Dataset Information

Collecting response times using Amazon Mechanical Turk and Adobe Flash.

ABSTRACT: Crowdsourcing systems like Amazon's Mechanical Turk (AMT) allow data to be collected from a large sample of people in a short amount of time. This use has garnered considerable interest from behavioral scientists. So far, most experiments conducted on AMT have focused on survey-type instruments because of difficulties inherent in running many experimental paradigms over the Internet. This study investigated the viability of presenting stimuli and collecting response times using Adobe Flash to run ActionScript 3 code in conjunction with AMT. First, the timing properties of Adobe Flash were investigated using a phototransistor and two desktop computers running under several conditions mimicking those that may be present in research using AMT. This experiment revealed some strengths and weaknesses of the timing capabilities of this method. Next, a flanker task and a lexical decision task implemented in Adobe Flash were administered to participants recruited with AMT. The expected effects in these tasks were replicated. Power analyses were conducted to describe the number of participants needed to replicate these effects. A questionnaire was used to investigate previously undescribed computer use habits of 100 participants on AMT. We conclude that a Flash program in conjunction with AMT can be successfully used for running many experimental paradigms that rely on response times, although experimenters must understand the limitations of the method.

SUBMITTER: Simcox T

PROVIDER: S-EPMC5283577 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Collecting response times using Amazon Mechanical Turk and Adobe Flash.

Simcox Travis T Fiez Julie A JA

Behavior research methods 20140301 1

Crowdsourcing systems like Amazon's Mechanical Turk (AMT) allow data to be collected from a large sample of people in a short amount of time. This use has garnered considerable interest from behavioral scientists. So far, most experiments conducted on AMT have focused on survey-type instruments because of difficulties inherent in running many experimental paradigms over the Internet. This study investigated the viability of presenting stimuli and collecting response times using Adobe Flash to ru ...[more]

PMID: 23670340

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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.

Dataset Information

Collecting response times using Amazon Mechanical Turk and Adobe Flash.

Publications

Collecting response times using Amazon Mechanical Turk and Adobe Flash.

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