Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge

Reframing data ethics in research methods education: a pathway to critical data literacy

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities.

Reproducibility in the Social Sciences

Concern over social scientists' inability to reproduce empirical research has spawned a vast and rapidly growing literature. The size and growth of this literature make it difficult for newly interested academics to come up to speed. Here, we provide a formal text modeling approach to characterize the entirety of the field, which allows us to summarize the breadth of this literature and identify core themes. We construct and analyze text networks built from 1,947 articles to reveal differences across social science disciplines within the body of reproducibility publications and to discuss the diversity of subtopics addressed in the literature. This field-wide view suggests that reproducibility is a heterogeneous problem with multiple sources for errors and strategies for solutions, a finding that is somewhat at odds with calls for largely passive remedies reliant on open science. We propose an alternative rigor and reproducibility model that takes an active approach to rigor prior to publication, which may overcome some of the shortfalls of the postpublication model.

Longitudinal Associations Among Low-Income Mothers' and Fathers' Parenting and Relationships with Children and Adolescent Depression

Few studies have examined the associations among quality of parent-child relationships, parenting, and adolescent depression in samples of low-income mothers and fathers with large numbers of nonresidential fathers. This study used the Fragile Families and Child Wellbeing data (N = 3,384) to examine the longitudinal associations among parent-child closeness, harsh parenting, and neglect when children were nine years old and adolescent depression at age 15. The findings indicated that children who perceived having close relationships with mothers and fathers at age nine reported significantly less depression at age 15, regardless of residential status. Moderation analyses showed a stronger negative relationship between daughters' closeness with mothers and depression than sons' closeness with mothers and depression. Daughters and sons who were close to fathers reported less depression. There were no significant associations among harsh discipline or neglect and depression. The findings support the use of interventions that promote healthy attachments and close parent-child relationships between low-income parents and children.

Consensus-based guidance for conducting and reporting multi-analyst studies

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

Sharing Practices for Datasets Related to Accessibility and Aging

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.

Ifenthaler & Schumacher (2016)

Privacy and confidentiality are core considerations in education, while at the same time, using and sharing data-and, more broadly, open science-is increasingly valued by editors, funding agencies, and the public. This manuscript responds to an empirical investigation of students' perceptions of the use of their data in learning analytics systems by Ifentahler and Schumacher (Educational Technology Research and Development, 64: 923-938, 2016). We summarize their work in the context of the COVID-19 pandemic and the resulting shift to digital modes of teaching and learning by many teachers, using the tension between privacy and open science to frame our response. We offer informed recommendations for educational technology researchers in light of Ifentahler and Schumacher's findings as well as strategies for navigating the tension between these important values. We conclude with a call for educational technology scholars to meet the challenge of studying learning (and disruptions to learning) in light of COVID-19 while protecting the privacy of students in ways that go beyond what Institutional Review Boards consider to be within their purview.

Computational Social Science and Sociology

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Associations between childhood exposure to community violence, child maltreatment and school outcomes

OBJECTIVES

This study examined whether physical abuse and community violence exposure (CVE) at age 5 were independently associated with academic performance at age 9, whether these effects were mediated by externalizing and internalizing behaviors, and whether the effects of CVE on mental health and academic performance were observed after accounting for the effects of physical abuse.

METHODS

Data were drawn from the Fragile Families and Child Wellbeing Study. Bayesian factor analysis was conducted in Mplus to form latent factors for internalizing behavior, externalizing behavior, and academic performance. Path analysis was then used to examine direct and indirect associations between CVE, internalizing and externalizing behaviors, and academic performance.

RESULTS

CVE at age 5 was independently negatively associated with academic performance at age 9. Physical abuse at age 5 was not independently associated with academic performance at age 9. The effects of CVE and physical abuse on academic performance were mediated by externalizing behavior, and not internalizing behavior. CVE, externalizing behavior, and internalizing behavior all had a direct negative association with academic performance, after accounting for the effects of physical abuse on externalizing behavior.

CONCLUSIONS AND IMPLICATIONS

The findings confirmed that community violence has a negative impact on school performance above and beyond the effects of interpersonal violence. These findings reinforce the need for communitywide prevention programs that reduce violence. These findings suggest that more attention needs to be paid to how younger children are impacted by CVE and physical abuse, both through their own experiences or the experiences of their caregivers.

Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data. These issues range from technical problems with the data used and features constructed, to problematic modeling assumptions, to limited interpretability, to the models' contributions to bias and inequality. Computational researchers have sought out technical solutions to these problems. The primary contribution of the present work is to argue that there is a limit to these technical solutions. At this limit, we must instead turn to social theory. We show how social theory can be used to answer basic methodological and interpretive questions that technical solutions cannot when building machine learning models, and when assessing, comparing, and using those models. In both cases, we draw on related existing critiques, provide examples of how social theory has already been used constructively in existing work, and discuss where other existing work may have benefited from the use of specific social theories. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data.

Measuring the predictability of life outcomes with a scientific mass collaboration

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Introduction to the Special Collection on the Fragile Families Challenge

The Fragile Families Challenge is a scientific mass collaboration designed to measure and understand the predictability of life trajectories. Participants in the Challenge created predictive models of six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. This Special Collection includes 12 articles describing participants' approaches to predicting these six outcomes as well as 3 articles describing methodological and procedural insights from running the Challenge. This introduction will help readers interpret the individual articles and help researchers interested in running future projects similar to the Fragile Families Challenge.