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Evaluating mobile-based data collection for crowdsourcing behavioral research.

Dennis T Esch1, Nikolaos Mylonopoulos2, Vasilis Theoharakis3

  • 1Cranfield School of Management, Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, UK. dennis.esch@cranfield.ac.uk.

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|February 28, 2025
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Summary
This summary is machine-generated.

Mobile devices are suitable for online research, with mobile-based responses showing comparable data quality to computer-based ones. However, platform choice and respondent factors significantly impact data attentiveness and quality in crowdsourcing.

Keywords:
Attention checksCrowdsourcingData qualityMTurkMobileOnline researchPollfishProlificQualtrics

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Area of Science:

  • Behavioral research
  • Human-computer interaction
  • Survey methodology

Background:

  • Online crowdsourcing platforms (e.g., MTurk, Prolific) are widely used but primarily recruit computer-based participants.
  • This risks excluding mobile-first users and raises concerns about data quality due to income-driven responses.

Purpose of the Study:

  • To compare data quality across different crowdsourcing platforms, including mobile-first options.
  • To investigate factors influencing data attentiveness and quality in online research.

Main Methods:

  • Collected data from Amazon Mechanical Turk (MTurk), Prolific, Pollfish, and Qualtrics audience panel using identical studies.
  • Constructed an attentiveness composite score to evaluate response quality across platforms and device types (mobile vs. computer).

Main Results:

  • Mobile-based responses from Pollfish and Qualtrics were comparable in quality to computer-based responses from MTurk and Prolific.
  • Platform differences in attentiveness were significant, influenced by incentives, prior activity, distractions, and recent study completion.
  • System 1 thinking correlated with lower attentiveness, mediating device use and attention.

Conclusions:

  • Mobile devices are viable for crowdsourcing behavioral research, challenging prior assumptions.
  • Platform selection and understanding respondent-specific factors are crucial for ensuring high-quality data in online research.
  • Standard attention checks may not adequately identify low-quality responses on platforms like MTurk.