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Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data
[journal article]
Abstract The pervasive use of mobile information technologies brings new patterns of media usage, but also challenges to the measurement of media exposure. Researchers wishing to, for example, understand the nature of selective exposure on algorithmically driven platforms need to precisely attribute individu... view more
The pervasive use of mobile information technologies brings new patterns of media usage, but also challenges to the measurement of media exposure. Researchers wishing to, for example, understand the nature of selective exposure on algorithmically driven platforms need to precisely attribute individuals’ exposure to specific content. Prior research has used tracking data to show that survey-based self-reports of media exposure are critically unreliable. So far, however, little effort has been invested into assessing the specific biases of tracking methods themselves. Using data from a multimethod study, we show that tracking data from mobile devices is linked to systematic distortions in self-report biases. Further inherent but unobservable sources of bias, along with potential solutions, are discussed.... view less
Keywords
digital media; utilization; measurement; data capture
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
digital traces; media exposure; nonreactive measurement; quantitative methods; self-reports; survey; tracking data
Document language
English
Publication Year
2020
Page/Pages
p. 600-615
Journal
Social Science Computer Review, 38 (2020) 5
DOI
https://doi.org/10.1177/0894439319831643
ISSN
1552-8286
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications