6533b831fe1ef96bd1299a1a
RESEARCH PRODUCT
Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data
Pascal JürgensMelanie MaginBirgit Starksubject
Erhebungstechniken und Analysetechniken der SozialwissenschaftenSozialwissenschaften SoziologieNutzungComputer sciencebusiness.industrydigital traces; media exposure; nonreactive measurement; quantitative methods; self-reports; survey; tracking datautilizationGeneral Social SciencesInformation technologyDigitale MedienLibrary and Information SciencesData scienceComputer Science Applicationsdata captureMethods and Techniques of Data Collection and Data Analysis Statistical Methods Computer Methodsddc:300MessungTracking datameasurementDatengewinnungbusinessSocial sciences sociology anthropologyLawdigital mediadescription
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. © 2019. This is the authors' accepted and refereed manuscript to the article. The final authenticated version is available online at: https://doi.org/10.1177%2F0894439319831643
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 | Social Science Computer Review |