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Special Collection: Fragile Families Challenge


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Introduction to the Special Collection on the Fragile Families Challenge

  • Matthew J. Salganik
  • Ian Lundberg
  • Alexander T. Kindel
  • Sara McLanahan

Abstract

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 ...
Open AccessResearch articleFirst published Sep 10, 2019
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Predicting Layoff among Fragile Families

  • Caitlin E. Ahearn
  • Jennie E. Brand

Abstract

The loss of a job is the loss of a major social and economic role and is associated with long-term negative economic and psychological consequences for workers and families. Modeling the causal effects of a social process like layoff with observational ...
Open AccessResearch articleFirst published Sep 10, 2019
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Leveraging Multiple Machine-Learning Techniques to Predict Major Life Outcomes from a Small Set of Psychological and Socioeconomic Variables: A Combined Bottom-up/Top-down Approach

  • Drew M. Altschul

Abstract

Predicting longitudinal outcomes from thousands of variables across multiple waves provides impressive opportunities to identify variables of importance, but what is the most efficient way to carry out such analyses on hundreds or thousands of variables? ...
Open AccessResearch articleFirst published Sep 10, 2019
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Variable Selection and Parameter Tuning for BART Modeling in the Fragile Families Challenge

  • Nicole Bohme Carnegie
  • James Wu

Abstract

Our goal for the Fragile Families Challenge was to develop a hands-off approach that could be applied in many settings to identify relationships that theory-based models might miss. Data processing was our first and most time-consuming task, particularly ...
Open AccessResearch articleFirst published Sep 10, 2019
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A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests

  • Ryan Compton

Abstract

Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and ...
Open AccessResearch articleFirst published Sep 10, 2019
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Black-Box Models and Sociological Explanations: Predicting High School Grade Point Average Using Neural Networks

  • Thomas Davidson

Abstract

The Fragile Families Challenge provided an opportunity to empirically assess the applicability of black-box machine learning models to sociological questions and the extent to which interpretable explanations can be extracted from these models. In this ...
Open AccessResearch articleFirst published Sep 10, 2019
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Humans in the Loop: Incorporating Expert and Crowd-Sourced Knowledge for Predictions Using Survey Data

  • Anna Filippova
  • Connor Gilroy
  • Ridhi Kashyap
  • Antje Kirchner
  • Allison C. Morgan
  • Kivan Polimis
  • Adaner Usmani
  • Tong Wang

Abstract

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to ...
Open AccessResearch articleFirst published Sep 10, 2019
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Imputing Data for the Fragile Families Challenge: Identifying Similar Survey Questions with Semiautomated Methods

  • Brian J. Goode
  • Debanjan Datta
  • Naren Ramakrishnan

Abstract

The Fragile Families Challenge charged participants to predict six outcomes for 4,242 children and their families interviewed in the Fragile Families and Child Wellbeing Study. These outcome variables are grade point average, grit, material hardship, ...
Open AccessResearch articleFirst published Sep 10, 2019
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When 4 ≈ 10,000: The Power of Social Science Knowledge in Predictive Performance

  • Stephen McKay

Abstract

Computer science has devised leading methods for predicting variables; can social science compete? The author sets out a social scientific approach to the Fragile Families Challenge. Key insights included new variables constructed according to theory (...
Open AccessResearch articleFirst published Sep 10, 2019
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Predicting GPA at Age 15 in the Fragile Families and Child Wellbeing Study

  • Louis Raes

Abstract

In this paper, we describe in detail the different approaches we used to predict the GPA of children at the age of 15 in the context of the Fragile Families Challenge. Our best prediction improved about 18 percent in terms of mean squared error over a ...
Open AccessResearch articleFirst published Sep 10, 2019
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Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge

  • Daniel E. Rigobon
  • Eaman Jahani
  • Yoshihiko Suhara
  • Khaled AlGhoneim
  • Abdulaziz Alghunaim
  • Alex “Sandy” Pentland
  • Abdullah Almaatouq

Abstract

In this article, the authors discuss and analyze their approach to the Fragile Families Challenge. The data consisted of more than 12,000 features (covariates) about the children and their parents, schools, and overall environments from birth to age 9. ...
Open AccessResearch articleFirst published Sep 10, 2019
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Friend Request Pending: A Comparative Assessment of Engineering- and Social Science–Inspired Approaches to Analyzing Complex Birth Cohort Survey Data

  • Claudia V. Roberts

Abstract

The Fragile Families Challenge is a mass collaboration social science data challenge whose aim is to learn how various early childhood variables predict the long-term outcomes of children. The author describes a two-step approach to the Fragile Families ...
Open AccessResearch articleFirst published Sep 10, 2019
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Using LASSO to Assist Imputation and Predict Child Well-being

  • Diana Stanescu
  • Erik Wang
  • Soichiro Yamauchi

Abstract

This article documents an approach to predicting children’s well-being using data from the Fragile Families and Child Wellbeing Study, which are representative of births in large U.S. cities. The authors use the least absolute shrinkage and selection ...
Open AccessResearch articleFirst published Sep 10, 2019
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Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

  • Alexander T. Kindel
  • Vineet Bansal
  • Kristin D. Catena
  • Thomas H. Hartshorne
  • Kate Jaeger
  • Dawn Koffman
  • Sara McLanahan
  • Maya Phillips
  • Shiva Rouhani
  • Ryan Vinh
  • Matthew J. Salganik

Abstract

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article ...
Open AccessResearch articleFirst published Sep 10, 2019
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Data-Specific Functions: A Comment on Kindel et al.

  • Jacob C. Fisher

Abstract

In this issue, Kindel et al. describe a new approach to managing survey data in service of the Fragile Families Challenge, which they call “treating metadata as data.” Although the approach they present is a good first step, a more ambitious proposal ...
Open AccessResearch articleFirst published Sep 10, 2019
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Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge

  • David M. Liu
  • Matthew J. Salganik

Abstract

Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although ...
Open AccessResearch articleFirst published Sep 10, 2019
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Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge

  • Ian Lundberg
  • Arvind Narayanan
  • Karen Levy
  • Matthew J. Salganik

Abstract

Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to ...
Open AccessResearch articleFirst published Sep 10, 2019
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