Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges




big data, education policy, network text analysis, sentiment analysis, text mining, topic models


Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data—characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy.


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Author Biography

Yinying Wang, Georgia State University

Yinying Wang is an assistant professor of educational leadership in the Department of Educational Policy Studies at College of Education and Human Development, Georgia State University. Her research interests include social network analysis in educational leadership and policy, social media in education policy making and organizational communication, and educational technology leadership.





How to Cite

Wang, Y. (2017). Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges. Education Policy Analysis Archives, 25, 94.