Overcoming compliance bias in self-report studies : a cross-study analysis
van Berkel, Niels; Goncalves, Jorge; Hosio, Simo; Sarsenbayeva, Zhanna; Velloso, Eduardo; Kostakos, Vassilis (2019-10-10)
Niels van Berkel, Jorge Goncalves, Simo Hosio, Zhanna Sarsenbayeva, Eduardo Velloso, Vassilis Kostakos, Overcoming compliance bias in self-report studies: A cross-study analysis, International Journal of Human-Computer Studies, Volume 134, 2020, Pages 1-12, ISSN 1071-5819, https://doi.org/10.1016/j.ijhcs.2019.10.003
© 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2020043023360
Tiivistelmä
Abstract
A popular methodology used for in situ observations is the Experience Sampling Method (ESM), in which participants intermittently answer short questionnaires. We analyse a set of recent ESM studies and find substantial differences in the number of collected responses across participants. These differences amount to ’compliance bias’, as the experiences of responsive participants skew the results. Our work develops ways for researchers to ensure the collection of an adequate number of responses across participants. Through a cross-study analysis of ESM studies, we construct a model that describes the effect of contextual, routine, and study-specific factors on participants’ response rate. In addition to previous work, which aims to maximise the number of total responses, this work also aims to achieve a more equal distribution of responses between participants. In order to achieve this goal, we analyse which contextual cues can be personalised to achieve a higher response rate. Our results highlight a number of factors that have a strong effect on participants’ response rate and can guide the design of future experiments.
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