Universities require faculty and students planning research involving human subjects to pass formal certification tests and then submit research plans for prior approval. Those who diligently take the tests may better understand certain important legal requirements but, at the same time, are often misled into thinking they can apply these rules to their own work which, in fact, they are not permitted to do. They will also be missing many other legal requirements not mentioned in their training but which govern their behaviors. Finally, the training leaves them likely to completely misunderstand the essentially political situation they find themselves in. The resulting risks to their universities, collaborators, and careers may be catastrophic, in addition to contributing to the more common ordinary frustrations of researchers with the system. To avoid these problems, faculty and students conducting research about and for the public need to understand that they are public figures, to whom different rules apply, ones that political scientists have long studied. University administrators (and faculty in their part-time roles as administrators) need to reorient their perspectives as well. University research compliance bureaucracies have grown, in well-meaning but sometimes unproductive ways that are not required by federal laws or guidelines. We offer advice to faculty and students for how to deal with the system as it exists now, and suggestions for changes in university research compliance bureaucracies, that should benefit faculty, students, staff, university budgets, and our research subjects.
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy.
In a major development in data sharing, data providers are beginning to supplement insecure privacy protection strategies, such as "de-identification," with a formal approach called "differential privacy". One version of differential privacy adds specially calibrated random noise to a dataset, which is then released to researchers. This offers mathematical guarantees for the privacy of research subjects while still making it possible to learn about aggregate patterns of interest. Unfortunately, adding random noise creates measurement error, which induces statistical bias -- including attenuation, exaggeration, switched signs, or incorrect uncertainty estimates. We offer an easy-to-use, computationally efficient approach that corrects for these biases, can be used as researchers would use linear regression, and gives statistically consistent and approximately unbiased estimates and standard errors. We use as our running example the Full URLs Dataset recently released by Social Science One and Facebook, containing more than 10 trillion cell values.
Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data, statistical guarantees for researchers seeking population-level insights from it, and protection for society from some fallacious scientific conclusions. We build on the standard of "differential privacy" but, unlike most such approaches, we also correct for the serious statistical biases induced by privacy-preserving procedures, provide a proper accounting for statistical uncertainty, and impose minimal constraints on the choice of data analytic methods and types of quantities estimated. Our algorithm is easy to implement, simple to use, and computationally efficient; we also offer open source software to illustrate all our methods.
Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong, but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one's own hypotheses. Since the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We derive "hacking intervals," which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval, and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals.
Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present booc.io, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, booc.io is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into booc.io, and perform introductory qualitative evaluation with students.
A vast literature demonstrates that voters around the world who benefit from their governments' discretionary spending cast more ballots for the incumbent party than those who do not benefit. But contrary to most theories of political accountability, some suggest that voters also reward incumbent parties for implementing "programmatic" spending legislation, over which incumbents have no discretion, and even when passed with support from all major parties. Why voters would attribute responsibility when none exists is unclear, as is why minority party legislators would approve of legislation that would cost them votes. We study the electoral effects of two large prominent programmatic policies that fit the ideal type especially well, with unusually large scale experiments that bring more evidence to bear on this question than has previously been possible. For the first policy, we design and implement ourselves one of the largest randomized social experiments ever. For the second policy, we reanalyze studies that used a large scale randomized experiment and a natural experiment to study the same question but came to opposite conclusions. Using corrected data and improved statistical methods, we show that the evidence from all analyses of both policies is consistent: programmatic policies have no effect on voter support for incumbents. We conclude by discussing how the many other studies in the literature may be interpreted in light of our results.
The origin, meaning, estimation, and application of the concept of partisan symmetry in legislative redistricting, and the justiciability of partisan gerrymandering. An edited transcript of a talk at the “Redistricting and Representation Forum,” American Academy of Arts & Sciences, Cambridge, MA 11/8/2017.
To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where the standard paired comparisons approach fails). We create a statistical model that predicts, with high accuracy, solely from the geometric features of the district, compactness evaluations by judges and public officials responsible for redistricting, among others. We also offer compactness data from our validated measure for 20,160 state legislative and congressional districts, as well as open source software to compute this measure from any district.
Winner of the 2018 Robert H Durr Award from the MPSA.
Some scholars build models to classify documents into chosen categories. Others, especially social scientists who tend to focus on population characteristics, instead usually estimate the proportion of documents in each category -- using either parametric "classify-and-count" methods or "direct" nonparametric estimation of proportions without individual classification. Unfortunately, classify-and-count methods can be highly model dependent or generate more bias in the proportions even as the percent of documents correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning of language changes between training and test sets or is too similar across categories. We develop an improved direct estimation approach without these issues by including and optimizing continuous text features, along with a form of matching adapted from the causal inference literature. Our approach substantially improves performance in a diverse collection of 73 data sets. We also offer easy-to-use software software that implements all ideas discussed herein.
The political science math prefresher arose a quarter century ago and has now spread to many of our discipline’s Ph.D. programs. Incoming students arrive for graduate school a few weeks early for ungraded instruction in math, statistics, and computer science as they are useful for political science. The prefresher’s benefits, however, go beyond the technical material taught: it develops lasting camaraderie with their entering class, facilitates connections with senior graduate students, opens pathways to mastering methods necessary for research, and eases the transition to the increasingly collaborative nature of graduate work. The prefresher also shows how faculty across a highly diverse discipline can work together to train the next generation. We review this program, highlight its collaborative aspects, and try to take the idea to the next level by building infrastructure to share teaching materials across universities so separate programs can build on each other’s work and improve all our programs.
Congratulations! You’ve made it to graduate school. This means you’re in a select group, about to embark on a great adventure to learn about the world and teach us all some new things. This also means you obviously know how to follow rules. So I have five for you -- not counting the obvious one that to learn new things you’ll need to break some rules. After all, to be a successful academic, you’ll need to cut a new path, and so if you do exactly what your advisors and I did, you won’t get anywhere near as far since we already did it. So here are some rules, but break some of them, perhaps including this one
We clarify the theoretical foundations of partisan fairness standards for district-based democratic electoral systems, including essential assumptions and definitions that have not been recognized, formalized, or in some cases even discussed. We also offer extensive empirical evidence for assumptions with observable implications. Throughout, we follow a fundamental principle of statistical inference too often ignored in this literature -- defining the quantity of interest separately so its measures can be proven wrong, evaluated, or improved. This enables us to prove which of the many newly proposed fairness measures are statistically appropriate and which are biased, limited, or not measures of the theoretical quantity they seek to estimate at all. Because real world redistricting and gerrymandering involves complicated politics with numerous participants and conflicting goals, measures biased for partisan fairness sometimes still provide useful descriptions of other aspects of electoral systems.
In various embodiments, subject matter for improving discussions in connection with an educational resource is identified and summarized by analyzing annotations made by students assigned to a discussion group to identify high-quality annotations likely to generate responses and stimulate discussion threads, identifying clusters of high quality annotations relating to the same portion or related portions of the educational resource , extracting and summarizing text from the annotations, and combining , in an electronically represented document, the extracted and summarized text and (i) at least some of the annotations and the portion or portions of the educational resource or (ii) click able links thereto.
Textual responses to open-ended (i.e., free-response) items provided by participants (e.g., by means of mobile wireless devices) are automatically classified, enabling an instructor to assess the responses in a convenient, organized fashion and adjust instruction accordingly.
In various embodiments, documents are searched and retrieved via receipt of a search query, electronically identifying a reference set of relevant documents, providing a search set of documents, creating a database comprising at least some of the documents of the search set and the reference set , computationally classifying the documents in the database , extracting keywords from the search set and one or more classified sets , optionally filtering the extracted keywords, and electronically identifying at least some of the documents from the database that contain one or more of the extracted keywords.
Representative embodiments of a method for grouping participants in an activity include the steps of: (i) defining a grouping policy; (ii) storing, in a database, participant records that include a participant identifier, a characteristic associated with the participant, and/or an identifier for a participant's handheld device; (iii) defining groupings based on the policy and characteristics of the participants relating to the policy and to the activity; and (iv) communicating the groupings to the handheld devices to establish the groups.
In various embodiments, online discussions in connection with an eductional resource are improved by analyzing annotations made by students assigned to a discussion group to identify high-quality annotations likely to generate responses and stimulate discussion threads and by making the identified annotations visibile to students not assigned to the discussion group.
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for ``linear contextual effects,'' which previous works have regarded as plausible but avoided due to non-identification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan-Davis bound, derived more than sixty-five years ago. To study the effectiveness of our approach, we collect and analyze 8,430 2x2 EI datasets with known ground truth from several sources --- thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan-Davis bound, on average, by about 44%, while still capturing the true district level parameter about 99% of the time. The remaining 12% revert to the Duncan-Davis bound.
Easy-to-use software is available that implements all the methods described in the paper.
The mission of the social sciences is to understand and ameliorate society’s greatest challenges. The data held by private companies, collected for different purposes, hold vast potential to further this mission. Yet, because of consumer privacy, trade secrets, proprietary content, and political sensitivities, these datasets are often inaccessible to scholars. We propose a novel organizational model to address these problems. We also report on the first partnership under this model, to study the incendiary issues surrounding the impact of social media on elections and democracy: Facebook provides (privacy-preserving) data access; eight ideologically and substantively diverse charitable foundations provide funding; an organization of academics we created, Social Science One (see SocialScience.One), leads the project; and the Institute for Quantitative Social Science at Harvard and the Social Science Research Council provide logistical help.