Dataset Information

Individual altruistic choice and attitude data from Amazon?s Mechanical Turk.

ABSTRACT: This article presents new data on individual altruistic choices and beliefs in a two-player sharing game, collected for the study reported in te Velde (2018) [2]. The decision maker in this game had to choose how much of a fixed budget to share with another anonymous Amazon Mechanical Turk user participating in the same study. With a known probability, the decision maker?s choice was overridden by one of two known default options, so that the recipient was unable to attribute those two outcomes to the decision maker with certainty. In addition to choice data, each participant?s incentivized guess of average behavior is included in the dataset, along with responses to a set of survey questions asking for opinions or beliefs about the morality of different options or the preferences or experienced utility of the recipient. Lastly, the dataset contains basic demographic information for each participant, along with detailed timing information about recruitment and each phase of the study.

SUBMITTER: Te Velde VL

PROVIDER: S-EPMC6098210 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Individual altruistic choice and attitude data from Amazon׳s Mechanical Turk.

Te Velde Vera L VL

Data in brief 20180727

This article presents new data on individual altruistic choices and beliefs in a two-player sharing game, collected for the study reported in te Velde (2018) [2]. The decision maker in this game had to choose how much of a fixed budget to share with another anonymous Amazon Mechanical Turk user participating in the same study. With a known probability, the decision maker׳s choice was overridden by one of two known default options, so that the recipient was unable to attribute those two outcomes ...[more]

PMID: 30128335

Similar Datasets

Project description:Internet interventions face significant challenges in recruitment and attrition rates are typically high and problematic. Finding innovative yet scientifically valid avenues for attaining and retaining participants is therefore of considerable importance. The main goal of this study was to compare recruitment process and participants characteristics between two similar randomized control trials of mood management interventions. One of the trials (Bunge et al., 2016) was conducted with participants recruited from Amazon's Mechanical Turk (AMT), and the other trial recruited via Unpaid Internet Resources (UIR). Methods:The AMT sample (Bunge et al., 2016) consisted of 765 adults, and the UIR sample (recruited specifically for this study) consisted of 329 adult US residents. Participants' levels of depression, anxiety, confidence, motivation, and perceived usefulness of the intervention were assessed. The AMT sample was financially compensated whereas the UIR was not. Results:AMT yielded higher recruitment rates per month (p?<?.05). At baseline, the AMT sample reported significantly lower depression and anxiety scores (p?<?.001 and p?<?.005, respectively) and significantly higher mood, motivation, and confidence (all p?<?.001) compared to the UIR sample. AMT participants spent significantly less time on the site (p?<?.05) and were more likely to complete follow-ups than the UIR sample (p?<?.05). Both samples reported a significant increase in their level of confidence and motivation from pre- to post-intervention. AMT participants showed a significant increase in perceived usefulness of the intervention (p?<?.0001), whereas the UIR sample did not (p?=?.1642). Conclusions:By using AMT, researchers can recruit very rapidly and obtain higher retention rates; however, these participants may not be representative of the general online population interested in clinical interventions. Considering that AMT and UIR participants differed in most baseline variables, data from clinical studies resulting from AMT samples should be interpreted with caution.

Project description:IntroductionStakeholder involvement has been described as an indispensable part of health research priority setting. Yet, more than 75% of the exercises using the Child Health and Nutrition Research Initiative (CHNRI) methodology have omitted the step involving stakeholders in priority setting. Those that have used stakeholders have rarely used the public, possibly due to the difficulty of assembling and/or accessing a public stakeholder group. In order to strengthen future exercises using the CHNRI methodology, we have used a public stakeholder group to weight 15 CHNRI criteria, and have explored regional differences or being a health stakeholder is influential, and whether the criteria are collapsible.MethodsUsing Amazon Mechanical Turk (AMT), an online crowdsourcing platform, we collected demographic information and conducted a Likert-scale format survey about the importance of the CHNRI criteria from 1051 stakeholders. The Kruskal-Wallis test, with Dunn's test for posthoc comparisons, was used to examine regional differences and Wilcoxon rank-sum test was used to analyse differences between stakeholders with health training/background and stakeholders without a health background and by region. A Factor Analysis (FA) was conducted on the criteria to identify the main domains connecting them. Criteria means were converted to weights.ResultsThere were regional differences in thirteen of fifteen criteria according to the Kruskal-Wallis test and differences in responses from health stakeholders vs those who were not in eleven of fifteen criteria using the Wilcoxon rank-sum test. Three components were identified: improve and impact results; implementation and affordability; and, study design and dissemination. A formula is provided to convert means to weights for future studies.ConclusionIn future CHNRI studies, researchers will need to ensure adequate representation from stakeholders to undue bias of CHNRI results. These results should be used in combination with other stakeholder groups, including government, donors, policy makers, and bilateral agencies. Global and regional stakeholder groups scored CHNRI criteria differently; due to this, researchers should consider which group to use in their CHNRI exercises.

Project description:BackgroundWearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices.ObjectiveThis study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data.MethodsThis was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses.ResultsA total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income.ConclusionsMost wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Dataset Information

Individual altruistic choice and attitude data from Amazon?s Mechanical Turk.

Publications

Individual altruistic choice and attitude data from Amazon׳s Mechanical Turk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets