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Paper

Conservative Vote Probabilities: An Easier Method for the Analysis of Roll Call Data Fowler, Anthony Hall, Andrew B. Uploaded 08-08-2012 Keywords Roll Call Ideology Congress Supreme Court State Legislatures Non-parametric Abstract We propose a new roll-call scaling method based on OLS which is easier to implement and understand than previous methods and also produces directly interpretable estimates. This measure, Conservative Vote Probability (CVP), indicates the probability that an individual legislator votes "conservatively" relative to the median legislator. CVP is a flexible non-parametric statistical technique that requires no complicated assumptions but still produces legislator scalings that correlate with previous roll call methods at extremely high levels. In this paper we introduce the methodology behind CVP and offer several substantive examples to demonstrate its e efficacy as an easier, more accessible alternative to previous roll call methods.

2

Paper

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.

3

Paper

Non-Parametric Analysis of Binary Choice Data Poole, Keith T. Uploaded 06-16-1997 Keywords discrete choice analysis non-parametric unfolding Abstract This paper shows a general non-parametric technique for maximizing the correct classification of binary choice or two-category data. Two general classes of data are analyzed. The first consists of binary choice matrices such as congressional roll calls or preferential rank ordering of stimuli gathered from individuals. For this class of data a general non-parametric unfolding procedure is developed. To unfold binary choice data two subproblems must be solved. First, given a set of chooser or legislator points a cutting plane through the space for the binary choice must be found such that it divides the legislators into two sets that reproduce the actual choices as closely as possible. Second, given a set of cutting planes for the binary choices a point for each chooser or legislator must be found that reproduces the actual choices as closely as possible. Solutions for these two problems are shown in this paper. The second class of data analyzed consists of a two-category dependent variable and a set of independent variables. This class of data is a subset of the binary choice unfolding problem. The cutting plane procedure can be used to estimate a cutting plane through the space of the independent variables that maximizes the number of correct classifications. The normal vector to this cutting plane closely corresponds to the beta vector from a standard probit, logit, or linear probability analysis.

4

Paper

Getting the Mean Right is a Good Thing: Generalized Additive Models Beck, Nathaniel Jackman, Simon Uploaded 01-30-1997 Keywords non-parametric regression scatterplot smoothing local fitting splines non-linearity Perot incumbency cabinet duration democratic peace Abstract This is a substantial revision of the paper submitted as beck96. A shorter version of this paper is under consideration at a political science journal of note. Theory: Social scientists almost always use statistical models positing the dependent variable as a linear function of X, despite suspicions that the social and political world is not so parsimonious. Generalized additive models (GAMs) permit each independent variable to be modelled non-parametrically while requiring that the independent variables combine additively, striking a sensible balance between the flexibility of non-parametric techniques and the ease of interpretation and familiarity of linear regression. GAMs thus offer social scientists a practical methodology for improving on the extant practice of ``linearity by default''. Method: We present the statistical concepts and tools underlying GAMs (e.g., scatterplot smoothing, non-parametrics more generally, and accompanying graphical methods), and summarize issues pertaining to estimation, inference, and the statistical properties of GAMs. Monte Carlo experiments assess the validity of tests of linearity accompanying GAMs. Re-analysis of published work in American politics, comparative politics, and international relations demonstrates the usefulness of GAMs in social science settings. Results: Our re-analyses of published work show that GAMs can extract substantive mileage beyond that yielded by linear regression, offering novel insights, particularly in terms of modelling interactions. The Monte Carlo experiments show there is little danger of GAMs spuriously finding non-linear structures. All data analysis, Monte Carlo experiments, and statistical graphs were generated using S-PLUS, Version 3.3. The routines and data are available at ftp://weber.uscd.edu/pub/nbeck/gam.

5

Paper

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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Paper

Non-parametric Mechanisms and Causal Modeling Glynn, Adam Quinn, Kevin Uploaded 07-15-2007 Keywords Neyman-Rubin model non-parametric structural equations causal inference covariate selection unmeasured confounding Abstract Political scientists tend to think about causality in terms of mechanisms. In this paper we argue that non-parametric structural equation models are consistent with how many empirical political scientists think about causality and are consistent with the powerful and well-respected Neyman-Rubin Causal Model. Furthermore, using examples we demonstrate that two important practical questions are more easily addressed within the mechanistic framework: What (if any) set or sets of conditioning variables will allow the identification of average causal effects in a regression or matching model? When unmeasured confounding is present, what (if any) adjustment will non-parametrically identify the average causal effect?

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Poster

Stronger Instruments by Design Morgan, Jason Keele, Luke Uploaded 07-31-2011 Keywords 2SLS instrumental variables matching non-parametric Abstract There is growing interest in natural experiments in political science. Natural experiments are often analyzed with instrumental variable estimators reflecting a belief that combining the power of natural random assignment with an instrumental variable approach will solve many of the research design problems endemic to social science. Here, we highlight how weak instruments can interact with the assumption of random assignment of the instrument. When the instrument is not randomly assigned, weak instruments produce bias that is not alleviated by additional data. We demonstrate how matching combined with a reverse caliper can be used to strengthen an instrument within a subset of the overall study. We start by presenting an alternative non-parametric instrumental variable estimator first proposed by Rosenbaum (1996) that allows us to combine matching with an IV estimator. Unlike the standard 2SLS IV estimator, this non-parametric approach provides accurate confidence intervals and consistent causal estimates even when the instrument is weak. A further advantage of this non-parametric method is the opportunity it provides to probe the random assignment assumption with a sensitivity test. We provide substantive examples of the proposed approach with a reevaluation of a recent paper that uses rainfall as an instrument for voter turnout in US counties (Hansford & Gomez 2010).