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Below results based on the criteria 'ordinal'
Total number of records returned: 7
Democracy as a Latent Variable
latent class analysis
Markov chain Monte Carlo
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.
Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type
latent variable analysis
Bayesian latent variable analysis
Unified Democracy Scores
multi-rater ordinal probit
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.
Agglomerative Clustering of Rankings Data, with an Application to Prison Rodeo Events
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.
Connecting Interest Groups and Congress: A New Approach to Understanding Interest Group Success
Victor, Jennifer Nicoll
Bayesian Information Criterion
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.
Estimation and Inference by Bayesian Simulation: an on-line resource for social scientists
Markov chain Monte Carlo
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).
Markov Chain Models for Rolling Cross-section Data: How Campaign Events and Political Awareness Affect Vote Intentions and Partisanship in the United States and Canada
Mebane, Walter R.
rolling cross-section data
We use a new approach we have developed for estimating discrete, finite-state Markov chain models from ``macro'' data to analyze the dynamics of individual choice probabilities in two collections of rolling cross-sectional survey data that were designed to support investigations of what happens to voters' information and preferences during campaigns. Using data from the 1984 American National Election Studies Continuous Monitoring Study, we show that not only did individual party identification vary substantially during the year, but the dynamics of party identification changed significantly in response to the conclusion of the Democratic party's nomination contest. Party identification appears to have measurement error only when the model misspecifies the dynamics. There are rapid oscillations among some categories of partisanship that may reflect individual stances regarding not only competition between the parties but also competition among party factions. Using data from the 1993 Canadian Election Study, we show that the critical events that shaped voting intentions in the election varied tremendously depending on an individual's level of political awareness, and that the effects of awareness varied across regions of the country.
Tau-b or Not Tau-b: Measuring Alliance Portfolio Similarity
Signorino, Curtis S.
Ritter, Jeffery M.
The pattern of alliance commitments among states is commonly assumed to reflect the extent to which states have common or conflicting security interests. For the past twenty years, Kendall's tau-b has been used to measure the similarity between two nations' ``portfolios'' of alliance commitments. Widely employed indicators of systemic polarity, state utility, and state risk propensity all rely upon tau-b. We demonstrate that tau-b is inappropriate for measuring the similarity of states' alliance commitments. We develop an alternative measure of alliance portfolio similiarity, S, which avoids many of the problems associated with tau-b, and we use data on alliances among European states to compare the effects of S versus tau-b in measures of utility and risk propensity. Finally, we identify several problems with inferring state interest from alliance commitments and we provide a method to overcome those problems using S in combination with data on alliances, trade, UN votes, diplomatic missions, and other types of state interaction.