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Below results based on the criteria 'statistics'
Total number of records returned: 16
Liberalism, Public Opinion, and their Critics: Some Lessons for Defending Science
Science and liberalism were both born out of the Enlightenment; liberalism's more-or-less successful defense against its critics may hold some insights for defenders of science against recent attacks. Liberalism, like science, is normatively thin, but procedurally rich. As such, liberalism and science has been able to accomodate shifting opinions about "the good" or "the truth" while pursuing it. For both science and liberalism, truth and "the good" are socially constructed, just as they themselves are socially constructed. This is sometimes overlooked. A brief history of the study of public opinion shows that liberalism's science -- political science, and the study of public opinion in particular -- is full of abstractions, metaphors, and approxiimations of reality that serve social ends. This can be used to disarm post-modern critics of science. The admission of a conetextualized basis for knowledge is not an abandonment of science, but rather an acknowledgement of the richness of the world, that is, if anything, an invitation to inquiry. This admission was the mutual origin of both science and liberalism, is the source of the their resiliance, and will ensure their safe passage throught the post-modern "storm".
The Diffusion of Democracy, 1946-1994
Ward, Michael D.
Lofdahl, Corey L.
Cohen, Jordin S.
Brown, David S.
Gleditsch, Kristian S.
Shin, Michael E.
exploratory spatial data analysis
measures of democracy
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.
Too many Variables? A Comment on Bartels' ModelAveraging Proposal
Erikson, Robert S.
Wright, Gerald C.
McIver, John P.
Bayesian Information Criterion
Abstract: Bartels (1997) popularizes the procedure of model- averaging (Raftery, 1995, 1997), making some important innovations of his own along the way. He offers his methodology as a technology for exposing excessive specification searches in other peoples' research. As a demonstration project, Bartels applied his version of model- averaging to a portion of our work on state policy and purports to detect evidence of considerable model uncertainty. . In response, we argue that Bartels' extensions of model averaging methodology are ill-advised, and show that our challenged findings hold up under the scrutiny of the original Raftery-type model averaging.
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.
Presidential Approval: the case of George W. Bush
dynamic linear model
Markov chain Monte Carlo
pages of killer graphs
We use a Bayesian dynamic linear model to track approval for George W. Bush over time. Our analysis deals with several issues that have been usually addressed separately in the extant literature. First, our analysis uses polling data collected at a higher frequency than is typical, using over 1,100 published national polls, and data on macro-economic conditions collected at the weekly level. By combining this much poll information, we are much better poised to examine the public's reactions to events over shorter time scales than can the typical analysis of approval that utilizes monthly or quarterly approval. Second, our statistical modeling explicitly deals with the sampling error of these polls, as well as the possibility of bias in the polls due to house effects. Indeed, quite aside from the question of ``what drives approval?'', there is considerable interest in the extent to which polling organizations systematically diverge from one another in assessing approval for the president. These bias parameters are not only necessary parts of any realistic model of approval that utilizes data from multiple polling organizations, but easily estimated via the Bayesian dynamics linear model.
Bargaining and Society: A Statistical Model of the Ultimatum Game
In this paper we derive a statistical estimator for the popular Ultimatum bargaining game. Using monte carlo data generated by a strategic bargaining process, we show that the estimator correctly recovers the relationship between dependent variables, such as the proposed division and bargaining failure, relative to substantive variables that comprise players' utilities. We then use the model to analyze bargaining data in a number of contexts. The current example examines the effects of demographics on bargaining behavior in experiments conducted on U.S. and Russian participants.
Using Graphs Instead of Tables to Improve the Presentation of Empirical Results in Political Science
When political scientists present empirical results, they are much more likely to use tables rather than graphs, despite the fact that the latter greatly increases the clarity of presentation and makes it easier for a reader or listener to draw clear and correct inferences. Using a sample of leading journals, we document this tendency and suggest reasons why researchers prefer tables. We argue the extra work required in producing graphs is rewarded by greatly enhanced presentation and communication of empirical results. We illustrate their benefits by turning several published tables into graphs, including tables that present descriptive data and regression results. We show that regression graphs properly emphasize point estimates and confidence intervals rather than null significance hypothesis testing, and that they can successfully present the results of multiple regression models. A move away from tables and towards graphs would increase the quality of the discipline's communicative output and make empirical findings more accessible to every type of audience.
Objections to Bayesian statistics
comparison of methods
foundations of statistics
Bayesian inference is one of the more controversial approaches to statistics. The fundamental objections to Bayesian methods are twofold: on one hand, Bayesian methods are presented as an automatic inference engine, and this raises suspicion in anyone with applied experience. The second objection to Bayes comes from the opposite direction and addresses the subjective strand of Bayesian inference. This article presents a series of objections to Bayesian inference, written in the voice of a hypothetical anti-Bayesian statistician. The article is intended to elicit elaborations and extensions of these and other arguments from non-Bayesians and responses from Bayesians who might have different perspectives on these issues.
Teaching Bayesian applied statistics to graduate students in political science, sociology, public health, education, economics, ...
I share some thoughts on teaching applied regression and Bayesian methods to students in political science and other fields.
Joint Modeling of Dynamic and Cross-Sectional Heterogeneity: Introducing Hidden Markov Panel Models
Park, Jong Hee
Hidden Markov models
Markov chain Monte Carlo methods
Reversible jump Markov chain Monte Carlo
Researchers working with panel data sets often face situations where changes in unobserved factors have produced changes in the cross-sectional heterogeneity across time periods. Unfortunately, conventional statistical methods for panel data are based on the assumption that the unobserved cross-sectional heterogeneity is time constant. In this paper, I introduce statistical methods to diagnose and model changes in the unobserved heterogeneity. First, I develop three combinations of a hidden Markov model with panel data models using the Bayesian framework; (1) a baseline hidden Markov panel model with varying fixed effects and varying random effects; (2) a hidden Markov panel model with varying fixed effects; and (3) a hidden Markov panel model with varying intercepts. Second, I present model selection methods to diagnose the dynamic heterogeneity using the marginal likelihood method and the reversible jump Markov chain Monte Carlo method. I illustrate the utility of these methods using two important ongoing political economy debates; the relationship between income inequality and economic growth and the effect of institutions on income inequality.
A Statistical Method for Empirical Testing of Competing Theories
finite mixture model
false discovery rate
Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a very general and well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the theories under consideration. Researchers can then estimate the probability that a specific observation is consistent with either of the competing theories. By directly modeling this probability with the characteristics of observations, one can also determine the conditions under which a particular theory applies. We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory. Finally, we propose several measures of the overall performance of a particular theory. We illustrate the advantages of our method by applying it to an influential study on trade policy preferences.
Statistical Estimation in the Presence of Multiple Causal Paths
Braumoeller, Bear F.
Large-N statistical methodology has in the past been criticized for its inability to model a phenomenon believed to be fundamental to social science research: equifinality, or multiple causal paths. I examine this claim and demonstrate that, although multiple causal paths are often hypothesized in political science research, tests rarely if ever reflect their logic. I then describe a procedure for constructing likelihood functions directly from the logic of multiple causal path theories so that the truth-status of such theories can be correctly evaluated with maximum likelihood techniques.
Political Methodology - A Welcoming Discipline
This article discusses, from my own perspective, political methodology at the age of twenty five years. In particular, I look at the relationship of political methodology to other methodological subdisciplines and to statistics, focussing on the division of labor among the various methodological disciplines. I also briefly discuss some issues in data collection.
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).
The Robustness of Normal-theory LISREL Models: Tests Using a New Optimizer, the Bootstrap, and Sampling Experiments, with Applications
Mebane, Walter R.
Wells, Martin T.
linear structural relations
Asymptotic results from theoretical statistics show that the linear structural relations (LISREL) covariance structure model is robust to many kinds of departures from multivariate normality in the observed data. But close examination of the statistical theory suggests that the kinds of hypotheses about alternative models that are most often of interest in political science research are not covered by the nice robustness results. The typical size of political science data samples also raises questions about the applicability of the asymptotic normal theory. We present results from a Monte Carlo sampling experiment and from analysis of two real data sets both to illustrate the robustness results and to demonstrate how it is unwise to rely on them in substantive political science research. We propose new methods using the bootstrap to assess more accurately the distributions of parameter estimates and test statistics for the LISREL model. To implement the bootstrap we use optimization software two of us have developed, incorporating the quasi-Newton BFGS method in an evolutionary programming algorithm. We describe methods for drawing inferences about LISREL models that are much more reliable than the asymptotic normal-theory techniques. The methods we propose are implemented using the new software we have developed. Our bootstrap and optimization methods allow model assessment and model selection to use well understood statistical principles such as classical hypothesis testing.
Pooling Disparate Observations
Bartels, Larry M.
Data analysts frequently face difficult choices about whether to pool disparate observations in their statistical analyses. I explore the inferential ramifications of such choices, and propose a new technique, dubbed "fractional pooling," which provides a simple way to incorporate prior beliefs about the theoretical relevance of disparate observations. The technique is easy to implement and has a plausible rationale in Bayesian statistical theory. I illustrate the potential utility of fractional pooling by applying the technique to political data originally analyzed by Ashenfelter (1994), Powell (1982), and Alesina et al. (1993). These examples demonstrate that conventional approaches to analyzing disparate observations can be seriously misleading, and that the approach proposed here can enrich our understanding of the inferential implications of unavoidably subjective judgments about the theoretical relevance of available data.