Journal Articles, Tech Reports and Book Chapters
Challenges to Replication and Iteration in Field Experiments: Evidence from two Direct Mail Shots. (with Nathaniel Higgins, Dean Karlan, Sarah Tulman, and Jonathan Zinman) American Economic Review (Papers \& Proceedings) , 2017. 107(5):1--3.
How to improve your relationship with your future self (V 2.0) (with Maarten Voors) Revista de Ciencia Política 2016 36:3 p 829--848. Github
Research Note: A More Powerful Test Statistic for Reasoning about Interference between Units (with Peter Aronow and Mark Fredrickson). Political Analysis doi:10.1093/pan/mpw018 published online July 2016. This paper follows up on the paper on statistical inference for experiments on social networks discussed on the OUP blog .
Comment: Method Games---A Proposal for Assessing and Learning about Methods A plea and a plan to enable comparisons of methods for pattern detection including QCA and other machine learning algorithms Sociological Methodology published online 28 July 2014 DOI: 10.1177/0081175014542078 . This paper suggests two ways for proponents of different methods to communicate with each other using qualitative comparative analysis (QCA) and machine learning (ex. the adapative lasso) as examples.
6+ Things you need to know about cluster randomization (with Ashlea Rundlett) This is an EGAP methodology guide.
Reasoning about Interference Between Units: A General Framework If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize let alone execute. In this paper, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the ``no interference'' assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena. (with Mark Fredrickson and Costas Panagopoulos ). Political Analysis 2013 21:97--124. The reproduction archive for the paper has been published in my dataverse ( or feel free to use a direct link to the study). A direct link to the pdf is here This paper won the Miller Prize for best work appearing in Political Analysis in 2013.
Bringing the Person Back In: Boundaries, Perceptions, and the Measurement of Racial Context Place is sometimes vague or undefined in studies of context, and scholars use a range of Census units to measure ``context.'' In this paper, we borrow from Parsons and Shils to offer a conceptualization of context. This conceptualization, and a recognition of both Lippmann's pseudoenvironments and the statistical Modifiable Areal Unit Problem, lead us to a new measurement strategy. We propose a map-based measure to capture how ordinary people use information about their environments to make decisions about politics. Respondents draw their contexts on maps --- deciding the boundaries of their relevant environments --- and describe their perceptions of the demographic make-up of these contexts. The evidence is clear: ``pictures in our heads'' do not resemble governmental administrative units in shape or content. By ``bringing the person back in'' to the measurement of context, we are able to marry psychological theories of information processing with sociological theories of racial threat. (with Cara Wong, Tarah Williams, and Katherine Drake) The Journal of Politics 2012. Vol 74, No. 4.
Making Effects Manifest in
Randomized Experiments Experimentalists desire precise
estimates of treatment effects and nearly always care about
how treatment effects may differ across subgroups. After data
collection, concern may focus on random imbalance between
treatment groups on substantively important variables. Pursuit
of these three goals --- enhanced precision, understanding
treatment effect heterogeneity, and imbalance adjustment ---
requires background information about experimental units. For
example, one may group similar observations on the basis of
such variables and then assign treatment within those blocks.
Use of covariates after data have been collected raises extra
concerns and requires special justification. For example
standard regression tables only approximate the statistical
inference that experimentalists desire. The standard linear
model may also mislead via extrapolation. After providing some
general background about how covariates may, in principle,
enable pursuit of precision and statistical adjustment, this
paper presents two alternative approaches to covariance
adjustment: one using modern matching techniques and another
using the linear model --- both use randomization as the basis
for statistical inference. in the
Cambridge Handbook of Experimental Political Science eds. James
N. Druckman, Donald P. Green, James H. Kuklinski, and Arthur Lupia. Cambridge
Univ Press. 2011.
The pdf file available here is the paper before copy-editing.
The compendium, or reproduction archive, for the paper is available as a package for the R statistical computing environment. See this little tutorial file for detailed instructions about how to download and use it.
Six steps to a better relationship with your future self. The Political Methodologist. 2011. Vol 18, No. 2. The original Sweave file used to produce this article can be accessed here
Attributing Effects to a Cluster Randomized Get-Out-The-Vote Campaign (with Ben Hansen ) Journal of the American Statistical Association 2009.
The compendium, or reproduction archive, for the paper has been published in my dataverse ( or feel free to use a direct link to the study) The working paper version of this article won the Franklin L. Burdette/Pi Sigma Alpha award for best paper presentat at APSA 2010. A policy brief about this paper can be found at the EGAP website .
(For more about reproduction archives in general see Gentleman, R and D. Lang. 2004. "Statistical Analyses and Reproducible Research." )
Covariate Balance in Simple, Stratified and Clustered Comparative Studies (with Ben Hansen ) Statistical Science 2008, Vol. 23, No. 2, 219-236. A typo on page 4 exists such that the variance of d should be Var(d) = (m/(mtmc))s2.
Politics across Generations: Family Transmission Reexamined (with Kent Jennings and Laura Stoker). The Journal of Politics 2009, Vol 71, No. 3, 782-799.
EDA for HLM: Visualization when Probabilistic Inference Fails (with Katherine Drake). Political Analysis. 2005, 13 (4):301-326. The Sweave source file and datasets used to produce this article have been published in my dataverse ( or feel free to use a direct link to the study)
"Using R to Keep it Simple: Exploring Structure in Multilevel Datasets" Fall 2004. The Political Methodologist. The Sweave file and datasets used to produce this article can be downloaded as a zipped archive here
Analyzing the 2000 National Election Study (with Nancy Burns, Michael Ensley, and Don Kinder). 2005. Political Analysis . 13(1):109-111.
"Does Moving Disrupt Campaign Activity?" August 2004. Political Psychology. 21(4):525-543.
"Designing Multi-level Studies: Sampling Voters and Electoral Contexts" (with Laura Stoker). 2002. Electoral Studies 21(2):235-267. This file includes the correct Figure 3 (published as an Erratum in the next issue of Electoral Studies).
(Figure 1 in color -- PDF, about .3MB)
Supplementary results and simulation programs are available on this page .
Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign. (with Ben Hansen ). University of Michigan, Dept of Statistics Technical Report # 448. October 2006. (pdf version here)
"Issues in Analyzing Data from the Dual-Mode 2000 American National Election Study" (with Michael Ensley). NES Technical Report #64. (April 2003).
"Black Threat and Christian Fundamentalist Threat: A National Election Study 1997 Pilot Study Report" 1997 NES Pilot Study Report
NES Pilot Study Efforts to Measure Values and Predispositions. NES Technical Report #18. (February 1995)