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classEx — an online tool for lab-in-the-field experiments with smartphones

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  • Giamattei, Marcus
  • Lambsdorff, Johann Graf

Abstract

classEx is an online tool for running experiments with smartphones. It can be employed for lab-in-the-field experiments, allowing the investigation of behavior in natural settings and with large groups, even if their size is unknown and varying. Standard experiments with multiple treatments, monetary incentives, roles, groups, rounds and stages are available in a ready-made format. An easy-to-use modular backend system allows users to conveniently implement their own experiments. Replication is facilitated by the possibility of sharing experiments and results with other researchers. We provide some applications, describe the functionality and show how classEx tackles the challenges of lab-in-the-field research.

Suggested Citation

  • Giamattei, Marcus & Lambsdorff, Johann Graf, 2019. "classEx — an online tool for lab-in-the-field experiments with smartphones," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 223-231.
    • Handle: RePEc:eee:beexfi:v:22:y:2019:i:c:p:223-231
    DOI: 10.1016/j.jbef.2019.04.008
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    Cited by:

    1. Chan, Shu Wing & Schilizzi, Steven & Iftekhar, Md Sayed & Da Silva Rosa, Raymond, 2019. "Web-based experimental economics software: How do they compare to desirable features?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 138-160.
    2. Verena Dorner & Marcus Giamattei & Matthias Greiff, 2020. "The Market for Reviews: Strategic Behavior of Online Product Reviewers with Monetary Incentives," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 72(3), pages 397-435, July.
    3. Kumar, Satish & Rao, Sandeep & Goyal, Kirti & Goyal, Nisha, 2022. "Journal of Behavioral and Experimental Finance: A bibliometric overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    4. Anna Kerkhof & Johannes Münster, 2021. "Detecting coverage bias in user-generated content," ECONtribute Discussion Papers Series 057, University of Bonn and University of Cologne, Germany.
    5. Claus, Corinna & Köhler, Ekkehard A. & Krieger, Tim, 2022. "Can moral reminders curb corruption? Evidence from an online classroom experiment," Discussion Paper Series 2022-01, University of Freiburg, Wilfried Guth Endowed Chair for Constitutional Political Economy and Competition Policy.
    6. Anna Kerkhof & Johannes Münster, 2021. "Detecting Coverage Bias in User-Generated Content," CESifo Working Paper Series 8844, CESifo.
    7. Fidanoski, Filip & Johnson, Timothy, 2023. "A z-Tree implementation of the Dynamic Experiments for Estimating Preferences [DEEP] method," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    8. Beck, Tobias, 2021. "How the honesty oath works: Quick, intuitive truth telling under oath," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    9. Marcus Giamattei & Kyanoush Seyed Yahosseini & Simon Gächter & Lucas Molleman, 2020. "LIONESS Lab: a free web-based platform for conducting interactive experiments online," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 6(1), pages 95-111, June.

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    More about this item

    Keywords

    Experimental software; Lab-in-the-field; Experimental standards; Smartphones; Classroom experiments;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

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