# Social Science Statistics Blog

October 2011
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Matt Blackwell (Gov)

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Martin Andersen (HealthPol)
Kevin Bartz (Stats)
Deirdre Bloome (Social Policy)
John Graves (HealthPol)
Rich Nielsen (Gov)
Maya Sen (Gov)
Gary King (Gov)

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### Recent Entries

- App Stats: Brown and Marks on "Empirical Social Science at Disney Research"
- App Stats: Nielsen on "Comparative Effectiveness of Matching Methods for Causal Inference"
- App Stats: Weihua An on "Peer Effects on Adolescent Smoking and Social Network-Based Interventions"
- App Stats: Weissman on "From Fourier to Forensics"
- App Stats: Liublinska on "Addressing missing data issues in a study with rare binary outcomes constrained by a small sample size"

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« September 2011 | Main | November 2011 »

31 October 2011

### App Stats: Brown and Marks on "Empirical Social Science at Disney Research"

We hope you can join us this Wednesday, November 2, 2011 for the Applied Statistics Workshop. Amber Brown, Senior Research Scientist at Disney Research, and Joe Marks, Vice President and Fellow of Disney Research, will give a talk entitled "Empirical Social Science at Disney Research". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Empirical Social Science at Disney Research"
Amber Brown and Joe Marks
Disney Research, Boston
CGIS K354 (1737 Cambridge St.)
Wednesday, November 2nd, 2011 12.00 pm

Abstract:

> At Disney Research we mostly work on technologies that are relevant to our various businesses: computer graphics, computer vision, robotics, human-computer interaction, materials, displays, etc. But we also have projects in the social sciences, with a heavy emphasis on rigorous empirical testing. We will describe four recent projects:
> - Novel pay-what-you-want pricing mechanisms.
> - Load balancing of park guests via pushed incentives on mobile devices.
> - Guest participation in environmental programs.
> - Introduction of a cinema culture to the developing world.

Posted by Konstantin Kashin at 1:08 AM

23 October 2011

### App Stats: Nielsen on "Comparative Effectiveness of Matching Methods for Causal Inference"

We hope you can join us this Wednesday, October 26, 2011 for the Applied Statistics Workshop. Rich Nielsen, a Ph.D. candidate from the Department of Government at Harvard University, will present a paper entitled "Comparative Effectiveness of Matching Methods for Causal Inference". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Comparative Effectiveness of Matching Methods for Causal Inference"
Rich Nielsen
Government Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 26th, 2011 12.00 pm

Abstract:

> Matching is an increasingly popular method of causal inference in observational data, but following methodological best practices has proven difficult for applied researchers. We address this problem by providing a simple graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable "Mahalanobis Distance Matching" (MDM), the commonly used "Propensity Score Matching" (PSM), and a newer approach called "Coarsened Exact Matching" (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in many real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can often degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach and easy-to-follow procedures, focus can be on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it.

The paper is joint work with Gary King, Carter Coberley, James E. Pope, and Aaron Wells.

Posted by Konstantin Kashin at 10:54 PM

17 October 2011

### App Stats: Weihua An on "Peer Effects on Adolescent Smoking and Social Network-Based Interventions"

We hope you can join us this Wednesday, October 19, 2011 for the Applied Statistics Workshop. Weihua An, a Lecturer in the Department of Sociology at Harvard University, will present his dissertation entitled "Peer Effects on Adolescent Smoking and Social Network-Based Interventions". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Peer Effects on Adolescent Smoking and Social Network-Based Interventions"
Weihua An
Sociology Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 19th, 2011 12.00 pm

Abstract:

> This study addresses a fundamental question in social network analysis: whether and to what extent peers affect a person's wellbeing. More specifically, it attempts to identify and quantify peer effects on smoking among adolescents.

> Based on the causal inference terminology, a systematic framework to study causal peer effects was developed to distinguish several types of peer effects, including peer effects under control, peer effects under treatment, etc. To overcome the difficulties in identifying peer effects with observational data, a novel field experiment was conducted with a partial treatment group design specifically tuned to estimate peer effects.

> More specifically, a smoking prevention intervention composed of distributing smoking prevention brochures and hosting health education workshops was assigned to partial randomly chosen members in a number of classes in six middle schools in China where the experiment was fielded. The goal was to study how the information contained in the intervention was spread across students and how it affected their information, knowledge, intention, and behavior regarding smoking. To accelerate or reinforce the diffusion, central students or students with their close friends as identified based on their social network information were also chosen respectively to receive the intervention in different treated classes.

> Descriptive analysis provided strong support for peer effects on the initiation and maintenance of adolescent smoking. Further statistical analysis showed that compared with students in the control classes, students whose classmates were randomly chosen to receive the intervention but who did not receive the intervention themselves were more likely to exchange information about the intervention with other students and to remain non- smokers or change to non-smokers overtime. It was also found that the social network- based interventions did not consistently bring significant added value in all the outcomes of interest and their benefits mainly concentrated on lowering students' intention to smoke and decreasing smokers' popularity.

> Special attention will be paid in the presentation to elaborating how to choose central students and student groups in a social network.

Posted by Konstantin Kashin at 12:06 AM

9 October 2011

### App Stats: Weissman on "From Fourier to Forensics"

We hope you can join us this Wednesday, October 12, 2011 for the Applied Statistics Workshop. Michael Weissman, a Professor Emeritus from the Physics Department at the University of Illinois, will give a presentation entitled "From Fourier to Forensics". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"From Fourier to Forensics"
Michael Weissman
Government Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 12th, 2011 12.00 pm

Abstract:

> Although the statistical and systematic problems of public opinion polls are fairly widely recognized, we tend to assume that published polling results reflect some sort of actual poll. In 2009 a prominent blog suggested that the pollster Strategic Vision might be fabricating data, based in part on surprising deviations from uniformity of the distribution of trailing digits of the results.(http://www.fivethirtyeight.com/search/label/strategic%20vision) Objections were raised to the assumed uniform distribution, but we were able to use Fourier analysis together with known polling statistics to show that the results were weird even if that assumption were dropped. http://query.nytimes.com/gst/fullpage.html?res=9C03E1DA123AF930A25751C1A96F9C8B63

> In 2010 we were contacted by a political consultant who had noticed anomalies in Research2000 poll reports. Using a variety of elementary statistical techniques, we showed that those results could not have accurately represented real polls. http://en.wikipedia.org/wiki/Research_2000 Unfortunately, we do not know if there are other bogus pollsters, disguising results via a random binary generator (cost $0.01).

Posted by Konstantin Kashin at 10:20 PM

2 October 2011

### App Stats: Liublinska on "Addressing missing data issues in a study with rare binary outcomes constrained by a small sample size"

We hope you can join us this Wednesday, October 5, 2011 for the Applied Statistics Workshop. Victoria Liublinska, a Ph.D. candidate from the Statistics Department at Harvard University, will present a paper entitled "Addressing missing data issues in a study with rare binary outcomes constrained by a small sample size". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Addressing missing data issues in a study with rare binary outcomes constrained by a small sample size"
Victoria Liublinska (with D. Rubin and R. Gutman)
Statistics Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 5th, 2011 12.00 pm

Abstract:

> We (re)analyze the data obtained in a recent study conducted to evaluate safety and efficacy of a new device designed for vertebroplasty. The following are just a few issues that had to be addressed: missing data in some covariates, incorrect analysis applied initially to the primary endpoint, missing data in secondary endpoints. The latter involved additional challenges such as panel data (responses were collected twice over time with a non monotone missingness pattern), secondary endpoints were rare binary events. The analysis was complicated by a relatively small sample size. Our work demonstrates how a complex missing data issue can be broken down into a set of small tasks that are solved individually. Some tasks involved multivariate missing data imputation using chained equations (van Buuren and Oudshoorn 2000; Raghunathan et al. 2001) with carefully chosen conditional models. Other tasks called for new state-of-the-art solutions, such as z-transformation procedure for combining repeated p-values (D. Rubin et al. 2011 (to be submitted), C. Licht 2009 Ph.D. thesis) or enhanced tipping-point graphs that assess sensitivity to various deviations from assumptions made about the missing data mechanism (Yan et al. 2009, Campbell et al. 2011).

Posted by Konstantin Kashin at 10:05 PM