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New and Old Working Papers and Reports and Presentations

All titles are preliminary and comments are welcome. This collection is somewhat disorganized at the moment. Newer works are on the top and older/back-burner work, is on the bottom of each section.

Applied Statistics

Randomization and Design Based Statistical Inference for Causal Inferences

How to increase the precision of causal inferences in experiments using machine learning (but without data snooping). A paper showing how covariates can be used to increase the precision of statistical inferences about causal effects without data snooping (with Mark Fredrickson and Ben Hansen).

Design of Randomized Experiments on Networks When Treatment Propagates: An Exploration A presentation at the end of my participation in the Statistics and Applied Mathematical Sciences Institute year focused on computational social science that was also presented at the Political Networks Conference, May 2014. A collaboration with Bruce Desmarais, Mark Fredrickson, and Nahomi Ichino to learn about the design of randomized experiments to learn about how treatment may propagate across a social or geographic network.

Ethnicity and Electoral Fraud in New Democracies: Modelling Political Party Agents in Ghana A paper using randomization inference with agent-based models to learn about theories of party competition and ethnicity in Ghana (with Nahomi Ichino and Mark Fredrickson).

Regression without Regrets: A modular approach to linear models in (quasi)-experiments. Keywords: Causal inference; Machine Learning; Lasso; Randomization Inference

Do Newspaper Ads Raise Voter Turnout? (with Costas Panagopoulos)

A General Representation of Potential Outcomes for Graphs/Networks (draft)

Fisher's randomization mode of statistical inference, then and now. How is statistical inference possible when n = 8? How can we infer without a sample from a population? How should we choose methods for assessing causal claims when we have low information (like a small sample, a binary outcome, a multilevel design with few clusters, or a weak instrument)? R. Fisher answered these questions in 1935 showing that valid small sample hypothesis tests are possible, inference does not require a population, and choices about assessing causal effects can arise from design.

This paper reframes and extends Fisher's method, showing that it is a practical alternative for political scientists. As an example, we show how to assess treatment effects using a field experiment of the effect of newspaper advertising on aggregate turnout with only eight observations. In the end, we produce confidence intervals using linear models, but requiring none of the standard assumptions of linear models to guarantee valid statistical inferences. keywords: randomization inference; analysis of experimental data; covariance adjustment; small sample statistical inference
(with Costas Panagopoulos ).

"Probability of What?": A Randomization-based Method for Hypothesis Tests and Confidence Intervals about Treatment Effects This drafty working paper provides my perspective on randomization-based inference for randomized experiments. (with Costas Panagopoulos).

Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: An Application of Randomization Inference Using Full Matching Statistical analysis requires a probability model: commonly, a model for the dependence of outcomes Y on confounders X and a potentially causal variable Z. When the goal of the analysis is to infer Z’s effects on Y, this requirement introduces an element of circularity: in order to decide how Z affects Y, the analyst first determines, speculatively, the manner of Y ’s dependence on Z and other variables. This paper takes a statistical perspective that avoids such cir- cles, permitting analysis of Z’s effects on Y even as the statistician remains entirely agnostic about the conditional distribution of Y given X and Z, or perhaps even denies that such a distribution exists. Our assumptions instead pertain to the conditional distribution Z|X, and the role of speculation in set- tling them is reduced by the existence of random assignment of Z in a field experiment as well as by poststratification, testing for overt bias before accept- ing a poststratification, and optimal full matching. Such beginnings pave the way for “randomization inference”, an approach which, despite a long history in the analysis of designed experiments, is relatively new to political science and to other fields in which experimental data are rarely available.

The approach applies to both experiments and observational studies. We illustrate this by applying it to analyze A. Gerber and D. Green’s New Haven Vote 98 campaign. Conceived as both a get-out-the-vote campaign and a field experiment in political participation, the study assigned households to treat- ment and desired to estimate the effect of treatment on the individuals nested within the households. We estimate the number of voters who would not have voted had the campaign not prompted them to — that is, the total number of votes attributable to the interventions of the campaigners — while taking into account the non-independence of observations within households, non-random compliance, and missing responses. Both our statistical inferences about these attributable effects and the stratification and matching that precede them rely on quite recent developments from statistics; our matching, in particular, has novel features of potentially wide applicability. Our broad findings resemble those of the original analysis by Gerber and Green (2000).
(with Ben Hansen ) prepared for presentation at the Political Methodology meetings, July 2005

Fixing Broken Experiments: How to Bolster the Case for Ignorability with Full Matching (with Ben Hansen).

Attributing Effects to a Get-Out-The-Vote Campaign Using Full Matching and Randomization Inference (with Ben Hansen ) prepared for presentation at the MPSA meetings, April 2005

Miscellaneous Fun Stuff

Cycling Involvements: Frequency Domain Time Series Analysis and Political Participation in the USA This paper shows that decomposing a time-series into periodic components can provide po- litically useful information about the shape of aggregate political participation in the United States. Specifically, it provides statistical tests for the periodicity of the aggregate time series of political participation and explains how this decomposition and associated tests work. Between 1973 and 1994 there appears to be an annual cycle in the reporting of political participation by respondents to a series of polls conducted by Gallup 10 times per year. This seasonality has been noted by in one other publication, by Rosenstone and Hansen (1993), but was explained as tied to a summer political cycle. In this article I suggest that this discovery has more to do with annual cycles in the composition of the Gallup sample than politics. I am currently trying to obtain detailed information on the monthly mail volume into and out of Congress. With this information, I will be able to test more directly if, despite the changes in sample composition of the Gallup polls, the political participation of Americans ought to be see as an "output" of Congressional mobilization or an "input" or in what way the flow of participation into Congress is related to the flow of mobilization out of it. Very drafty. If someone has flows of mail to and from Congress, I would love to collaborate. I think this approach would be very useful for relating such nearly continuous political "signals". This paper contains a basic description of some frequency domain time series analysis as applied to a political science topic.

Political Behavior

Can television encourage anti-violence norms in Northern Nigeria? This new collaboration with Annette Brown (of the International Initiative for Impact Evaluation) and Graham Couturier (Equal Access International) and Chris Grady (Univ of Illinois) uses a series of experiments to assess theories of social learning for norm change when stakes are high

Political Participation As a Dynamic Sporadic Process in the Lives of Ordinary Americans (now called "The Shape of Political Participation") Although much voting is habitual and education may be a "universal solvent"(324) (Converse, 1972), neither habit nor education can well predict the moments when a person decides to send a letter, join or organize a protest, or merely work together with others on some community project. Nor can they predict how long a person will spend as an active participant before redirecting her energies away from politics. Nor would they tell us much about what kinds of events or conditions might interrupt such spells of concentrated action. Are all such dynamics explained by mobilization? Many are but many are not.

This paper does not propose a unifying theory of political action. Instead, it has three simple aims: (1) to present evidence to make vivid and compelling the fact that political participation occurs as a sparse series of episodes in the lives of people in addition to a line between the ruled and the rulers; (2) propose an analytical and conceptual distinction between potentiating and precipitating factors in the etiology of political participation to help guide those who will produce a unifying theory; and (3) explore some of the implications of the descriptions presented for questions we might ask about political participation and democracy and future research on these topics.
A collaboration with Paul Test to propose a theoretical account of political action that can help us understand both cross-section and temporal variation.

The Shape of Political Participation (An exercise in description) Although 50 years of excellent scholarship have taught us a great deal about how political participation varies across individuals within one point in time, scholars do not know much about how political participation changes over time within the lives of individuals. By focusing predominantly on the preconditions for participation, the literature has largely ignored the precipitants of it. In this paper, I endeavor to show what political participation looks like if we think of it as a process evolving year-by-year across the lives of ordinary people. The new description offered here provides some evidence that challenges the basis for extant theories of why individuals participate in politics. The purpose of this paper is not to offer new theories or frameworks for understanding, but merely to offer a new vision of what political participation is; a vision which differs from and, I hope, complements that currently assumed by scholars in this field; a vision that, I hope, spurs new theories and new modes of research in this area.

A Framework for Studying the Dynamics of Political Participation. Political action is driven by events. Although the effects of particularly dramatic events on social movements is well documented, the effects of events, quotidian or exceptional, on the behavior of individuals are significantly less well understood. This paper proposes a framework for understanding how a moment of political action may occur in the life of an ordinary person. It synthesizes past literature and theories that explain variation among people at a single point in time on the basis of largely time-constant attributes of people and elaborates on this literature to suggest when we might expect the poor and disadvantaged to surmount such resource, skills, and status barriers to get involved in politics. Furthermore, this framework suggests a way for future syntheses, theory-building, and empirical studies to coordinate such that all of our disparate findings about political participation cumulate more effectively. An Appendix for this working paper.

Maps In Their Heads/Community Mapping (with Cara Wong , Daniel Rubenson , and Mark Fredrickson ) This project is currently funded by an Insight Development Grant from the SSHRC of Canada (Feb 2011 Competition).

Threat, Mobilization, and Participation: The Impact of Crossburnings on Political Behavior in North Carolina (with Mark Fredrickson )

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Dissertation

An overview and summary of my dissertation here
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Some of the material on this page is based upon work supported by the National Science Foundation under Grant Numbers SES-0753164 and SES-0753168 and previously by a National Science Foundation Graduate Research Fellowship 1994--1999. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).