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Flexible Prior Specifications for Factor Analytic Models with an Application to the Measurement of American Political Ideology Quinn, Kevin M. Uploaded 04-20-2000 Keywords factor analysis intrinsic autoregression hierarchical modeling Bayesian inference political ideology Abstract Factor analytic measurement models are widely used in the social sciences to measure latent variables and functions thereof. Examples include the measurement of: political preferences, liberal democracy, latent determinants of exchange rates, and latent factors in arbitrage pricing theory models and the corresponding pricing deviations. Oftentimes, the results of these measurement models are sensitive to distributional assumptions that are made regarding the latent factors. In this paper I demonstrate how prior distributions commonly used in image processing and spatial statistics provide a flexible means to model dependencies among the latent factor scores that cannot be easily captured with standard prior distributions that treat the factor scores as (conditionally) exchangeable. Markov chain Monte Carlo techniques are used to fit the resulting models. These modeling techniques are illustrated with a simulated data example and an analysis of American political attitudes drawn from the 1996 American National Election Study.

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Aggregate Economic Conditions and Indivdual Forecasts: A Mulilevel Model of EconomicExpectations Jones, Bradford S. Haller, H. Brandon Uploaded 00-00-0000 Keywords random coefficient modeling multilevel analysis hierarchical linear models Abstract To what extent are individual economic expectations related to actual economic conditions? This is the central question examined in this paper. Surprisingly, little research exists examining how economic expectations are formed. Moreover, even less research has been done examining the interaction between the state of the national economy and individual forecasts. Most research addressing expectation formation has resided at the aggregate level. In this paper, we utilize the methodology of random coefficient models to explore the linkage between individuals and the macroeconomic environment. We conceptualize individuals as being "nested" within time periods. Individual forecasts are treated as contextually conditioned by the state of the economy. We find evidence that aggregate economic indicators do influence the parameters predicting economic expectations. Furthermore, the relationship between the macroeconomy and individual expectations provides strong support for Katona's (1972, 1975) notion of "psychological economics." We find that individual forecasts of the future are "brighter" when aggregate economic conditions are "darkest." Additionally, we find that individuals tend to rely less on retrospective evaluations of the economy when the economy is faring poorly.

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Generalized Substantively Reweighted Least Squares Regression Gill, Jeff Uploaded 01-29-1997 Keywords Linear Models Robust Procedures Data Analysis Outlier Identification Abstract Linear modeling often employs robust and resistant techniques to compensate for undesirable properties in the data. Conversely, Substantive Weighted Least Squares differs from these techniques since it seeks to analyze what makes the outliers distinguishable in their use of resources. SWLS does not see outliers as becoming potentially unbounded or even that they are necessarily undesirable elements of the data. SWLS runs consecutive weighted OLS models downweighting each case whose jacknifed residual is less than a specific threshold. Final iteration significant variables are identified as those which have a greater effect on higher performing cases and therefore provide prescriptive recommendations. GSRLS generalizes the SWLS technique by using transformations relating the jackknifed residuals to a common tabular distribution. This allows alpha-level positive outlier identification. Here, GSRLS is first placed in a theoretical context and further explored through monte-carlo simulation. In general, GSRLS can be seen as a data-analytic tool that exploits certain characteristics of the linear model to find variable influence on successful cases.

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Cosponsorship in the U.S. Senate: A Multilevel Approach to Detecting Subtle Social Predictors of Legilslative Support Gross, Justin Uploaded 09-14-2008 Keywords Congress cosponsorship social network analysis multilevel models mixed effects GLMM Abstract Why do members of Congress choose to cosponsor legislation proposed by their colleagues and what can we learn from their patterns of cosponsorship? To answer these questions properly requires models that respect the relational nature of the relevant data and the resulting interdependence among observations. We show how the inclusion of carefully selected random effects can capture network-type dependence, allowing us to more confidently investigate senators' propensity to support colleagues' proposals. To illustrate, we examine whether certain social factors such as demographic similarities, opportunities for interaction, and institutional roles are associated with varying odds of cosponsorship during the 2003-04 (108th) Senate.

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The Insignificance of Null Hypothesis Significance Testing Gill, Jeff Uploaded 02-06-1999 Keywords hypothesis testing inverse probability Fisher Neyman-Pearson Bayesian approaches confidence sets meta-analysis Abstract The current method of hypothesis testing in the social sciences is under intense criticism yet most political scientists are unaware of the important issues being raised. Criticisms focus on the construction and interpretation of a procedure that has dominated the reporting of empirical results for over fifty years. There is evidence that null hypothesis significance testing as practiced in political science is deeply flawed and widely misunderstood. This is important since most empirical work in political science argues the value of findings through the use of the null hypothesis significance test. In this article I review the history of the null hypothesis significance testing paradigm in the social sciences and discuss major problems, some of which are logical inconsistencies while others are more interpretive in nature. I suggest alternative techniques to convey effectively the importance of data-analytic findings. These recommendations are illustrated with examples using empirical political science publications.

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Getting the Mean Right: Generalized Additive Models Beck, Nathaniel Jackman, Simon Uploaded 00-00-0000 Keywords non-parametric regression smoothing loess non-linear egression Monte Carlo analysis interaction effects incumbency cabinet duration violence Abstract We examine the utility of the generalized additive model as an alternative to the common linear model. Generalized additive models are flexible in that they allow the effect of each independent variable to be modelled non-parametrically while requiring that the effect of all the independent variables is additive. GAMs are common in the statistics literature but are conspicuously absent in political science. The paper presents the basic features of the generalized additive model. Through Monte Carlo experimentation we show that there is little danger of the generalized additive model finding spurious structures. We use GAMS to reanalyze several political science data sets. These applications show that generalized additive models can be used to improve standard analyses by guiding researchers as to the parametric shape of response functions. The technique also provides interesting insights about data, particularly in terms of modelling interactions.

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Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs Jakulin, Aleks Uploaded 10-27-2004 Keywords roll call analysis latent variable models MCMC information theory clustering visualization Abstract To analyze the roll calls in the US Senate in year 2003, we have employed the methods already used throughout the science community for analysis of genes, surveys and text. With information-theoretic measures we assess the association between pairs of senators based on the votes they cast. Furthermore, we can evaluate the influence of a voter by postulating a Shannon information channel between the outcome and a voter. The matrix of associations can be summarized using hierarchical clustering, multi-dimensional scaling and link analysis. With a discrete latent variable model we identify blocs of cohesive voters within the Senate, and contrast it with continuous ideal point methods. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we were able to estimate the empirical voting power of individual blocs through what-if analysis.

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Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analyses of a Field Experiment on Democratic Deliberations Imai, Kosuke Yamamoto, Teppei Uploaded 06-30-2008 Keywords differential misclassification nonparametric bounds retrospective studies sensitivity analysis survey measurements Abstract Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measurement error on causal estimation. The proposed nonparametric identification analysis avoids arbitrary modeling decisions and formally characterizes the roles of additional assumptions. We show the serious consequences of differential misclassification and offer a new sensitivity analysis that allows researchers to evaluate the robustness of their conclusions. Our methods are motivated by a field experiment on democratic deliberations, in which one set of estimates potentially suffers from differential misclassification. We show that an analysis ignoring differential measurement error may considerably overestimate the causal effects. This finding contrasts with the case of classical measurement error which always yields attenuation bias.

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Analysis of Crossover Voting Alvarez, R. Michael Uploaded 02-26-1999 Keywords crossover voting strategic voting ecological inference exit poll analysis Abstract We undertake the analysis of primary elections from 1980 through 1996 using both academic individual level survey data, media exit-polls, and aggregate election returns on a county by county basis. We come to the following conclusions: 1. there is very little crossover voting in general in United States primaries; 2. the difference in the amount of crossover voting between states with open primaries and closed primaries is not substantively large; 3. thee amount of strategic behavior on the part of voters is extremely small.

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Measurement Models for Time Series Analysis: Estimating Dynamic Linear Errors in Variables McAvoy, Gregory Uploaded 07-12-1999 Keywords measurement models time series analysis errors-in-variables Rolling Thunder Abstract This paper uses state space modelling and Kalman filtering to estimate a dynamic linear errors-in-variables model with random measurement error in both the dependent and independent variables. I begin with a general description of the dynamic errors-in-variables model, translate it into state space form, and show how it can be estimated via the Kalman filter. I then use the model in a substantive example to examine the effects of aggregate partisanship on evaluations of President Reagan's job performance using data from the 1984 primary campaign and compare the OLS estimates for this example to those derived from maximum likelihood estimates of the dynamic shock-error setup. Next, I report the results of a simulation in which the amount of random measurement error is varied and, thus, demonstrate the importance of estimating measurement error models and the superiority that Kalman filtering has over regression. Finally, I estimate a dynamic linear errors-in-variables model using multiple indicators for the latent variables.

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Identifying Intra-Party Voting Blocs in the UK House of Commons Quinn, Kevin Spirling, Arthur Uploaded 07-19-2005 Keywords roll-call analysis UK House of Commons Bayesian nonparametrics Dirichlet process mixtures Abstract Legislative voting records are an important source of information about legislator preferences, intra-party cohesiveness, and the divisiveness of various policy issues. Standard methods of analyzing a legislative voting record tend to have serious drawbacks when applied to legislatures, such as the UK House of Commons, that feature highly disciplined parties, strategic voting, and large amounts of missing data. We present a method (based on a Dirichlet process mixture model) for analyzing such voting records that does not suffer from these same problems. We apply the method to the voting records of Labour and Conservative Party MPs in the 1997-2001 session of the UK House of Commons. Our method has a number of advantages over existing approaches. It is model-based and thus allows one to make probability statements about quantities of interest. It allows one to estimate the number of voting blocs within a party or any other group of MPs. It handles missing data in a principled fashion and does not rely on an ad hoc distance metric between voting profiles. Finally, it can be used as both a predictive model and an exploratory model. We illustrate these points in our analysis of the UK data.

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Regression Adjustments to Experimental Data: Do David Freedmanâ??s Concerns Apply to Political Science? Green, Donald Uploaded 07-15-2009 Keywords Experiments Regression Covariates Analysis of Covariance Abstract Abstract: One of David Freedman's important legacies was to raise awareness of the assumptions that underlie everyday statistical practice, such as regression analysis. His recent papers (Freedman 2008a, 2008b) offer stern warnings to those who offer regression analysis as an appropriate way to analyze experimental results. In particular, Freedman demonstrates that including pre-treatment covariates as controls leads to bias in finite samples and inaccurate standard errors. Freedman advises researchers against using regression adjustments for experiments involving fewer than 500 observations (2008a, p.191), a recommendation that has gained increasing attention and acceptance among social scientists. This paper argues that the ever-cautious Freedman was probably too cautious in his recommendations. After explicating the special features of Freedman's model, I use a combination of simulated and actual examples to show that as a practical matter the biases that Freedman pointed out tend to be negligible for N > 20. Pathological cases that could generate biases for larger experiments involve extreme outliers that would be readily detected through visual inspection.

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The Initiative as a Catalyst for Policy Change Boehmke, Frederick Uploaded 03-08-1999 Keywords Initiative political theory event history analysis Abstract In this paper I develop and test a theoretical model of the role that the initiative process plays in shaping policy outcomes. My model builds on Gerber (1996) by introducing uncertainty over the median voter's ideal point and by allowing the interest group to lobby the legislature before a potential initiative is proposed. Successful lobbying may occur due to the uncertainty over the outcome of an initiative. Besides the possibility of lobbying, the results differ from Gerber's since proposal of an initiative is an equilibrium outcome for certain parameter values. I then turn to an event history analysis of state lottery adoptions to test the model's prediction that the initiative process should make it more likely that a state adopt a lottery. This is related to work by Berry and Berry (1990). The empirical hypothesis is found to be supported in the post 1980 period, which I believe is a result of the well-documented resurgence in its use after California's Proposition 13 in 1978. An indirect effect of the initiative in non-initiative states is also found through the importance of neighbors' adoptions. This confirms the view that initiative states are often policy leaders, which I argue may lead to less effective policy choices since they have less information about how to implement then.

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Polarization and Political Violence Penubarti, Mohan Asea, Patrick Uploaded 07-12-1996 Keywords polarization political violence extreme bounds analysis Abstract We explore the implications of a new notion of inequality --- polarization --- for the incidence and level of political violence. A society is said to be polarized when its members can be classified into different clusters, with each cluster being similar in terms of the attributes of its members (intra--group homogeneity) but with different clusters having members with dissimilar attributes (inter--group heterogeneity). The notion of polarization provides an important conceptual breakthrough in understanding inequality in societies because a society may be facing a decrease (increase) in inequality while at the same time experiencing an increase (decrease) in polarization. We conduct empirical analysis on a large sample of countries to demonstrate the positive link between polarization and political violence. In contrast, traditional measures of inequality perform poorly with the introduction of polarization in the model specification. Additionally, we conduct global sensitivity analysis to explore the robustness of the polarization measure to reasonable changes in the conditioning information set.

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Democracy as a Latent Variable Treier, Shawn Jackman, Simon Uploaded 07-16-2003 Keywords democracy Polity measurement latent variables Bayesian statistics item-response model ordinal data latent class analysis democratic peace Markov chain Monte Carlo Abstract Measurement is critical to the social scientific enterprise. Many key concepts in social-scientific theories are not observed directly, and researchers rely on assumptions (tacitly or explicitly, via formal measurement models) to operationalize these concepts in empirical work. In this paper we apply formal, statistical measurement models to the Polity indicators of democracy and autocracy, used widely in studies of international relations. In so doing, we make explicit the hitherto implicit assumptions underlying scales built using the Polity indicators. We discuss two models: one in which democracy is operationalized as a latent continuous variable, and another in which democracy is operationalized as a latent class. Our modeling approaches allow us to assess the measurement error in the resulting measure of democracy. We show that this measurement error is considerable, and has substantive consequences when using a measure of democracy as an independent variable in cross-national statistical analysis. Our analysis suggests that skepticism as to the precision of the Polity democracy scale is well-founded, and that many researchers have been overly sanguine about the properties of the Polity democracy scale in applied statistical work.

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Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects Imai, Kosuke Keele, Luke Yamamoto, Teppei Uploaded 07-20-2009 Keywords causal inference causal mediation analysis direct and indirect eects linear structural equation models sequential ignorability unmeasured confounders Abstract Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines including epidemiology, political science, psychology, and sociology. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal path between the treatment and outcome variables. In this paper, we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identifying assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator if the linearity and no-interaction assumptions are satisfied in addition to the proposed assumption. We show that this assumption can easily be relaxed within the framework of LSEM. Second, we consider a simple nonparametric estimator of the ACME in order to relax distributional and functional form assumptions. We also discuss a more general nonparametric approach. Third, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the standard LSEM framework. Like the existing identifying assumptions, the proposed assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology.

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Multiculturalism, Diversity, and Prejudice Branton, Regina P. Jones, Bradford S. Uploaded 03-27-1999 Keywords random coefficients multilevel analysis multiculturalism racial politics Abstract In this paper, we consider the relationship between racial and ethnic diversity and individuals' assessments of racial and ethnic groups measured on several public opinion items. To examine these issues, we merge 1992 National Election Study data with U.S. Census Bureau demographic data measured at the congressional district level. We then develop an index of diversity that is based on the distribution of racial and ethnic groups in the congressional district. To examine the relationship between diversity and individual-level attitudes toward racial and ethnic groups, we estimate a series of models treating the response variable as a function of both individual-level attributes and district-level attributes. This approach allows us to assess, among other things, if diversity is associated with more positive or negative evaluations of racial and ethnic groups. The models herein are all estimated as mixed effects models to account for the clustering of observations within congressional districts. We find that diversity is associated with group affect and individuals' placement on policy issues; however, in contrast to some of the extant literature, we find that racial and ethnic diversity is nonlinearly associated with group affect: extremely low and extremely high levels of racial and ethnic diversity are associated with lower racial and ethnic group evaluations, while districts that are moderately diverse are associated with higher evaluations. This result also holds for some of the policy items examined. Specifically, we find that support for government assistance to blacks, and to a lesser extent, support for affirmative action, exhibits this nonlinearity with regard to racial and ethnic diversity. We also find this pattern for individuals' assessment of welfare recipients.

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Modelling Space and Time: The Event History Approach Beck, Nathaniel Uploaded 08-22-1996 Keywords duration analysis event history analysis time-series--cross-section data discrete duration data duration dependence Abstract This is an elementary exposition of duration modelling prepared for a volume in celebration of the 30th anniversary of the Essex Summer School (Research Strategies in the Social Sciences, Elinor Scarbrough and Eric Tanenbaum, editors). The approach is non-mathematical. The running example used is the King et al. model of cabinet durations with particular attention paid to detecting and interpreting duration dependence in that model. There is some new discussion of ascertaining duration dependence using discrete methods and the relationship between discrete duration data and binary time-series--cross-section data.

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A Bayesian analysis of the multinomial probit model using marginal data augmentation Imai, Kosuke van Dyk, David A. Uploaded 08-21-2002 Keywords Bayesian analysis Data augmentation Prior distributions Probit models Rate of convergence Abstract We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesian representation of the model places a new, and possibly improper, prior distribution directly on the identifiable parameters and thus is relatively easy to interpret and use. Our algorithms, which are based on the method of marginal data augmentation, involve only draws from standard distributions and dominate other available Bayesian methods in that they are as quick to converge as the fastest methods but with a more attractive prior specification.

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A General Approach to Causal Mediation Analysis Imai, Kosuke Keele, Luke Tingley, Dustin Uploaded 07-20-2009 Keywords causal inference causal mechanisms sensitivity analysis sequential ignorability structural equation modeling unobserved confounder Abstract In a highly influential paper, Baron and Kenny (1986) proposed a statistical procedure to conduct a causal mediation analysis and identify possible causal mechanisms. This procedure has been widely used across many branches of the social and medical sciences and especially in psychology and epidemiology. However, one major limitation of this approach is that it is based on a set of linear regressions and cannot be easily extended to more complex situations that are frequently encountered in applied research. In this paper, we propose an approach that generalizes the Baron-Kenny procedure. Our method can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. We also provide a formal statistical justification for the proposed generalization of the Baron-Kenny procedure by placing causal mediation analysis within the widely-accepted counterfactual framework of causal inference. Finally, we develop a set of sensitivity analyses that allow applied researchers to quantify the robustness of their empirical conclusions. Such sensitivity analysis is important because as we show the Baron-Kenny procedure and our generalization of it rest on a strong and untestable assumption even in randomized experiments. We illustrate the proposed methods by applying them to a randomized field experiment, the Job Search Intervention Study (JOBS II). We also offer easy-to-use software that implements all of our proposed methods.

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Time Series Models for Compositional Data Brandt, Patrick T. Monroe, Burt L. Williams, John T. Uploaded 07-09-1999 Keywords compositional data VAR time series analysis bootstrap Monte Carlo simulation macropartisanship Abstract Who gets what? When? How? Data that tell us who got what are compositional data - they are proportions that sum to one. Political science is, unsurprisingly, replete with examples: vote shares, seat shares, budget shares, survey marginals, and so on. Data that also tell us when and how are compositional time series data. Standard time series models are often used, to detrimental consequence, to model compositional time series. We examine methods for modeling compositional data generating processes using vector autoregression (VAR). We then use such a method to reanalyze aggregate partisanship in the United States.

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Using Cluster Analysis to Derive Early Warning Indicators for Political Change in the Middle East, 1979-1996 Schrodt, Philip A. Gerner, Deborah J. Uploaded 08-22-1996 Keywords event data conflict early warning Middle East cluster analysis genetic algorithms Abstract This paper uses event data to develop an early warning model of major political changes in the Levant for the period April 1979 to July 1996. Following a general review of statistical early warning research, the analysis focuses on the behavior of eight Middle Eastern actorsÑEgypt, Israel, Jordan, Lebanon, the Palestinians, Syria, the United States and USSR/RussiaÑusing WEIS-coded event data generated from Reuters news service lead sentences with the KEDS machine-coding system. The analysis extends earlier work (Schrodt and Gerner 1995) demonstrating that clusters of behavior identified by conventional statistical methods correspond well with changes in political behavior identified a priori. We employ a new clustering algorithm that uses the correlation between the dyadic behaviors at two points in time as a measure of distance, and identifies cluster breaks as those time points that are closer to later points than to preceding points. We also demonstrate that these data clusters begin to "stretch" prior to breaking apart; this characteristic is used as an early-warning indicator. A Monte- Carlo analysis shows that the clustering and early warning measures perform very differently in simulated data sets having the same mean, variance, and autocorrelation as the observed data (but no cross-correlation) which reduces the likelihood that the clustering patterns are due to chance. The initial analysis uses Goldstein's (1992) weighting system to aggregate the WEIS-coded data. In an attempt to improve on the Goldstein scale, we use a genetic algorithm to optimize the weighting of the WEIS event categories for the purpose of clustering. This does not prove very successful and only differentiates clusters in the first half of the data set, a result similar to one we obtained using the cross-sectional K- Means clustering procedure. Correlating the frequency of events in the twenty-two 2-digit WEIS categories, on the other hand, gives clustering and early warning results similar to those produced by the Goldstein scale. The paper concludes with some general remarks on the role of quantitative early warning and directions for further research. This paper was presented at the American Political Science Association, San Francisco, 28 August - 1 September 1996

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A Robust Transformation Procedure for Interpreting Political Texts Martin, Lanny Vanberg, Georg Uploaded 04-25-2006 Keywords content analysis wordscores Abstract In a recent article in the American Political Science Review, Laver, Benoit, and Garry propose a new method for conducting content analysis. Their Wordscores approach, by automating text coding procedures, represents a fundamental advance in content analysis and will potentially have a large long-term impact on research across the discipline. In this research note, we contend that the usefulness of this procedure is unfortunately limited by the fact that the transformation procedure used by the authors (which is meant to allow for the substantive interpretation of results) has two significant shortcomings. Specifically, it distorts the metric on which content scores are placed—hindering the ability of scholars to make meaningful comparisons across texts—and it is very sensitive to the texts that are scored—opening up the possibility that researchers may generate, inadvertently or not, results that depend on the texts they choose to include in their analyses. We propose (and have written program code to implement) a transformation procedure that solves these problems.

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Quantitative Discovery from Qualitative Information: A General-Purpose Document Clustering Methodology King, Gary Grimmer, Justin Uploaded 07-19-2009 Keywords unsupervised learning discovery content analysis Abstract Many people attempt to discover useful information by reading large quantities of unstructured text, but because of known human limitations even experts are ill-suited to succeed at this task. This difficulty has inspired the creation of numerous automated cluster analysis methods to aid discovery. We address two problems that plague this literature. First, the optimal use of any one of these methods requires that it be applied only to a specific substantive area, but the best area for each method is rarely discussed and usually unknowable ex ante. We tackle this problem with mathematical, statistical, and visualization tools that define a search space built from the solutions to all previously proposed cluster analysis methods (and any qualitative approaches one has time to include) and enable a user to explore it and quickly identify useful information. Second, in part because of the nature of unsupervised learning problems, cluster analysis methods are not routinely evaluated in ways that make them vulnerable to being proven suboptimal or less than useful in specific data types. We therefore propose new experimental designs for evaluating these methods. With such evaluation designs, we demonstrate that our computer-assisted approach facilitates more efficient and insightful discovery of useful information than either expert human coders using qualitative or quantitative approaches or existing automated methods. We (will) make available an easy-to-use software package that implements all our suggestions.

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Age-Period-Cohort Analysis with Noisy, Lumpy Data Brady, Henry E. Elms, Laurel Uploaded 07-14-1999 Keywords cohort analysis smoothing political participation Abstract We have developed several relatively simple methods for doing age-period-cohort analysis with noisy, lumpy data. The first method, using additional information from the Census, does not work well with our data constraints because the age composition of the population does not vary enough over relatively short periods of time. The second method, approximating APC surfaces with polynomial functions, smooths the data too much. This approach is very much a brute force curve-fitting exercise that makes a very general assumption about the functional form of the APC surface and then fits it to the data. However, a third technique we evaluate starts with a theoretically informed model of how APC effects operate for a given dependent variable. This method allows for hypothesis testing and a reasonable amount of smoothing, but probably does not smooth period effects enough. It also yields interesting results about age, period, and cohort effects. The last method we discuss briefly, combining the third technique with additional smoothing, needs more development but may improve our estimates.

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The Diffusion of Democracy, 1946-1994 O'Loughlin, John Ward, Michael D. Lofdahl, Corey L. Cohen, Jordin S. Brown, David S. Reilly, David Gleditsch, Kristian S. Shin, Michael E. Uploaded 11-12-1997 Keywords Spatial diffusion exploratory spatial data analysis spatial statistics regional effects democracy measures of democracy space-time autocorrelation Abstract Research to date on democratization neglects the interconnections between temporal and spatial components that influence this process. This article presents research that reveals the relationship between the temporal and spatial aspects of democratic diffusion in the world-system since 1946. We provide strong and consistent evidence of temporal cascading of democratic and autocratic trends as well as strong spatial association (or autocorrelation) of authority structures. The analysis uses an exploratory data approach in a longitudinal framework to understand global and regional trends in democratization. Our work also reveals discrete changes in regimes that run counter to the dominant aggregate trends of democratic waves or sequences. We demonstrate how the ebb and flow of democracy varies between the world's regions. We conclude that further modeling of the process of regime change from autocracy to democracy as well as reversals should start from a "domain-specific" position that disaggregates the globe into its regional mosaics.

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A Hierarchical Bayesian Framework for Item Response Theory Models with Applications in Ideal Point Estimation Lu, Ying Wang, Xiaohui Uploaded 07-15-2006 Keywords item response theory testlet response theory random and fixed effect models vote cast data roll call analysis Abstract Ideal point estimation, a variation of item response theory models, has been widely used by political scientists to analyze legislative behaviors. However, many existing ideal point estimation research is based on unrealistic assumptions of independence of different individuals' decisions towards all cases/bills and the independence of one's decisions towards different cases/bills. The violation of such assumptions leads to bias and inefficiency in parameter estimation. More importantly, failing to address these assumptions has hampered the ideal point estimation research from offering intuitive and concise explanations on complex legislative behaviors such as multidimensionality, strategic voting, temporary coalitions. In this paper, we extend one testlet response theory model by Bradlow, Wainer and Wang(1999) to a comprehensive hierarchical Bayesian statistical framework that allows researchers to model inter-individual and intra-individual correlations through random effects and/or fixed effects. Through simulations and an analysis of the US Supreme Court vote cast data, we show that the proposed framework holds good promise for tackling many unsettled issues in ideal point estimations. As a companion to this paper, we also offer an easy-to-use R package with C code that implements the methods discussed herein.

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Invaluable Involvement: Purposive Interest Group Networks in the 21st Century Uploaded 02-04-2010 Keywords Network Analysis Interest Groups Amicus Curiae Coalition Strategy Abstract We present the first comprehensive social network analysis of purposive and coordinated interest group relationships. We utilize a network measure based on cosigner status to United States Supreme Court amicus curiae, or friend of the court briefs. The illuminated structures lend insight into the central players and overall formation of the network over the first seven years of the 21st century. We find that the majority of interest groups primarily partake in coalition strategies with other groups of similar policy interest and ideological character. This is in contrast to previous literature that focused only on one or the other. Network analysis provides evidence, for example, that the National Wildlife Foundation, the National Association of Criminal Defense Lawyers and the American Civil Liberties Union are all particularly strong groups, but exploit different central roles.

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The Influence of the Initiative Process on Interest Groups and Lobbying Techniques Boehmke, Frederick Uploaded 09-22-1999 Keywords Initiative direct democracy survey analysis interest groups lobbying selection bias Abstract I use survey data on interest groups and their activities drawn from four state populations to test hypotheses about the implications of direct democracy for the characteristics and strategic choices of interest groups. I use this data to test predictions about direct democracy's effect for group populations, confirming previous work (Boehmke 1999b) and extending it by exploring more detailed characteristics such as membership and resources. I then link these characteristics to lobbying techniques to test if the initiative process has an impact at the group level. As expected, groups involved in initiative campaigns tend to accentuate outside lobbying strategies, but even groups not currently involved in initiatives are influenced by the possibility of its use. This is because the initiative process alters the characteristics that can be effectively used when attempting to influence policy. The analysis makes use of a technique to correct for heterogeneous response rates across group types. By gathering information about a high percentage of an additional, smaller sample, I am able to correct for this response rate differential through a weighting procedure. The correction is found to have a substantial effect on the results: its absence would leave the researcher to conclude that the initiative plays little role in state interest group activities. This data will also be used to test and correct for possible sample selection bias.

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Estimating the Probability of Events That have Never Occurred: When Does Your Vote Matter? Gelman, Andrew King, Gary Boscardin, John Uploaded 10-27-1997 Keywords conditional probability decision analysis elections electoral campaigning forecasting political science presidential elections rare events rational choice subjective probability voting power Abstract Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that your vote will be decisive in a U.S. presidential election, a problem that has been studied by political scientists for more than two decades. The exact value of this probability is of only minor interest, but the number has important implications for understanding the optimal allocation of campaign resources, whether states and voter groups receive their fair share of attention from prospective presidents, and how formal ``rational choice'' models of voter behavior might be able to explain why people vote at all. We show how the probability of a decisive vote can be estimated empirically from state-level forecasts of the presidential election and illustrate with the example of 1992. Based on generalizations of standard political science forecasting models, we estimate the (prospective) probability of a single vote being decisive as about 1 in 10 million for close national elections such as 1992, varying by about a factor of 10 among states. Our results support the argument that subjective probabilities of many types are best obtained via empirically-based statistical prediction models rather than solely mathematical reasoning. We discuss the implications of our findings for the types of decision analyses that are used in public choice studies.

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An Automated Method of Topic-Coding Legislative Speech Over Time with Application to the 105th-108th U.S. Senate Quinn, Kevin Monroe, Burt Colaresi, Michael Crespin, Michael Radev, Dragomir Uploaded 07-18-2006 Keywords legislatures agendas content analysis Bayesian time series cluster analysis unsupervised learning Abstract We describe a method for statistical learning from speech documents that we apply to the Congressional Record in order to gain new insight into the dynamics of the political agenda. Prior efforts to evaluate the attention of elected representatives across topic areas have largely been expensive manual coding exercises and are generally circumscribed along one or more features of detail: limited time periods, high levels of temporal aggregation, and coarse topical categories. Conversely, the Congressional Record has scarcely been used for such analyses, largely because it contains too much information to absorb manually. We describe here a method for inferring, through the patterns of word choice in each speech and the dynamics of word choice patterns across time, (a) what the topics of speeches are, and (b) the probability that attention will be paid to any given topic or set of topics over time. We use the model to examine the agenda in the United States Senate from 1997-2004, based on a new database of over 70 thousand speech documents containing over 70 million words. We estimate the model for 42 topics and provide evidence that we can reveal speech topics that are both distinctive and inter-related in substantively meaningful ways. We demonstrate further that the dynamics our model gives us leverage into important questions about the dynamics of the political agenda.

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Unpacking the Black Box: Learning about Causal Mechanisms from Experimental and Observational Studies Imai, Kosuke Keele, Luke Tingley, Dustin Yamamoto, Teppei Uploaded 07-01-2010 Keywords causal inference direct and indirect effects mediation moderation potential outcomes sensitivity analysis media cues incumbency effects Abstract Understanding causal mechanisms is a fundamental goal of social science research. Demonstrating whether one variable causes a change in another is often insufficient, and researchers seek to explain why such a causal relationship arises. Nevertheless, little is understood about how to identify causal mechanisms in empirical research. Many researchers either informally talk about possible causal mechanisms or attempt to quantify them without explicitly stating the required assumptions. Often, some assert that process tracing in detailed case studies is the only way to evaluate causal mechanisms. Others contend the search for causal mechanisms is so elusive that we should instead focus on causal effects alone. In this paper, we show how to learn about causal mechanisms from experimental and observational studies. Using the potential outcomes framework of causal inference, we formally define causal mechanisms, present general identification and estimation strategies, and provide a method to assess the sensitivity of one's conclusions to the possible violations of key identification assumptions. We also propose several alternative research designs for both experimental and observational studies that may help identify causal mechanisms under less stringent assumptions. The proposed methodology is illustrated using media framing experiments and observational studies of incumbency advantage.

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A Spectral Analysis of Military Expenditures: Implications for Data and Theory Gerace, Michael P. Uploaded 11-13-1999 Keywords spectral analysis military expenditures defense economics arms race Abstract This paper employs spectral analysis on the military expenditures of 7 countries across two broad time periods. The countries are the United States, Britain, France, Germany, Italy, Russia and Japan and the periods are 1872-1913 and 1950-1991 (less Russia in the second period). Periodograms of the 13 military expenditure variables are estimated in order to evaluate the variance structure of each variable. This procedure is conducted on each variable in its trending form and after detrending (26 times in all). While the trend in the data accounts for most of the variance in the levels of the data, the importance of the trend and the length of the period defining the trend seem to be influenced by the presence of war in the data. Despite these differences, however, the trend remains the most important feature of the data. The detrended data indicate that numerous influences converge on military expenditures, as is indicated by the large number of periods with significant waves. The large number of periods in the data attest to the difficulty of estimating a parsimonious model in the time domain. The idea of extracting certain portions from the data to estimate relationships in the time domain is briefly explored.

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Do Voters Learn from Presidential Election Campaigns? Alvarez, R. Michael Glasgow, Garrett Uploaded 10-27-1997 Keywords random effects panel models content analysis presidential election campaigns voter decisionmaking voter learning Abstract We present a model of voter campaign learning which is based on Bayesian learning models. This model assumes voters are imperfectly informed and that they incorporate new information into their existing perceptions about candidate issue positions in a systematic manner. Additional information made available to voters about candidate issue positions during the course of a political campaign will lead voters to have more precise perceptions of the issue positions of the candidates involved. We use panel survey data from the 1976 and 1980 presidental elections, combined with content analyses of the media during these same elections. Our primary analysis is conducted using random effects panel models. We find that during each of these campaigns many voters became better informed about the positions of candidates on many issues and that these changes in voter information are directly related to the information flow during each presidential campaign.

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Statistical Analysis of Randomized Experiments with Nonignorable Missing Binary Outcomes Imai, Kosuke Uploaded 07-24-2006 Keywords Causal Inference Instrumental Variables Intention-to-Treat Effect Latent Ignorability Noncompliance Treatment Effect Sensitivity Analysis Abstract Missing data are frequently encountered in the statistical analysis of randomized experiments. In this article, I propose statistical methods that can be used to analyze randomized experiments with a nonignorable missing binary outcome where the missing-data mechanism may depend on the unobserved values of the outcome variable itself. I first introduce an identification strategy for the average treatment effect and compare it with the existing alternative approaches in the literature. I then derive the maximum likelihood estimator and its asymptotic properties, and discuss possible estimation methods. Furthermore, since the proposed identification assumption is not directly verifiable from the data, I show how to conduct a sensitivity analysis based on the parameterization that links the key identification assumption with the causal quantities of interest. Then, the proposed methodology is extended to the analysis of randomized experiments with noncompliance. Although the method introduced in this article may not directly apply to randomized experiments with non-binary outcomes, I briefly discuss possible identification strategies in more general situations. Finally, I apply the proposed methodology to analyze data from the German election experiment and the influenza vaccination study, which originally motivated the methodological problems addressed in this article.

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The Government Agenda in Parliamentary Democracies Martin, Lanny W. Uploaded 12-06-1999 Keywords agenda politics survival analysis nonproportional hazard Abstract In this paper, I examine the effects of party ideology and legislative institutions on the organization of the government agenda in parliamentary democracies. Analyzing data on the timing and policy content of over eight hundred government bills from the 1980s for four European democracies, I show that cabinets schedule bills earlier on the legislative agenda the greater their saliency to the prime minister and to those ministers responsible for their formulation and implementation. Moreover, I show that cabinets tend to delay bills that impose ideological compromise on cabinet members or that create conflict between the cabinet and parties in the opposition, particularly in periods of minority government. I find that all of these effects are greater at the beginning of a government’s tenure and decline by varying degrees over time.

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Pauline, the Mainstream, and Political Elites: the place of race in Australian political ideology Jackman, Simon Uploaded 08-25-1997 Keywords public opinion political ideology political elites race immigration Australian politics factor analysis ideological locations density estimation plotting highest density regions Abstract An often heard claim in the current ``race debate'' is that Australia's major political parties are out of touch with ``mainstream'' Australia on issues related to race. Parallel surveys of the electorate and candidates in the 1996 Federal election allow this claim to be tested, with items tapping general ideological dispositions, but including questions about Aboriginal Australians, immigration, and links with Asia. I make three critical findings: egin{itemize} item the electorate holds quite conservative opinions on these issues relative to the candidates, and is quite distant from ALP candidates in particular; item attitudes on racial issues are a powerful component of the electorate's otherwise relatively loosely organized political ideology, so much so that any categorisation of Australian political ideology ignoring race must be considered incomplete; item racial attitudes cut across other components of the electorate's ideology, placing all the parties under internal ideological strains, but the ALP appears particularly vulnerable on this score. end{itemize} While the data show the Coalition to be the net beneficiary of the ideological tensions posed by race, the formation of Pauline Hanson's One Nation party has exposed the Coalition's vulnerability to race as a cross-cutting political issue. Racial issues thus have many characteristics of a realigning dimension in Australian politics.

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Extracting Systematic Social Science Meaning from Text Hopkins, Daniel King, Gary Uploaded 07-12-2007 Keywords automated content analysis machine learning simulated extrapolation non-parametric estimation internet 2008 U.S. Presidential election Abstract We develop two methods of automated content analysis that give approximately unbiased estimates of quantities of theoretical interest to social scientists. With a small sample of documents hand coded into investigator-chosen categories, our methods can give accurate estimates of the proportion of text documents in each category in a larger population. Existing methods successful at maximizing the percent of documents correctly classified allow for the possibility of substantial estimation bias in the category proportions of interest. Our first approach corrects this bias for any existing classifier, with no additional assumptions. Our second method estimates the proportions without the intermediate step of individual document classification, and thereby greatly reduces the required assumptions. For both methods, we also correct statistically, apparently for the first time, for the far less-than-perfect levels of inter-coder reliability that typically characterize human attempts to classify documents, an approach that will normally outperform even population hand coding when that is feasible. We illustrate these methods by tracking the daily opinions of millions of people about candidates for the 2008 presidential nominations in online blogs, data we introduce and make available with this article, and through evaluations in available corpora from other areas, including movie reviews, university web sites, and Enron emails. We also offer easy-to-use software that implements all methods described.

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Analyzing the robustness of semi-parametric duration models for the study of repeated events models Box-Steffensmeier, Janet Linn, Suzanna Smidt, Corwin Uploaded 08-25-2010 Keywords repeated events event history analysis Abstract Estimators within the Cox family are often used to estimate models for repeated events. Yet there is much we do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, varying number of events experienced, and varying sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these things change and also under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes and conditions.

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Regression Analysis and the Philosophy of Social Sciences -- a Critical Realist View Ron, Amit Uploaded 12-20-1999 Keywords Regression analysis empiricism critical realism philosophy of social science Abstract This paper challenges the connection conventionally made between regression analysis and the empiricist philosophy of science and offers an alternative explication for the way regression analysis is being practiced. The alternative explication is based on critical realism, a competing approach to empiricism in the field of philosophy of science. The paper argues that critical realism can better explicate the way in which scientists ‘play’ with the data as part of the process of inquiry. The practice of regression analysis is understood by the critical realist explication as a post hoc attempt to identify a restricted closed system. The gist of successful regression analysis is not being able to offer a law-like statement but to bring forth evidence of an otherwise hidden mechanism. Through the study methodological debates regarding regression analysis, it is argued that critical realism can offer conceptual tools for better understanding the core issues that are at stake in these debates.

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Early Warning of Conflict in Southern Lebanon using Hidden Markov Models Schrodt, Philip A. Uploaded 08-24-1997 Keywords hidden Markov models event data early warning international crisis sequence analysis Middle East WEIS BCOW Abstract This paper extends earlier work on the application of hidden Markov models (HMMs) to the problem of forecasting international conflict. HMMs are a sequence comparison method widely used in computerized speech recognition as a computationally efficient method of generalizing a set of sequences observed in a noisy environment. The technique is easily be adapted to work with sequences of international event data. The paper provides a theoretical "micro-foundation" for the use of sequence comparison in conflict early- warning based on coadaptation of organizational standard operating procedures. The left-right (LR) HMM used in speech recognition is first extended to a left-right-left (LRL) model that allows a crisis to escalate and de-escalate. This model is tested for its ability to correctly discriminate between BCOW crisis that involve and do not involve war. The LRL model provides slightly more accurate classification than the LR model. The interpretation of the hidden states in the LRL models, however, is more ambiguous than in the LR model. The HMM is then applied to the problem of forecasting the outbreak of armed violence between Israel and Arab forces in south Lebanon during the period 1979 to 1997 (excluding 1982-1985). An HMM is estimated using six cases of "tit-for-tat" escalation, then fitted to the entire time period. The model identifies about half of the TFT conflictsÑincluding all of the training casesÑthat occur in the full sequence, with only one false positive. This result suggests that HMMs could be used in an event-based monitoring system. However, the fit of the model is very sensitive to the number of days in a sequence when no events occurred, and consequently the fit measure is ineffective as an early warning indicator. Nonetheless, in a subset of models, the maximum likelihood estimate of the sequence of hidden Markov states provides a robust early warning indicator with a three to six-month lead. These models are valid in a split-sample test, and the patterns of cross-correlation of the individual states of the model are consistent with the theoretical expectations. While this approach clearly needs further validation, it appears promising. The paper concludes with observations on the extent to which the HMM approach can be generalized to other categories of conflict, some suggestions on how the method of estimation can be improved, and the implications that sequence-based forecasting techniques have for theories of the causes of conflict.

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An Introduction to the Dataverse Network as an Infrastructure for Data Sharing King, Gary Uploaded 09-16-2007 Keywords data sharing replication citation analysis archiving preservation informatics Abstract We introduce a set of integrated developments in web application software, networking, data citation standards, and statistical methods designed to increase scholarly recognition for data contributions; put some of the universe of data and data sharing practices on firmer ground; and facilitate the public distribution of persistent, authorized, and verifiable data, with powerful and easy-to-use technology, even when the data are confidential or proprietary. Our goal is to solve some of the political and sociological problems of data sharing via technological means, with the result intended to benefit both the scientific community and the sometimes apparently contradictory goals of individual researchers. (More information on this project is available at http://TheData.org.)

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Noncommutative harmonic analysis of voting in small committees Lawson, Brian Orrison, Michael Uminsky, David Uploaded 07-13-2003 Keywords spectral analysis noncommutative harmoinc analysis voting analysis supreme court Abstract This paper introduces a new method, noncommutative harmonic analysis, as a tool for political scientists. The method is based on recent results in mathematics which systematically identify coalitions in voting data. The first section shows how this new approach, noncommutative harmonic analysis is a generalization of classical spectral analysis. The second section shows how noncommutative harmonic analysis is applied to a hypothetical example. The third section uses noncommutative harmonic analysis to analyze coalitions on the Supreme Court. The final section suggests ideas for extending the approach presented here to the study of voting in legislatures and preferences over candidates in multicandidate mass elections.

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Operationalizing and Testing Spatial Theories of Voting Quinn, Kevin M. Martin, Andrew D. Uploaded 04-15-1998 Keywords spatial voting factor analysis multinomial probit multinomial logit Bayesian inference model comparison Bayes factors MCMC Dutch politics Danish politics Abstract Spatial models of voting behavior provide the foundation for a substantial number of theoretical results. Nonetheless, empirical work involving the spatial model faces a number of potential difficulties. First, measures of the latent voter and candidate issue positions must be obtained. Second, evaluating the fit of competing statistical models of voter choice is often more complicated than previously realized. In this paper, we discuss precisely these issues. We argue that confirmatory factor analysis applied to mass-level issue preference questions is an attractive means of measuring voter ideal points. We also show how party issue positions can be recovered using a variation of this strategy. We go on to discuss the problems of assessing the fit of competing statistical models (multinomial logit vs. multinomial probit) and competing explanations (those based on spatial theory vs. those derived from other theories of voting such as sociological theories). We demonstrate how the Bayesian perspective not only provides computational advantages in the case of fitting the multinomial probit model, but also how it facilitates both types of comparison mentioned above. Results from the Netherlands and Denmark suggest that even when the computational cost of multinomial probit is disregarded, the decision whether to use multinomial probit (MNP) or multinomial logit (MNL) is not clear-cut.

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The Analysis of Binary Time-Series--Cross-Section Data and/or The Democratic Peace Beck, Nathaniel Katz, Jonathan Uploaded 07-18-1997 Keywords binary dependent variable time-series--cross-section data serially correlated errors event history analysis Abstract The analysis of binary time-series--cross-section (BTSCS) data almost invariably ignores temporal dependence. Using Monte Carlo we show that ordinary probit standard errors underestimate variability in the presence of serially correlated errors. This underestimate, while severe, is smaller than in a corresponding OLS analysis. The simulations show that the standard errors can be partially corrected using Huber's method. We then discuss a variety of other methods for allowing temporal dependency in BTSCS estimation. The simulations show that using a lagged dependent variable will not be the panacea that it is for continuous data. We briefly examine the ``general estimating equation approach.'' We then note the equivalence of BTSCS and event history data, and thus show that common event history techniques which allow for ``duration dependence'' can be used for temporally dependent BTSCS data. The methods are used to re-analyze Oneal and Russett's study of the role of democracy and trade in facilitating peace. After correcting for temporal dependence we still find support for the democratic peace hypothesis but no longer find support for the liberal trade hypothesis.

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Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type Pemstein, Daniel Meserve, Stephen Melton, James Uploaded 02-07-2008 Keywords democracy measurement democracy measurement regime regime type latent variable analysis Bayesian latent variable analysis UDS Unified Democracy Scores multi-rater ordinal probit Abstract Using a Bayesian latent variable approach, we synthesize a new measure of democracy, the Unified Democracy Scores (UDS), from ten extant scales. We accompany this new scale with quantitative estimates of uncertainty, provide estimates of the relative reliability of the constituent indicators, and quantify what the ordinal levels of each of the existing measures mean in relationship to one another. Our method eschews the difficult -- and often arbitrary -- decision to use one existing democracy scale over another in favor of a cumulative approach that allows us to simultaneously leverage the measurement efforts of numerous scholars.

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Agglomerative Clustering of Rankings Data, with an Application to Prison Rodeo Events Zorn, Christopher Uploaded 07-03-2003 Keywords Cluster analysis ordinal data classification rankings dissimilarity Abstract This paper considers the problem of assessing item similarity on the basis of rankings data, that is, data on ordinal outcomes. I discuss a modification to the standard dissimilarity measure used in agglomerative clustering which addresses the ordinal nature of ranking data. I then apply this alternative to cluster nine events comprising the Angola, Louisiana prison rodeo.

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Who Votes By Mail? A Dynamic Model of the Individual-Level Consequences of Vote-by-Mail Systems Berinsky, Adam Burns, Nancy Traugott, Michael Uploaded 04-17-1998 Keywords turnout vote-by-mail duration analysis continuous-time multistate duration model Abstract Throughout the years, a number of changes have been proposed to electoral laws with the aim of increasing voter turnout and altering the composition of the electorate to make it more reflective of the voting age population. The most recent of these innovations is voting-by-mail (VBM). While the use of VBM has spread through the United States, little empirical evaluation of the impact of VBM has been undertaken to date. The analysis presented here fills this gap in our knowledge by assessing the impact of VBM on the Oregon electorate through a multistate duration analysis (Heckman and Singer, 1984; Heckman and Walker, 1986, 1991) that takes into account other factors associated with election administration and characteristics of individual voters. This methodology has the added advantage of providing a reasonable basis for extrapolation of these effects to other jurisdictions. The results of our research suggest that VBM does increase voter turnout in the aggregate, although its effects are not uniform across all groups in the electorate. More importantly, it does not seem to exert any influence on the partisan composition of the electorate. From a methodological perspective, the use of a multistate duration analysis provides a promising approach to extrapolating the impact of a policy change from one jurisdiction to another when appropriate data are available in each.

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Modeling Multilevel Data Structures Jones, Bradford S. Steenbergen, Marco R. Uploaded 07-17-1997 Keywords multilevel models random coefficients contextual analysis comparative Abstract Although integrating multiple levels of data into an analysis can often yield better inferences about the phenomenon under study, traditional methodologies used to combine multiple levels of data are problematic. In this paper, we discuss several methodologies under the rubric of multilevel analysis. Multilevel methods, we argue, provide researchers, particularly researchers using comparative data, substantial leverage in overcoming the typical problems associated with either ignoring multiple levels of data, or problems associated with combining lower-level and higher-level data (including overcoming implicit assumptions of fixed and constant effects). The paper discusses several variants of the multilevel model and provides an application of individual-level support for European integration using comparative political data from Western Europe.

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Estimating Binary Dependent Variable Models Under Conditions of Specification Uncertainty Berry, William DeMeritt, Jacqueline Esarey, Justin Uploaded 01-25-2007 Keywords logit probit binary dependent variable specification uncertainty interaction Monte Carlo analysis Abstract Political scientists routinely use logit or probit models when their data involve binary dependent variables (BDVs). Yet the hypotheses we test with logit and probit are rarely specific enough to justify that one of these models is the correct functional form for the process (or true model) generating the data. In this situation of specification uncertainty, it is reasonable to assume that the model being estimated is misspecified. The only issue is the severity of the resulting distortion in results, i.e., whether logit or probit approximates the true model well enough to yield estimated effects that are acceptably close to the true ones. To study estimation in the presence of specification uncertainty, we conduct Monte Carlo analysis using a strategy of purposeful misspecification: we use various logit and probit models with different terms on data sets generated from a wide range of known true models involving a BDV, none of which takes the exact form of a logit or probit model. We find that a widely-employed approach for using logit or probit to test the hypothesis that an independent variable has a positive (or negative) effect on the probability that some event will occur-­by estimating the effect of the variable at central values of the independent variables­-is highly forgiving of specification uncertainty, yielding reasonably accurate inferences even when the true model is not logit or probit. Unfortunately, other applications of logit and probit­-including a common approach to testing a hypothesis that independent variables interact in influencing the probability of event occurrence­-are not nearly as forgiving of the uncertainty. In some situations of specification uncertainty, we can improve the quality of estimated effects by relying on the Akaike Information Criterion [AIC] to choose the terms to be included in a model, but even these improved estimates leave much to be desired.