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Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

Socius : sociological research for a dynamic world · 11 authors, 1 centre

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TL;DR

The authors redesigned the metadata system for the Fragile Families and Child Wellbeing Study from a set of PDF documents into a machine-actionable CSV-based system, motivated by widespread data-preparation difficulties observed among participants in the Fragile Families Challenge. They demonstrate that treating metadata as data—standardizing variables, expanding metadata fields, and providing machine-readable formats—reduces the burden and error rate of preparing longitudinal survey data for analysis.

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In response, they redesigned the metadata system by transforming human-readable PDF documents into a CSV file containing comprehensive metadata on all variables collected since the study's start. The new system standardizes variables, provides expanded metadata fields capturing creators' previously tacit knowledge, and makes metadata available in multiple formats supporting both manual and automated processing. The paper contributes methodological insights about metadata architecture for complex longitudinal surveys and advocates for greater scholarly attention to data preparation as a research stage.

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other

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research methods

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ORIGINAL PAPER

PMC10198672

Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

Socius : sociological research for a dynamic world

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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 describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., 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. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.

CITATION EXPORT

Alexander T. Kindel, Vineet Bansal, Kristin D. Catena et al.. Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge. Socius : sociological research for a dynamic world. (2019). https://doi.org/10.1177/2378023118817378

AUTHORS

11 · 1 CENTRES

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

1Princeton University, Princeton, NJ, USA