Reducing the number of non-naïve participants in Mechanical Turk samples
Using participants who have been previously exposed to experimental stimuli (referred to as non-naïveté) can reduce effect sizes. The workforce of Amazon's Mechanical Turk is particularly vulnerable to this problem and solutions are usually cost and time inefficient and of mixed effectiveness....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Methods in Psychology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590260120300199 |
| _version_ | 1819278930287263744 |
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| author | Ethan A. Meyers Alexander C. Walker Jonathan A. Fugelsang Derek J. Koehler |
| author_facet | Ethan A. Meyers Alexander C. Walker Jonathan A. Fugelsang Derek J. Koehler |
| author_sort | Ethan A. Meyers |
| collection | DOAJ |
| description | Using participants who have been previously exposed to experimental stimuli (referred to as non-naïveté) can reduce effect sizes. The workforce of Amazon's Mechanical Turk is particularly vulnerable to this problem and solutions are usually cost and time inefficient and of mixed effectiveness. In response to this problem and its currently underwhelming solutions, we tested various participant recruitment strategies designed to recruit participants naïve to frequently used experimental stimuli. We collected samples using maximum HIT restrictions (50 for Experiment 1 and 2, 500 for Experiment 2) and TurkPrime's (now CloudResearch) naiveté feature and compared them to samples recruited with standard restrictions (95% HIT approval rating). In these comparisons, we replicated past findings where using nonnaïve (vs. naïve) participants has been shown to reduce effect sizes and affect performance on a variety of tasks (e.g., the Cognitive Reflection Test, a Public Goods Game). We demonstrate that restricting by the maximum number of HITs heavily reduces the number of “experienced” research subjects in samples but necessitates some sacrifice in data quality and collection speed. We discuss the pragmatics of our method, its limitations, and future directions for solving the problem of non-naïveté on Mechanical Turk. For those looking to avoid this issue, we recommend setting a maximum HIT restriction of 50 when recruiting participants. |
| first_indexed | 2024-12-24T00:19:50Z |
| format | Article |
| id | doaj.art-45a2316901f847e8bf25776402229943 |
| institution | Directory Open Access Journal |
| issn | 2590-2601 |
| language | English |
| last_indexed | 2024-12-24T00:19:50Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Methods in Psychology |
| spelling | doaj.art-45a2316901f847e8bf257764022299432022-12-21T17:24:37ZengElsevierMethods in Psychology2590-26012020-12-013100032Reducing the number of non-naïve participants in Mechanical Turk samplesEthan A. Meyers0Alexander C. Walker1Jonathan A. Fugelsang2Derek J. Koehler3Corresponding author.; Department of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, CanadaDepartment of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, CanadaDepartment of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, CanadaDepartment of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, CanadaUsing participants who have been previously exposed to experimental stimuli (referred to as non-naïveté) can reduce effect sizes. The workforce of Amazon's Mechanical Turk is particularly vulnerable to this problem and solutions are usually cost and time inefficient and of mixed effectiveness. In response to this problem and its currently underwhelming solutions, we tested various participant recruitment strategies designed to recruit participants naïve to frequently used experimental stimuli. We collected samples using maximum HIT restrictions (50 for Experiment 1 and 2, 500 for Experiment 2) and TurkPrime's (now CloudResearch) naiveté feature and compared them to samples recruited with standard restrictions (95% HIT approval rating). In these comparisons, we replicated past findings where using nonnaïve (vs. naïve) participants has been shown to reduce effect sizes and affect performance on a variety of tasks (e.g., the Cognitive Reflection Test, a Public Goods Game). We demonstrate that restricting by the maximum number of HITs heavily reduces the number of “experienced” research subjects in samples but necessitates some sacrifice in data quality and collection speed. We discuss the pragmatics of our method, its limitations, and future directions for solving the problem of non-naïveté on Mechanical Turk. For those looking to avoid this issue, we recommend setting a maximum HIT restriction of 50 when recruiting participants.http://www.sciencedirect.com/science/article/pii/S2590260120300199Mechanical TurkNonnaiveteHIT restrictionData quality |
| spellingShingle | Ethan A. Meyers Alexander C. Walker Jonathan A. Fugelsang Derek J. Koehler Reducing the number of non-naïve participants in Mechanical Turk samples Methods in Psychology Mechanical Turk Nonnaivete HIT restriction Data quality |
| title | Reducing the number of non-naïve participants in Mechanical Turk samples |
| title_full | Reducing the number of non-naïve participants in Mechanical Turk samples |
| title_fullStr | Reducing the number of non-naïve participants in Mechanical Turk samples |
| title_full_unstemmed | Reducing the number of non-naïve participants in Mechanical Turk samples |
| title_short | Reducing the number of non-naïve participants in Mechanical Turk samples |
| title_sort | reducing the number of non naive participants in mechanical turk samples |
| topic | Mechanical Turk Nonnaivete HIT restriction Data quality |
| url | http://www.sciencedirect.com/science/article/pii/S2590260120300199 |
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