Big data? Big issues: Degradation in longitudinal data and implications for social sciences

Matthew S. Weber, Hai Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This article analyzes the issue of degradation of data accuracy in large-scale longitudinal data sets. Recent research points to a number of issues with large-scale data, including problems of reliability, accuracy and quality over time. Simultaneously, large-scale data is increasingly being utilized in the social sciences. As scholars work to produce theoretically grounded research utilized "small-scale" methods, it is important for researchers to better understand the critical issues associated with the analysis of large-scale data. In order to illustrate the issues associated with this type of research, a case study analysis of archival Internet data is presented focusing on the issues of degradation of data accuracy over time. Suggestions for future studies are given.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450336727
DOIs
StatePublished - Jun 28 2015
Externally publishedYes
Event7th ACM Web Science Conference, WebSci 2015 - Oxford, United Kingdom
Duration: Jun 28 2015Jul 1 2015

Publication series

NameProceedings of the 2015 ACM Web Science Conference

Other

Other7th ACM Web Science Conference, WebSci 2015
CountryUnited Kingdom
CityOxford
Period6/28/157/1/15

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    Weber, M. S., & Nguyen, H. (2015). Big data? Big issues: Degradation in longitudinal data and implications for social sciences. In Proceedings of the 2015 ACM Web Science Conference (Proceedings of the 2015 ACM Web Science Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2786451.2786482