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Improving metadata infrastructure for complex surveys: 
Insights from the Fragile Families Challenge

Author

Listed:
  • Alexander Kindel

    (Princeton University)

  • Vineet Bansal

    (Princeton University)

  • Kristin Catena

    (Princeton University)

  • Thomas Hartshorne

    (Princeton University)

  • Kate Jaeger

    (Princeton University)

Abstract

Researchers rely on metadata systems to prepare data for analysis. As the complexity of datasets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study based on the experiences of participants in the Fragile Families Challenge. We demonstrate how treating metadata as data—that is, releasing comprehensive information about variables in a format amenable to both automated and manual processing—can make the task of data preparation less arduous and less error-prone for all types of data analysis. We hope that our work will facilitate new applications of machine learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. We have open-sourced the tools we created so that others can use and improve them.

Suggested Citation

  • Alexander Kindel & Vineet Bansal & Kristin Catena & Thomas Hartshorne & Kate Jaeger, 2018. "Improving metadata infrastructure for complex surveys: 
Insights from the Fragile Families Challenge," Working Papers wp18-10-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
    • Handle: RePEc:pri:crcwel:wp18-10-ff
    as

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    File URL: https://fragilefamilies.princeton.edu/sites/fragilefamilies/files/wp18-10-ff.pdf
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    References listed on IDEAS

    as
    1. Jeremy Freese, 2007. "Replication Standards for Quantitative Social Science," Sociological Methods & Research, , vol. 36(2), pages 153-172, November.
    2. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    3. Reichman, Nancy E. & Teitler, Julien O. & Garfinkel, Irwin & McLanahan, Sara S., 2001. "Fragile Families: sample and design," Children and Youth Services Review, Elsevier, vol. 23(4-5), pages 303-326.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Sandrah Eckel & Roger Peng, 2009. "Interacting with local and remote data repositories using the stashR package," Computational Statistics, Springer, vol. 24(2), pages 247-254, May.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Watts, Duncan J & Beck, Emorie D & Bienenstock, Elisa Jayne & Bowers, Jake & Frank, Aaron & Grubesic, Anthony & Hofman, Jake M. & Rohrer, Julia Marie & Salganik, Matthew, 2018. "Explanation, prediction, and causality: Three sides of the same coin?," OSF Preprints u6vz5, Center for Open Science.

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    More about this item

    Keywords

    metadata; survey research; data sharing; quantitative methodology; computational social science;
    All these keywords.

    JEL classification:

    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations

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    This paper has been announced in the following NEP Reports:

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