Overcoming Compliance Bias in Self-Report Studies: A Cross-Study Analysis

Niels van Berkel, Jorge Goncalves, Simo Hosio, Zhanna Sarsenbayeva, Eduardo Velloso, Vassilis Kostakos

Research output: Contribution to journalJournal articleResearchpeer-review

24 Citations (Scopus)

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.

Original languageEnglish
JournalInternational Journal of Human-Computer Studies
Volume134
Pages (from-to)1-12
Number of pages12
ISSN1071-5819
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Bias
  • Completion rate
  • Compliance
  • ESM
  • Ecological Momentary Assessment
  • Experience Sampling Method
  • Response rate
  • Self-report

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