Conducting online research on Amazon Mechanical Turk and beyond

著者

    • Litman, Leib
    • Robinson, Jonathan

書誌事項

Conducting online research on Amazon Mechanical Turk and beyond

Leib Litman, Jonathan Robinson

SAGE, c2021

  • : pbk.

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内容説明・目次

内容説明

Conducting Online Research on Amazon Mechanical Turk (R) and Beyond, written by Leib Litman and Jonathan Robinson, provides both students and experienced researchers with essential information about the online platforms most often used for social science research. This insightful and accessible text answers common questions like, "How do I maintain data quality in online studies?," "What is the best way to recruit hard-to-reach samples?" and "How can researchers navigate the ethical issues that are unique to online research?" Drawing on their experiences as the founders of CloudResearch (formerly TurkPrime), the authors provide information that guides new users planning their first online studies and engages even the most experienced researchers with detailed discussions about the challenges of online research. The book begins with an overview of Amazon's Mechanical Turk and its rapid rise within academic research. Then, the authors describe how to set up an MTurk study with screenshots that walk readers through the steps of creating an account, designing a study, collecting data, and using third-party applications to enhance MTurk's functionality. Later chapters provide readers with a detailed understanding of the MTurk environment and use data from hundreds of thousands of participants and tens of millions of completed tasks to dive into issues like participant demographics, sources of sampling bias, and the generalizability of findings from MTurk. Finally, the book explores the benefits of using other online platforms as a complement to MTurk and the ethical issues that are unique to conducting research with online participant platforms. Throughout the book, the authors share hands-on advice and best practices, such as those for conducting longitudinal studies or carrying out complex studies. Altogether the mix of data, insight, and advice make this book an essential resource for researchers who want to understand the online environment and the most effective ways to conduct research online.

目次

Chapter 1: Introduction A Scientific Revolution in the Making A Brief History of Online Research in the Social and Behavioral Sciences: From HTML 2.0 to Mechanical Turk The Use of Online Samples in Applied Behavioral Research Amazon Mechanical Turk Chapter 2: The Mechanical Turk Ecosystem - Leib Litman, Cheskie Rosenzweig, Jonathan Robinson How Quality is Maintained Reputation Mechanism Selectively Recruiting Specific Workers Protections for Workers Communicating with Workers A Worker's Perspective Worker Communities Chapter 3: Conducting a Study on Mechanical Turk Sample Project Setting up a Requester Account on Mechanical Turk Creating a HIT The 'Design Layout' tab Monitoring Progress on the Requester's Dashboard When a Worker Runs Out of Time Sample Study Results Conducting Follow-up Studies Using Requester-Issued Qualifications Appendix A: Checklist for best practices of setting up a Mechanical Turk HIT Chapter 4: API and Third Party Apps Third Party API-based Platforms Common Uses for API Scripts and Third Party API-based Apps TurkPrime Chapter 5: Data Quality Issues on MTurk - Jesse Chandler, Gabriele Paolacci, David Hauser Defining and Measuring Data Quality Measuring Individual Participant Data Quality Causes of and Cures for Poor Data Quality Concluding Thoughts Chapter 6: Who are the Mechanical Turk Workers? Sources of Data Location of Workers in the US Demographics of Mechanical Turk Chapter 7: Sampling Mechanical Turk Workers: Problems and Solutions Sampling on Mechanical Turk Sources of Sampling Bias The Problem of Superworkers Time-of-day Effects Pay Rate Dropout Sampling Best Practices Chapter 8: Data Representativeness of Mechanical Turk Samples Representativeness, surveys, and survey sampling The methodology of survey sampling Mechanical Turk as a sampling frame The fit-for-purpose framework Chapter Overview: Chapter 9: Conducting Longitudinal Research on Amazon Mechanical Turk Why Longitudinal Research? Retention, Longitudinal Research, and MTurk Case Studies Best practices for longitudinal research Chapter 10: Beyond Mechanical Turk: Using Online Market Research Platforms Limitations of MTurk Online probability-based panels Online market research platforms Overall comparisons between Mechanical Turk and market research platforms Chapter 11: Conducting Ethical Online Research: A Data-Driven Approach Historical background Risk of harm in online research Research on sensitive topics A deeper dive into controversial and complex issues Economics of Mechanical Turk: Considerations for setting wages Setting wages: Considerations of ethics and methodology Considerations for rejecting, blocking and disqualifying workers. Practical advice for requester/worker interactions Anonymity Appendix A

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