses the effect of domestic political institutions on foreignpolicy decision making. Do domestic institutions have effectsbecause they hold political leaders accountable, or do theysimply promote political norms that shape elite bargaining behavior?The results indicate that the latter model has greater explanatorypower. Authors' note: This work was supported by National Science FoundationGrant SES-0213771. I thank Paul K. Huth and Todd L. Allee forgraciously sharing their data and code. I also thank Bear Braumoeller,Curtis Signorino, Tasos Kalandrakis, participants in the NorthEastMethodology Program, New York University, 2003, and the reviewersfor their comments. Errors remain my own. Supplementary materialsare available on the Political Analysis Web site.     

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.     

Institutional Arrangements and Logrolling: Evidence from the European Union

This article illustrates how voting rules used to pass a piece of legislation and the structure of the legislation, in terms of whether or not it has single or multiple issue dimensions, influence the frequency and the purpose of position changes in legislative negotiations. Through analysis of data on a set of legislative proposals negotiated in the European Union, I show that position changes are less common under unanimity rule than under majority rule. More importantly, I argue and show that when the negotiated legislation is multidimensional (i.e., contains multiple issues) and the voting rule is unanimity, position changing is a lucrative strategy for legislators. Multidimensional legislation creates opportunities for logrolling, and legislators’ veto power under the unanimity rule enables them to exploit these opportunities. Accordingly, under this scenario, legislators often engage in what I call a within‐legislation logroll and secure favorable legislative outcomes.     

Explaining Variance; Or, Stuck in a Moment We Can't Get Out Of

e-mail: bfbraum{at}fas.harvard.edu Political scientists overwhelmingly seek evidence of causationby measuring changes in the mean of the distribution of thedependent variable. This article points out that some causalrelationships produce changes in the variance, not the mean,of that distribution. It makes the case for variance-alteringcausation by demonstrating its empirical relevance to politicalscientists. The article also lays out an array of causal mechanisms—underthe general headings of aggregation, contagion, and constraint—inorder to demonstrate the logical coherence of variance-alteringcausation and the many ways in which it can arise. The discussionhighlights the often-stringent empirical and logical requirementsthat must be met if the researcher hopes to make concrete predictionsabout changes in variance.     

Comparing Legislators and Legislatures: The Dynamics of Legislative Gridlock Reconsidered

Lawrence S. Rothenberg Department of Political Science, University of Rochester, Rochester, NY 14627 e-mail: lrot{at}mail.rochester.edu (corresponding author) Although political methodologists are well aware of measurementissues and the problems that can be created, such concerns arenot always front and center when we are doing substantive research.Here, we show how choices in measuring legislative preferenceshave influenced our understanding of what determines legislativeoutputs. Specifically, we replicate and extend Binder's highlyinfluential analysis (Binder, Sarah A. 1999. The dynamics oflegislative gridlock, 1947–96. American Political ScienceReview 93:519–33; see also Binder, Sarah A. 2003. Stalemate:Causes and consequences of legislative gridlock. Washington,DC: Brookings Institution) of legislative gridlock, which emphasizeshow partisan, electoral, and institutional characteristics generatemajor legislative initiatives. Binder purports to show thatexamining the proportion, rather than the absolute number, ofkey policy proposals passed leads to the inference that thesefeatures, rather than divided government, are crucial for explaininggridlock. However, we demonstrate that this finding is underminedby flaws in preference measurement. Binder's results are a functionof using W-NOMINATE scores never designed for comparing Senateto House members or for analyzing multiple Congresses jointly.When preferences are more appropriately measured with commonspace scores (Poole, Keith T. 1998. Recovering a basic spacefrom a set of issue scales. American Journal of Political Science42:964–93), there is no evidence that the factors thatshe highlights matter. Authors' note: Thanks to Sarah Binder and Keith Poole for furnishingdata used in our analysis and to Chris Achen and Kevin Clarkefor advice. All errors remain our own. Online appendix is availableon the Political Analysis Web site.     

Does Voting by Mail Increase Participation? Using Matching to Analyze a Natural Experiment

Megan Mullin Department of Political Science, Temple University, 408 Gladfelter Hall (025-22), 1115 West Berks Street, Philadelphia, PA 19122-6089 e-mail: mmullin{at}temple.edu (corresponding author) Would holding elections by mail increase voter turnout? Manyelectoral reform advocates predict that mail ballot electionswill boost participation, basing their prediction on the highturnout rate among absentee voters and on the rise in voterturnout after Oregon switched to voting by mail. However, selectionproblems inherent to studies of absentee voters and Oregon giveus important reasons to doubt whether their results would extendto more general applications of voting by mail. In this paper,we isolate the effects of voting in mail ballot elections bytaking advantage of a natural experiment in which voters areassigned in a nearly random process to cast their ballots bymail. We use matching methods to ensure that, in our analysis,the demographic characteristics of these voters mirror thoseof polling-place voters who take part in the same elections.Drawing on data from a large sample of California counties intwo general elections, we find that voting by mail does notdeliver on the promise of greater participation in general elections.In fact, voters who are assigned to vote by mail turn out atlower rates than those who are sent to a polling place. Analysisof a sample of local special elections, by contrast, indicatesthat voting by mail can increase turnout in these otherwiselow-participation contests. Authors' note: We are grateful to the Haynes Foundation forsupporting this project, Karin MacDonald for bringing mail ballotprecincts to our attention, Anup Pradhan for providing criticaldata, Shawn Treier for his helpful comments on an earlier draft,and Sam Deddeh and Mike Binder for data entry assistance.     

Sensitive Questions, Truthful Answers? Modeling the List Experiment with LISTIT

Standard estimation procedures assume that empirical observationsare accurate reflections of the true values of the dependentvariable, but this assumption is dubious when modeling self-reporteddata on sensitive topics. List experiments (a.k.a. item counttechniques) can nullify incentives for respondents to misrepresentthemselves to interviewers, but current data analysis techniquesare limited to difference-in-means tests. I present a revisedprocedure and statistical estimator called LISTIT that enablemultivariate modeling of list experiment data. Monte Carlo simulationsand a field test in Lebanon explore the behavior of this estimator. Author's Note: My thanks to Robert Axelrod, Janet Box-Steffensmeier,Sarah Croco, Adam Glynn, Sunshine Hillygus, John Jackson, LukeKeele, Gary King, James Kuklinski, Irfan Nooruddin, Mark Tessler,Ashutosh Varshney, and two anonymous reviewers for their commentsand suggestions. Replication materials are available on thePolitical Analysis web site.     

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.     

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 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.     

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.     

Bayesian Approaches for Limited Dependent Variable Change Point Problems

e-mail: spln{at}mail.rochester.edu Limited dependent variable (LDV) data are common in politicalscience, and political methodologists have given much good adviceon dealing with them. We review some methods for LDV "changepoint problems" and demonstrate the use of Bayesian approachesfor count, binary, and duration-type data. Our applicationsare drawn from American politics, Comparative politics, andInternational Political Economy. We discuss the tradeoffs bothphilosophically and computationally. We conclude with possibilitiesfor multiple change point work. Author's note: This paper is a revised version of my "second-yearpaper" presented to the department in September 2005, and Ithank attendees for feedback. For comments on an earlier draft,I am grateful to Kevin Clarke, David Firth, Jeff Gill, KosukeImai, Tasos Kalandrakis, Andrew Martin, Kevin Quinn, Curt Signorino,Randy Stone, and two anonymous referees. Any remaining errorsand omissions remain mine and mine alone.