Conducting online research on Amazon Mechanical Turk and beyond. (2020)

• Record Type: Book
• Further Information: Note: Leib Litman, Jonathan Robinson.
• Authors: Litman, Leib; Robinson, Jonathan
• Contents: 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;
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 001.4202854678; Internet research; Cloud computing; Web services
• Languages: English
• ISBNs: 9781506391113
• Related ISBNs: 9781506391137
• Notes: Note: Description based on CIP data; resource not viewed.
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