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Connecting Interest Groups and Congress: A New Approach to Understanding Interest Group Success Victor, Jennifer Nicoll Uploaded 07-16-2002 Keywords Interest Groups Congress Multiple Imputation Bayesian Information Criterion Ordinal Probit Non-nested Models Legislative Context Abstract The primary challenge in explaining interest group legislative success in Congress has been methodological. The discipline requires at least two critical elements to make progress on this important question. First, we need a theory that accounts for the highly interactive spatial game between interest groups and legislators. Second, the discipline needs an empirical model that associates interest groups and their activities with specific congressional bills. In this project I begin to contribute to our understanding of the complex relationship between interest groups and Congress. I develop a theory of group success that is based upon the strategies in which groups engage, the groups' organizational capacity, and the strategic context of legislation. I predict that groups will tailor their activities (and strategically spend their resources) in Congress based upon two critical factors: whether the group supports or opposes the legislation, and the legislative environment for the bill. To test this model I develop a unique sampling procedure and survey design. I use legislative hearings to generate a sample of groups that are associated with specific issues and survey them about their activities on those issues. Then, I associate each group's issue with a specific bill in Congress. I then track the bill to discern its final status. I create a dependent variable of interest group success that is based on the group's position (favor or oppose) and the final status of the bill. This sampling procedure and dependent variable allow me to make inferences about group behavior over specific legislative proposals. I develop independent variables of group activity, group organizational capacity, and legislative context from the survey instrument and objective information about the bills. To fill in gaps in the survey data set, I use a multiple imputation method that generates plausible values based on given distributions of data. I estimate two models-one for groups in favor of legislation, and one for opposition groups. The ordinal probit models generally support the theoretical expectations. In sum, I find that groups can best expend their resources in pursuit of rules that advantage their position rather than fighting for bill content.

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Estimation and Inference by Bayesian Simulation: an on-line resource for social scientists Jackman, Simon Uploaded 08-30-1999 Keywords Markov chain Monte Carlo Bayesian statistics how-to BUGS ordinal probit time series Abstract http://tamarama.stanford.edu/mcmc a Web-based on-line resource for Markov chain Monte Carlo, specifically tailored for social scientists. MCMC is probably the most exciting development in statistics in the last ten years. But to date, most applications of MCMC methods are in bio-statistics, making it difficult for social scientists to fully grasp the power of MCMC methods. In providing this on-line resource I aim to overcome this deficiency, helping to put MCMC in the reach of social scientists. The resource comprises: (*) a set of worked examples (*) data and programs (*) links to other relevant web sites (*) notes and papers At the meetings in Atlanta, I will present two of the worked examples, which are part of this document: (*) Cosponsor: computing auxiliary quantities from MCMC output (e.g., percent correctly predicted in a logit/probit model of legislative behavior; cf Herron 1999). (*) Delegation: estimating a time-series model for ordinal data (e.g., changes to the U.S. president's discretionary power in trade policy, 1890-1990; cf Epstein and O'Halloran 1996).

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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.