The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, other government programs, industry decision making, and the evidence base of many scholarly articles. Because SSA makes public little replication information and uses qualitative and antiquated statistical forecasting methods, fully independent alternative forecasts (and the ability to score policy proposals to change the system) are nonexistent. Yet, no systematic evaluation of SSA forecasts has ever been published by SSA or anyone else --- until a companion paper to this one (King, Kashin, and Soneji, 2015a). We show that SSA's forecasting errors were approximately unbiased until about 2000, but then began to grow quickly, with increasingly overconfident uncertainty intervals. Moreover, the errors are all in the same potentially dangerous direction, making the Social Security Trust Funds look healthier than they actually are. We extend and then attempt to explain these findings with evidence from a large number of interviews we conducted with participants at every level of the forecasting and policy processes. We show that SSA's forecasting procedures meet all the conditions the modern social-psychology and statistical literatures demonstrate make bias likely. When those conditions mixed with potent new political forces trying to change Social Security, SSA's actuaries hunkered down trying hard to insulate their forecasts from strong political pressures. Unfortunately, this otherwise laudable resistance to undue influence, along with their ad hoc qualitative forecasting models, led the actuaries to miss important changes in the input data. Retirees began living longer lives and drawing benefits longer than predicted by simple extrapolations. We also show that the solution to this problem involves SSA or Congress implementing in government two of the central projects of political science over the last quarter century:  promoting transparency in data and methods and  replacing with formal statistical models large numbers of qualitative decisions too complex for unaided humans to make optimally.
The vast majority of social science research presently uses small (MB or GB scale) data sets. These fixed-scale data sets are commonly downloaded to the researcher's computer where the analysis is performed locally, and are often shared and cited with well-established technologies, such as the Dataverse Project (see Dataverse.org), to support the published results. The trend towards Big Data -- including large scale streaming data -- is starting to transform research and has the potential to impact policy-making and our understanding of the social, economic, and political problems that affect human societies. However, this research poses new challenges in execution, accountability, preservation, reuse, and reproducibility. Downloading these data sets to a researcher’s computer is infeasible or not practical; hence, analyses take place in the cloud, require unusual expertise, and benefit from collaborative teamwork and novel tool development. The advantage of these data sets in how informative they are also means that they are much more likely to contain highly sensitive personally identifiable information. In this paper, we discuss solutions to these new challenges so that the social sciences can realize the potential of Big Data.
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach’s robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2014b).
To reduce model dependence and bias in causal inference, researchers usually use matching as a data preprocessing step, after which they apply whatever statistical model and uncertainty estimators they would have without matching. Unfortunately, this approach is appropriate in finite samples only under exact matching, which is usually infeasible, or approximate matching only under asymptotic theory if large enough sample sizes are available, but even then requires unfamiliar specialized point and variance estimators. Instead of attempting to change common practices, we show how those analyzing certain specific (but extremely common) types of data can instead appeal to a much easier version of existing theory. This alternative theory is substantively plausible, requires no asymptotic theory, and is simple to understand. Its core conceptualizes continuous variables as having natural breakpoints, which are common in applications (e.g., high school or college degrees in years of education, a governmental poverty level in income, or phase transitions in temperature). The theory allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments.
"Robust standard errors" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, will still bias estimators of all but a few quantities of interest. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help researchers realize these gains via a more productive way to understand and use robust standard errors; a new general and easier-to-use "generalized information matrix test" statistic that can formally assess misspecification (based on differences between robust and classical variance estimates); and practical illustrations via simulations and real examples from published research. How robust standard errors are used needs to change, but instead of jettisoning this popular tool we show how to use it to provide effective clues about model misspecification, likely biases, and a guide to considerably more reliable, and defensible, inferences. Accompanying this article [soon!] is software that implements the methods we describe.
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 identifer, 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.
MatchingFrontier is an easy-to-use R Package for making optimal causal inferences from observational data. Despite their popularity, existing matching approaches leave researchers with two fundamental tensions. First, they are designed to maximize one metric (such as propensity score or Mahalanobis distance) but are judged against another for which they were not designed (such as L1 or differences in means). Second, they lack a principled solution to revealing the implicit bias-variance trade off: matching methods need to optimize with respect to both imbalance (between the treated and control groups) and the number of observations pruned, but existing approaches optimize with respect to only one; users then either ignore the other, or tweak it, usually suboptimally, by hand.
MatchingFrontier resolves both tensions by consolidating previous techniques into a single, optimal, and flexible approach. It calculates the matching solution with maximum balance for each possible sample size (N, N-1, N-2,...). It thus directly calculates the entire balance-sample size frontier, from which the user can easily choose one, several, or all subsamples from which to conduct their final analysis, given their own choice of imbalance metric and quantity of interest. MatchingFrontier solves the joint optimization problem in one run, automatically, without manual tweaking, and without iteration. Although for each subset size k, there exist a huge (N choose k) number of unique subsets, MatchingFrontier includes specially designed fast algorithms that give the optimal answer, usually in a few minutes.
Existing research on the extensive Chinese censorship organization uses observational methods with well-known limitations. We conducted the first large-scale experimental study of censorship by creating accounts on numerous social media sites, randomly submitting different texts, and observing from a worldwide network of computers which texts were censored and which were not. We also supplemented interviews with confidential sources by creating our own social media site, contracting with Chinese firms to install the same censoring technologies as existing sites, and—with their software, documentation, and even customer support—reverse-engineering how it all works. Our results offer rigorous support for the recent hypothesis that criticisms of the state, its leaders, and their policies are published, whereas posts about real-world events with collective action potential are censored.
This is a poster that describes our analysis of "partisan taunting," the explicit, public, and negative attacks on another political party or its members, usually using vitriolic and derogatory language. We first demonstrate that most projects that hand code text in the social sciences optimize with respect to the wrong criterion, resulting in large, unnecessary biases. We show how to fix this problem and then apply it to taunting. We find empirically that, unlike most claims in the press and the literature, taunting is not inexorably increasing; it appears instead to be a rational political strategy, most often used by those least likely to win by traditional means -- ideological extremists, out-party members when the president is unpopular, and minority party members. However, although taunting appears to be individually rational, it is collectively irrational: Constituents may resonate with one cutting taunt by their Senator, but they might not approve if he or she were devoting large amounts of time to this behavior rather than say trying to solve important national problems. We hope to partially rectify this situation by posting public rankings of Senatorial taunting behavior.
This is a poster presentation describing (1) the largest ever experimental study of media effects, with more than 50 cooperating traditional media sites, normally unavailable web site analytics, the text of hundreds of thousands of news articles, and tens of millions of social media posts, and (2) a design we used in preparation that attempts to anticipate experimental outcomes
Last year was difficult for Google Flu Trends (GFT). In early 2013, Nature reported that GFT was estimating more than double the percentage of doctor visits for influenza like illness than the Centers for Disease Control and Prevention s (CDC) sentinel reports during the 2012 2013 flu season (1). Given that GFT was designed to forecast upcoming CDC reports, this was a problematic finding. In March 2014, our report in Science found that the overestimation problem in GFT was also present in the 2011 2012 flu season (2). The report also found strong evidence of autocorrelation and seasonality in the GFT errors, and presented evidence that the issues were likely, at least in part, due to modifications made by Google s search algorithm and the decision by GFT engineers not to use previous CDC reports or seasonality estimates in their models what the article labeled algorithm dynamics and big data hubris respectively. Moreover, the report and the supporting online materials detailed how difficult/impossible it is to replicate the GFT results, undermining independent efforts to explore the source of GFT errors and formulate improvements.
Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.
In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3, 4), what lessons can we draw from this error?
The social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams; and from a purely academic pursuit to having a major impact on the world. To facilitate these important developments, universities, funding agencies, and governments need to shore up and adapt the infrastructure that supports social science research. We discuss some of these developments here, as well as a new type of organization we created at Harvard to help encourage them -- the Institute for Quantitative Social Science. An increasing number of universities are beginning efforts to respond with similar institutions. This paper provides some suggestions for how individual universities might respond and how we might work together to advance social science more generally.
A method for selecting clusterings to classify a predetermined data set of numerical data comprises five steps. First, a plurality of known clustering methods are applied, one at a time, to the data set to generate clusterings for each method. Second, a metric space of clusterings is generated using a metric that measures the similarity between two clusterings. Third, the metric space is projected to a lower dimensional representation useful for visualization. Fourth, a “local cluster ensemble” method generates a clustering for each point in the lower dimensional space. Fifth, an animated visualization method uses the output of the local cluster ensemble method to display the lower dimensional space and to allow a user to move around and explore the space of clustering.
The American system of higher education is under attack by political, economic, and educational forces that threaten to undermine its business model, governmental support, and operating mission. The potential changes are considerably more dramatic and disruptive than what we've already experienced. Traditional colleges and universities urgently need a coherent, thought-out response. Their central role in ensuring the creation, preservation, and distribution of knowledge may be at risk and, as a consequence, so too may be the spectacular progress across fields we have come to expect as a result.
Symposium contributors include Henry E. Brady, John Mark Hansen, Gary King, Nannerl O. Keohane, Michael Laver, Virginia Sapiro, and Maya Sen.
We marshal discoveries about human behavior and learning from social science research and show how they can be used to improve teaching and learning. The discoveries are easily stated as three social science generalizations: (1) social connections motivate, (2) teaching teaches the teacher, and (3) instant feedback improves learning. We show how to apply these generalizations via innovations in modern information technology inside, outside, and across university classrooms. We also give concrete examples of these ideas from innovations we have experimented with in our own teaching.
We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services all over China before the Chinese government is able to find, evaluate, and censor (i.e., remove from the Internet) the large subset they deem objectionable. Using modern computer-assisted text analytic methods that we adapt to and validate in the Chinese language, we compare the substantive content of posts censored to those not censored over time in each of 85 topic areas. Contrary to previous understandings, posts with negative, even vitriolic, criticism of the state, its leaders, and its policies are not more likely to be censored. Instead, we show that the censorship program is aimed at curtailing collective action by silencing comments that represent, reinforce, or spur social mobilization, regardless of content. Censorship is oriented toward attempting to forestall collective activities that are occurring now or may occur in the future --- and, as such, seem to clearly expose government intent.
Guido Imbens, Donald B Rubin, Gary King, Richard A Berk, Daniel E Ho, Kevin M Quinn, James D Greiner, Ian Ayres, Richard Brooks, Paul Oyer, and Richard Lempert. 2012. “Brief of Empirical Scholars as Amici Curiae.” Filed with the Supreme Court of the United States in Abigail Noel Fisher v. University of Texas at Austin, et al. Abstract
In Grutter v. Bollinger,
this Court held that a state has a compelling interest in attaining a diverse student body for the benefit of all students, and thatthis compelling interest justifies the consideration of race as a factor in university admissions. See 539 U.S. 306, 325, 328 (2003). In this, the latest case to consider the constitutionality of affirmative-action admissions policies, Professor Richard H. Sander, along with lawyer and journalist Stuart S. Taylor, Jr., filed a brief amici curiae arguing that social-8science research has shown affirmative action to be harmful to minority students. See Brief Amici Curiae for Richard Sander and Stuart Taylor, Jr. in Supportof Neither Party (“Sander-Taylor Brief”) 2. According to them, a “growing volume of very careful research, some of it completely unrebutted by dissenting work” has found that affirmative-action practices are not having their intended effect. Id.; see also Brief Amici Curiae of Gail Heriot et al. in Support of Petitioner (“Three Commissioners Brief”) 14 (“The Commissioner Amici are aware of no empirical research that challenges [Sander’s] findings.”).
But, as amici will show, the principal research on which Sander and Taylor rely for their conclusion about the negative effects of affirmative action—Sander’s so-called “mismatch” hypothesis2—is far from “unrebutted.” Sander-Taylor Brief 2. Since Sander first published findings in support of a“mismatch” in 2004, that research has been subjected to wide-ranging criticism. Nor is Sander’s research “very careful.” Id. As some of those critiques discussin detail, Sander’s research has major methodologicalflaws—misapplying basic principles of causal inference—that call into doubt his controversial conclusions about affirmative action. The Sander “mismatch” research—and its provocative claim that, on average, minority students admitted through affirmative action would be better off attending less selective colleges and universities—is not good social science.
Sander’s research has “significantly overestimated the costs of affirmative action and failed to demonstrate benefits from ending it.” David L. Chambers et al., The Real Impact of Affirmative Action in American Law Schools: An Empirical Critique of Richard Sander’s Study, 57 Stan. L. Rev. 1855, 1857 (2005). That research, which consists of weak empirical contentions that fail to meet the basic tenets of rigorous social-science research, provides no basis for this Court to revisit longstanding precedent supporting the individualized consideration of race in admissions. Cf. Grutter, 539 U.S. at 334 (“Universities can * * * consider race or ethnicity more flexibly as a ‘plus’ factor in the context of individualized consideration of each and every applicant.”) (citing Regents of Univ. of Cal. v. Bakke, 438 U.S. 265, 315-316 (1978) (opinion of Powell, J.,)).In light of the significant methodological flaws on which it rests, Sander’s research does not constitute credible evidence that affirmative action practices are harmful to minorities, let alone that the diversity rationale at the heart of Grutter is at odds with social science.
A method of computerized content analysis that gives “approximately unbiased and statistically consistent estimates” of a distribution of elements of structured, unstructured, and partially structured source data among a set of categories. In one embodiment, this is done by analyzing a distribution of small set of individually-classified elements in a plurality of categories and then using the information determined from the analysis to extrapolate a distribution in a larger population set. This extrapolation is performed without constraining the distribution of the unlabeled elements to be equal to the distribution of labeled elements, nor constraining a content distribution of content of elements in the labeled set (e.g., a distribution of words used by elements in the labeled set) to be equal to a content distribution of elements in the unlabeled set. Not being constrained in these ways allows the estimation techniques described herein to provide distinct advantages over conventional aggregation techniques.