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119
Making the most of statistical analyses: Improving interpretation and presentation
 American Journal of Political Science
, 2000
"... Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. ..."
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Cited by 408 (22 self)
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Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a readerfriendly manner. Using this technique requires some expertise,
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
 American Political Science Review
, 2000
"... We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scatter ..."
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Cited by 306 (48 self)
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We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The reason for this discrepancy lies with the fact that the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise. In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, ...
Matching as Nonparametric Preprocessing for Reducing Model Dependence
 in Parametric Causal Inference,” Political Analysis
, 2007
"... Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other ..."
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Cited by 213 (41 self)
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Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fastgrowing methodological
The Social Structure of Entrepreneurial Activity
 Geographic Concentration of Footwear Production in the U.S., 1940–1989.’’ American Journal of Sociology 106:324–62
, 2000
"... Nearly all industries exhibit geographic concentration. Most theories of the location of industry explain the persistence of these production centers as the result of economic efficiency. This article argues instead that heterogeneity in entrepreneurial opportunities, rather than differential perfor ..."
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Cited by 85 (8 self)
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Nearly all industries exhibit geographic concentration. Most theories of the location of industry explain the persistence of these production centers as the result of economic efficiency. This article argues instead that heterogeneity in entrepreneurial opportunities, rather than differential performance, maintains geographic concentration. Entrepreneurs need exposure to existing organizations in the industry to acquire tacit knowledge, obtain important social ties, and build selfconfidence. Thus, the current geographic distribution of production places important constraints on entrepreneurial activity. Due to these constraints, new foundings tend to reify the existing geographic distribution of production. Empirical evidence from the shoe industry supports this thesis.
A Statistical Model for Multiparty Electoral Data
 American Political Science Review
, 1999
"... e propose a comprehensive statistical model for analyzing multiparty, districtlevel elections. This model, which provides a tool for comparative politics research analogous to that which regression analysis provides in the American twoparty context, can be used to explain or predict how geographic ..."
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Cited by 47 (12 self)
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e propose a comprehensive statistical model for analyzing multiparty, districtlevel elections. This model, which provides a tool for comparative politics research analogous to that which regression analysis provides in the American twoparty context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contraiy to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningfkl, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party's advantage is predominantly drawn. w e propose the first internally consistent statistical model for analyzing multiparty, districtlevel aggregate election data. Our model can
Poissonbased regression analysis of aggregate crime rates
 Journal of Quantitative Criminology
, 2000
"... This article introduces the use of regression models based on the Poisson distribution as a tool for resolving common problems in analyzing aggregate crime rates. When the population size of an aggregate unit is small relative to the offense rate, crime rates must be computed from a small number of ..."
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Cited by 41 (1 self)
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This article introduces the use of regression models based on the Poisson distribution as a tool for resolving common problems in analyzing aggregate crime rates. When the population size of an aggregate unit is small relative to the offense rate, crime rates must be computed from a small number of offenses. Such data are illsuited to leastsquares analysis. Poissonbased regression models of counts of offenses are preferable because they are built on assumptions about error distributions that are consistent with the nature of event counts. A simple elaboration transforms the Poisson model of offense counts to a model of per capita offense rates. To demonstrate the use and advantages of this method, this article presents analyses of juvenile arrest rates for robbery in 264 nonmetropolitan counties in four states. The negative binomial variant of Poisson regression effectively resolved difficulties that arise in ordinary leastsquares analyses. KEY WORDS: Poisson; negative binomial; crime rates; aggregate analysis. 1.
Unifying Political Methodology
, 1989
"... "political science statistics " (Rai and Blydenburgh 1973), "political statistics" ..."
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Cited by 35 (0 self)
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"political science statistics " (Rai and Blydenburgh 1973), "political statistics"
A scaling model for estimating timeseries party positions from texts
 American Journal of Political Science
, 2008
"... Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing textbased methods face challenges in producing valid and reliable timeseries data. This article proposes a scaling algorithm called WORDFISH to estimate pol ..."
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Cited by 35 (1 self)
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Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing textbased methods face challenges in producing valid and reliable timeseries data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing timeseries estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection. Many theories of comparative politics rely on the ability of researchers to locate political parties in a policy space. Theories of coalition formation and duration use party positions to predict which governments form and how long they survive (Baron
How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics
, 1990
"... This article demonstrates how the selection of cases for study on the basis of outcomes on the dependent variable biases conclusions. It first lays out the logic of explanation and shows how it is violated when only cases that have achieved the outcome of interest are studied. It then examines three ..."
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Cited by 30 (0 self)
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This article demonstrates how the selection of cases for study on the basis of outcomes on the dependent variable biases conclusions. It first lays out the logic of explanation and shows how it is violated when only cases that have achieved the outcome of interest are studied. It then examines three wellknown and highly regarded studies in the field of comparative politics, comparing the conclusions reached in the original work with a test of the arguments on cases selected without regard for their position on the dependent variable. In each instance, conclusions based on the uncorrelated sample differ from the original conclusions. Comparative politics, like other subfields in political science, has norms and conventions about what constitutes an appropriate research strategy and what kind of evidence makes an argument persuasive. One of our most durable conventions is the selection of cases for study on the dependent variable. 1 That is, if we want to understand something, for example, revolution, we
Toward a Common Framework for Statistical Analysis and Development
 Journal of Computational and Graphical Statistics
, 2008
"... We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R’s numerous existing packages. This framework offers a simple unified structure and syntax that can encompass ..."
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Cited by 27 (7 self)
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We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R’s numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.