High school guidance counselors can improve student outcomes
In a quasi-random design, assignment to more effective high school guidance counselors increased graduation, college enrollment, and bachelor’s degree attainment, particularly for low-achieving and low-income students.
Counselors are a common school resource for students navigating complicated and consequential education choices. I estimate counselors' causal effects using quasi-random assignment policies in Massachusetts. Counselors vary substantially in their effectiveness at increasing high school graduation and college attendance, selectivity, and persistence. Counselor effects on educational attainment are similar in magnitude to teacher effects, but they flow through improved information and assistance more than cognitive or noncognitive skill development. Counselor effectiveness is most important for low-income and low-achieving students, so improving access to effective counseling may be a promising way to increase educational attainment and close socioeconomic gaps in education.
Differential privacy with inferential validity
The authors develop a data access system that protects sensitive information through a differential privacy algorithm while also ensuring inferential validity.
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 privacy concerns. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for research subjects, and statistical validity guarantees for researchers seeking social science insights. We build on the standard of “differential privacy,” correct for biases induced by the privacy-preserving procedures, provide a proper accounting of uncertainty, and impose minimal constraints on the choice of statistical methods and quantities estimated. We illustrate by replicating key analyses from two recent published articles and show how we can obtain approximately the same substantive results while simultaneously protecting privacy. Our approach is simple to use and computationally efficient; we also offer open-source software that implements all our methods.
Developing measures of disease stigma
In a sample of 4.7 million news articles published between 1980 to 2018, word embedding measures of stigma for 106 health conditions reveal that preventable conditions and infectious diseases are associated with the highest levels of stigma.
Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the strongest connotations of immorality and negative personality traits, and infectious diseases are most marked by disgust. These results lend new empirical support to theories that norm enforcement and contagion avoidance drive disease stigma. Challenging existing theories, we find no evidence for a link between medicalization and stigma, and inconclusive evidence on the relationship between advocacy and stigma. Finally, we find that stigma has declined dramatically over time, but only for chronic physical illnesses. In the past four decades, disease stigma has transformed from a sea of negative connotations surrounding most diseases into two primary conduits of meaning: infectious diseases spark disgust, and behavioral health conditions cue negative stereotypes. These results show that cultural meanings are especially durable when they are anchored by interests, and that cultural changes intertwine in ways that only become visible through large-scale research.
Estimating causal spillover effects
A new method allows for causal estimates of treatment spillover effects in an instrumental variable design when spillovers in treatment take-up can be restricted by the analyst.
I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of the peers’ types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes. Supplementary material of this article will be available in online.
Impacts of mandatory reporting on anthropological research
New laws requiring mandatory reporting of hazing activity in fraternities constrain anthropological research on hazing.
Applying image processing to digitized newspaper images
Machine learning and image processing strategies promise to significantly expand access to the information available in the corpus of digitized newspaper images.
Diving below the surface has its challenges, however. For example, “noise effects” are especially widespread when digital images have been created from earlier microphotographic copies, as is common in historical newspaper collections. Noise effects introduce interference to the primary signals of the pages, both for human vision and computer vision and processing. Various types of noise effects (fig. 1) are common, including unevenly distributed luminosity (i.e., range effects), visible characters from the other side of the page (bleed-through), tilted document scans (skewed orientation), and markings on the newspaper that obscure text (blobs).1 There is a wide range of severity for each of these effects, and images can range from very clean to very noisy within and across datasets.
Training teachers to reduce bullying
A cluster randomized controlled trial of a teacher training program designed to increase classroom support for victims of bullying led to reduced victimization over an 18-week semester.
Peer victimization is a worldwide crisis unresolved by 50 years of research and intervention. We capitalized on recent methodological advances and integrated self-determination theory with a social–ecological perspective. We provided teachers with a professional development experience to establish a highly supportive classroom climate that enabled the emergence of pro-victim student bystanders during bullying episodes. In our longitudinal cluster randomized control trial, we randomly assigned 24 teachers (15 men, 9 women; 19 middle school, 5 high school; 32.8 years old, 6.7 years of experience) in 48 classrooms to the autonomy-supportive teaching (AST) workshop (24 classrooms) or the no-intervention control (24 classrooms). Their 1,178 students (age: M = 13.7, SD = 1.5; range = 11–18) reported their perceived teacher autonomy support; perceived classmates’ autonomy support; adoption of the defender role; and peer victimization at the beginning, middle, and end of an 18-week semester. A doubly latent multilevel structural equation model with follow-up mediation tests showed that experimental-group teachers created a substantially more supportive classroom climate, leading student bystanders to embrace the defender role. This classroom-wide (L2) emergence of pro-victim peer bystanders led to sharply reduced victimization (effect size = −.40). Unlike largely unsuccessful past interventions that focused mainly on individual students, our randomized control trial intervention substantially reduced bullying and victimization. Focusing on individual students is likely to be ineffective (even counterproductive) without first changing the normative climate that reinforces bullying. Accordingly, our intervention focused on the classroom teacher. In the classrooms of these teachers, bystanders supported the victims because the classroom climate supported the bystanders. (PsycInfo Database Record (c) 2023 APA, all rights reserved)