Highlights of 2011 - Institute for Policy Research - Northwestern ...

Quantitative Methods for Policy Research
Experiments in Political Science
Druckman is co-editor of the first comprehensive overview
of how experimental research is transforming political science.
Published by Cambridge University Press, the Cambridge Handbook
of Experimental Political Science (2011) offers methodological
insights and groundbreaking research from 30 of the discipline’s
leading experimentalists, including Druckman, Shanto
Iyengar and Paul Sniderman of Stanford, Alan Gerber of Yale,
Donald Green of Columbia, and Diana Mutz of the University
of Pennsylvania. The handbook aims to ensure that political
science experiments are conducted with the highest level of
intellectual rigor, thereby enabling political scientists to provide
policymakers with significant data and conclusions. The volume
came together after a May 2009 conference at Northwestern
University and also features contributions from IPR associates
Daniel Diermeier and Dennis Chong (see p. 61).
Framing and Obesity-Related Behaviors
A variety of persuasive communications and interventions
have been explored as possible means to prevent or reduce
obesity. One persuasive message variation that has been
of interest to researchers in this domain is the contrast
between gain-framed and loss-framed appeals. A gain-framed
appeal emphasizes the advantages of compliance with the
advocated action, whereas a loss-framed appeal emphasizes
the disadvantages of noncompliance. Work by communication
studies researcher and IPR associate Daniel O’Keefe and his
colleagues provides a meta-analytic review of the accumulated
experimental research concerning the relative persuasiveness
of gain-framed and loss-framed appeals for influencing various
obesity-related behaviors. The results showed that gain-framed
appeals were significantly more persuasive than their lossframed
counterparts for messages encouraging physical activity.
But the researchers found no evidence that either gain- or lossframed
appeals held any persuasive advantage in influencing
healthy eating behaviors. They advised designers of messages
aimed at specifically obesity-relevant eating practices not to
spend much time worrying about whether those messages are
gain- or loss-framed.
Statistical Theories for Census Data
A new project led by IPR statistician Bruce Spencer with IPR
economist Charles F. Manski will address fundamental problems
for government statistical agencies: how to understand the value
of the statistics they produce, how to compare value to cost to
guide rational setting of statistical priorities, and how to increase
value for a given cost. Because data use is so complicated and
difficult to study, Spencer argues that new theory is needed so
that case studies for use of data in policymaking and research in
the social, behavioral, and economic sciences can be developed,
analyzed, and interpreted. The practical implications of research
findings are important for statistical agencies, both in the long
term and the short term, to understand and communicate
the value of data programs the agencies might seek to carry
out. Supported by a grant from the U.S. Census Bureau, the
researchers propose to extend and apply statistical decision
theory, including cost-benefit analysis, to attack such basic
questions of the statistical agencies. The research will focus
on data use, data quality, data cost, and optimization, and the
findings will be applied to problems of the U.S. Census Bureau
with the goal of carrying out a cost-benefit analysis of the 2020
census, which is facing severe cost constraints.
Sampling Theory and Methodology for
Networks
Government data collections are tempting targets for budget
cutters—not because the budgets are so large, but because
ignorance about data use makes the effects of data reductions
hard to see. There is a reason that so little is known about data
use, however. Inferring the impacts of data use is a problem of
assessing the causal effect of an intervention—people either
observe what happened when the data program was conducted,
or what happened when it was not conducted, but not both.
Spencer is currently reviewing the state of knowledge about
whether and how data are used, including both theoretical and
empirical research. His work pays particular attention to the
effects of data quality.
Human Capital Forecasting
IPR economist Charles F. Manski (left) clarifies his use of partial
identification analysis in a study of deterrence and the death
penalty with IPR statistician Bruce Spencer.
Spencer is working on estimates and forecasts for selected
areas of human capital, such as those that categorize U.S.
workers employed in science and technology jobs according
to skill. Past studies of U.S. educational attainment have
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