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Evaluating Quality of Online Behavior Data
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis has two purposes; emphasizing the importance of data quality of Big Data, and identifying and evaluating potential error sources in JavaScript tracking (a client side on - site online behavior clickstream data collection method commonly used in web analytics). The importance of data quality of Big Data is emphasized through the evaluation of JavaScript tracking. The Total Survey Error framework is applied to JavaScript tracking and 17 nonsampling error sources are identified and evaluated. The bias imposed by these error sources varies from large to small, but the major takeaway is the large number of error sources actually identified. More work is needed. Big Data has much to gain from quality work. Similarly, there is much that can be done with statistics in web analytics.

Place, publisher, year, edition, pages
2013. , 48 p.
Keyword [en]
Big Data, Data quality, Total Survey Error, Nonsampling errors, Web analytics, Clickstream data, JavaScript tracking
National Category
Social Sciences Interdisciplinary
URN: urn:nbn:se:su:diva-97524OAI: diva2:678725
2013-05-31, B705, Department of Statistics, Stockholm University, Stockholm, 09:00 (English)
Available from: 2014-01-23 Created: 2013-12-12 Last updated: 2014-01-23Bibliographically approved

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Evaluating Quality of Online Behavior Data(789 kB)395 downloads
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