Amazon mturk work demo
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- Is Your Website Easy To Understand? Test it With Qualitative Data Using Amazon MTurk
- 4. Turkers in this canvassing: young, well-educated and frequent users
- Generic web-based annotation tool for Mechanical Turk (v0.1)
- Image/Video Processing
- How it Works
- TurkScanner: Predicting the Hourly Wage of Microtasks
- Amazon Mechanical Turk for LabelMe
- Conducting Survey Research Using MTurk
Is Your Website Easy To Understand? Test it With Qualitative Data Using Amazon MTurk
Try out PMC Labs and tell us what you think. Learn More. Mechanical Turk survey including the Roland Morris Disability Questionnaire and prioritization survey. The involvement of patients in research better aligns evidence generation to the gaps that patients themselves face when making decisions about health care.
The appropriateness of such crowdsourcing methods in medical research has yet to be clarified. The goals of this study were to 1 understand how those on MTurk who screen positive for back pain prioritize research topics compared with those who screen negative for back pain, and 2 determine the qualitative differences in open-ended comments between groups. We compared demographic information and research priorities between the 2 groups and performed qualitative analyses on free-text commentary that participants provided.
We conducted 2 screening waves. We first screened individuals for back pain over 33 days and invited We later screened individuals over 7 days and invited Those with back pain who prioritized were comparable with those without in terms of age, education, marital status, and employment. The group with back pain had a higher proportion of women , The 2 groups agreed on 4 of the top 5 and 9 of the top 10 research priorities. Crowdsourcing platforms such as MTurk support efforts to efficiently reach large groups of individuals to obtain input on research activities.
In the context of back pain, a prevalent and easily understood condition, the rank list of those with back pain was highly correlated with that of those without back pain.
However, subtle differences in the content and quality of free-text comments suggest supplemental efforts may be needed to augment the reach of crowdsourcing in obtaining perspectives from patients, especially from specific populations. Modern health care decision making incorporates expert opinion, practice standards, and the individual preferences and values of patients themselves [ 1 , 2 ]. In support of patient-centered care, patient-centered outcomes research equally seeks to engage patients and the public in designing and implementing research studies.
Efforts to involve patients in research can take various forms ranging from consultative eg, researchers can seek patient opinion about the design of a study to more collaborative approaches eg, patients can be involved as members of the study team itself. Engagement throughout the research process is an important step in developing evidence that will support patients and providers as they make health care decisions.
Identifying and prioritizing research topics—the first phases of patient-centered outcomes research—direct researchers to address the relevant and important problems facing those who may benefit most from study findings; thus, patient involvement is imperative [ 3 ]. Patient-centered outcomes research teams have begun to use novel technology-driven engagement strategies—including social media and crowdsourcing platforms—to augment traditional engagement activities.
Emerging evidence has suggested that online engagement methods such as crowdsourcing may provide an efficient alternative to in-person meetings [ 4 , 5 ]. Crowdsourcing as a whole is appealing in its ability to rapidly obtain responses from a broad and potentially diverse population. For prevalent conditions, such platforms may provide an efficient and effective method for obtaining input on research activities, including research prioritization.
Originally designed to allow the rapid completion of complex but repetitive work, MTurk has been adopted by behavioral scientists and market researchers to serve as a virtual laboratory to quickly and inexpensively administer thought experiments via online surveys, perform market research for organizations, and give insight into the thought processes underlying decision making [ 4 , 7 - 9 ]. Furthermore, some have begun to use MTurk to obtain public opinion on health care-related topics [ 10 ].
Our group has worked to understand the relative strengths and weakness of various patient engagement activities for research prioritization in the context of low back pain. Despite its prevalence and health burden, there is no clear mechanism for patient engagement in the decision making around back pain research [ 5 ]. In a prior study, we compared the research priorities established by patients with back pain who participated in a patient registry with those established by MTurk participants who self-reported having back pain.
The 2 groups ranked research topics similarly, despite large differences in age the MTurk cohort being younger and in selection into the cohorts: those in the patient registry had a formal diagnosis of back pain, whereas the MTurk group was selected on the basis of their Roland Morris Disability Questionnaire RMDQ score. The RMDQ is a validated tool that is used to score back-related disability and was used as a proxy to distinguish those with back pain from those without back pain.
The conclusion of the study was that these two methods of identifying patients for engagement—patient registries and crowdsourcing—complement one another [ 14 ]. Our prior study exposed difficulties in participant selection from a crowdsourced sample for research prioritization.
We had used the RMDQ to find those with back pain but had no understanding of whether this selection process changed the ranking of research topics or improved the information gathered from our cohort. This study, therefore, expands our prior work to a broader population on MTurk, comparing those who screen positive for back pain against those who screen negative for back pain, with categorization based on the RMDQ score.
We sought to understand how these 2 groups differed with respect to their research topic rank lists and additional commentary in order to guide the use of MTurk as a platform to support research prioritization for low back pain. We hypothesized that this comparison would also give insight into the use of MTurk for research prioritization, generally. This study is part of a series of investigations to understand methods of patient engagement, and specifically research topic prioritization for back pain [ 14 - 16 ].
We conducted 2 cross-sectional surveys via MTurk: the first in January targeting those with back pain, and the second in August targeting those without back pain Figure 1 , limiting the MTurk sample to only those residing in the United States. The University of Washington Human Subjects Division provided ethical approval for this study prior to administration of the surveys. Mechanical Turk MTurk enrollment. Schematic flow diagram of enrollment of both cohorts, including screening and response rates.
See also Figure 1 in Truitt et al [ 15 ]. The RMDQ screens for current back-related disability but does not offer clear insight into a possible history of back pain.
Therefore, the group without back pain could have contained individuals with a history of back-related disability that had since improved. We invited a subset of those who took the screening survey to complete a prioritization activity, based on the above categorization. The prioritization survey was extended to those with back pain during the first survey administration and to those without back pain during the second administration.
In addition, participants could add up to 5 additional topics in open-ended comment fields beyond the topics in the list provided. Users provided demographic information at the conclusion of the prioritization survey. Both the screening RMDQ and the prioritization surveys were administered using Research Electronic Data Capture REDCap , a software platform specifically designed for electronic data capture in research studies [ 19 ].
Both surveys were developed by our team prior to administration as an open survey on MTurk. We used neither randomization nor adaptive questioning methods.
We added an internal validation question to the screen such that, if none of the 24 items on the RMDQ applied, participants were instructed to check a box noting this. Those who did not pass this internal check were removed from analysis. Participants were not able to review their answers prior to submitting, but they were able to change answers as they proceeded through screening and prioritization.
We tabulated age, sex, highest level of education attained, current level of employment, and ethnicity and race, reporting frequencies for categorical variables and means for continuous variables to compare participant demographic characteristics. To understand the geographic distribution of our MTurk sample, we tabulated the US states of residences within each group. We created a ranked list of research topics within each group by determining the frequency that each topic was selected as the top priorities and ordered them accordingly.
A Spearman rank-order coefficient was used to compare the rank lists of research topics generated by each group. A Spearman coefficient close to 1 would signify a high level of agreement in the order of the ranked research topic lists between groups; a value close to 0 would signify little agreement in the rank lists; a value approaching —1 would signify that the rank lists are opposite one another.
We performed a Wilcoxon rank sum test, without continuity correction, to understand whether the distribution of rankings—that is, the relative importance of the top- versus bottom-ranked research topic—was the same or different between groups.
Administering 2 separate surveys at 2 different time points opened the possibility for MTurk users to repeat the exercise. We selected those individuals who took the RMDQ both in January and in August to compare how their RMDQ score changed over the time period and, for those who were eligible to take the prioritization survey twice, how their research prioritizations changed. We performed a directed content analysis on the additional comments provided by participants in both groups using an iterative process.
After reviewing all comments, we generated a list of codes that reflected the content. Two members of our team, blinded to the work of one another, applied the codes and, where there were disagreements, a third member reconciled the code applied. To assess the quality of the content provided through open-ended comments, we created a coding scheme to indicate how helpful comments were for designing future research topics. My pain has spread from the lower lumbar region into the hips and down the legs over the last 25 years.
We applied all codes using Dedoose version 7. We screened a total of individuals over 40 days. Of those, The prioritization activity was completed by of those with back pain Table 1 presents the demographic information for the 2 groups. Race was recategorized into Asian, black or African American, white, and other to perform the test of significance, but the original categories are displayed here.
The groups were similar with respect to age, ethnicity, and race. The 2 groups differed in the proportion of men versus woman, current employment status, highest level of education completed, and marital status see Table 1.
The study sample represented 48 states and the District of Columbia, with representation from Wyoming and South Dakota missing in the prioritization results.
The 2 groups agreed on 4 of the top 5 and 9 of the top 10 research topics ranked as most important see Table 2. Both groups ranked topics related to treatment and diagnosis most highly overall, accounting for all of the top 5 most highly ranked topics in the back pain group, and 4 of the top 5 in the no back pain group. The rank lists differed in how the groups ranked the importance of topics such as prevention, clinical definition, and treatment.
Rank lists are divided by group back pain vs no back pain and ordered by rank of the back pain group. A total of 41 participants 1. The mean change in RMDQ score of those who screened twice was 0.
Of the 2 participants eligible to prioritize twice, 1 completed the prioritization activity twice and ranked the same research topic as their top priority both times. Additional comments were provided by 53 The comments from the group with back pain were nearly twice as long as comments from the group without back pain as measured by word and character counts word count average of Qualitative and quantitative differences in the additional comments between groups back pain vs no back pain a.
We grouped the topic areas of additional comments into 13 overarching categories, some of which are shown in Table 4. Of note, research topics related to treatment were suggested most commonly by both groups, followed by prevention-related topics in the back pain group and epidemiology-related topics in the group without back pain.
Topic areas identified by additional comments, by back pain group, subdivided by quality of the comment. To our knowledge, our work is novel in its use of the MTurk platform for obtaining input on research prioritization and its application of a patient-reported outcome measurement tool to select a cohort from a crowdsourced sample [ 14 ].
In fact, only recently has crowdsourcing been used outside of the realm of behavioral and psychological investigations for patient engagement research, and specifically for research prioritization determination [ 21 - 23 ].
The implications of this work are potentially far-reaching: understanding the strengths and limitations of crowdsourcing techniques is important given both the need to engage the public in research activities and the ease of use of platforms such as MTurk.
Obtaining patient and public input and including a diversity of perspectives has posed and remains a challenge. While crowdsourcing platforms can provide a large and often captive audience, finding the right individuals to engage—whether by using a screening survey or by some other method—adds a layer of difficulty. We therefore sought to understand how those with a condition would rank research topics compared with those without a condition.
In the context of low back pain, a prevalent condition, the research topic rank lists of those on MTurk with back pain and those without were very similar, with agreement on 4 of the top 5 and 9 of the top 10 topics. However, we found nuanced differences in the ranked lists of research topics and the additional commentary.
4. Turkers in this canvassing: young, well-educated and frequent users
Amazon Mechanical Turk is a website where you can post short tasks and have workers quickly and easily perform these tasks for small sums of money. It is ideal for running short psychology experiments, since it allows large amounts of data to be collected quickly and easily. While a slightly less controlled environment than running studies in the lab, running studies on Turk has a number of advantages. In particular, it allows experimenters to 1 collect enough data to ensure their studies are not underpowered, as psychology studies chronically are; 2 collect data from a sample that is much more diverse than local college undergraduates and much more representative of the US at large; 3 easily replicate studies before publishing them to ensure that effects are real and effect sizes aren't inflated. I've run this tutorial twice and the videos for both times are presented here Northwestern, Harvard. The most updated version of these videos are from a tutorial given at Northwestern University. Northwestern generously had these professionally filmed and hosts them on their website so others can learn from them.
Generic web-based annotation tool for Mechanical Turk (v0.1)
The following topics describe information and additional resources you can use to implement a project using Amazon Mechanical Turk. If you didn't use the tutorial, you can learn how to complete basic and advanced Amazon Mechanical Turk tasks using the Amazon Mechanical Turk Developer Guide and by looking at code samples. When you wish to have a hands-on approach to using Amazon Mechanical Turk and have a relatively small number of assignments and results, the CLI is a good choice. When the number of assignments you have or the number of results you have is large, the Amazon Mechanical Turk API is a good choice. If you have a very large number of similar HITs, consider using the Requester user interface. It merges one question template with lots of question data to create many similar HITs. Creating a successful HIT involves more than programming. There is a certain art involved, for example, in pricing a HIT correctly, laying out the question correctly, breaking down the task into HITs, and minimizing the Worker's time spent with the HIT. For that reason, we created a best practices guide that gives detailed instructions about creating an effective HIT.
Image/Video Processing
Skip to search form Skip to main content Skip to account menu You are currently offline. Some features of the site may not work correctly. DOI: One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequently end up performing labor with little pay. In general, workers are provided with little information about tasks, and are left to rely on noisy signals, such as textual description of the task or rating of the requester.
How it Works
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TurkScanner: Predicting the Hourly Wage of Microtasks
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Amazon Mechanical Turk for LabelMe
SoPHIE's MTurk integration offers experimental researchers the huge potential of the most popular crowdsourcing platform. SoPHIE is the right experimental software if you would like to recruit subjects via Amazon Mechanical Turk and let them take part in human interaction experiments on the internet. Running experiments online is easy, fast and comes at low costs.
Conducting Survey Research Using MTurk
RELATED VIDEO: MTURK-How to work \u0026 Complete this Transcribe Job Amazon Mturk Get Paid-Demo Video In Tamil-Over the past 25 years, the Internet has progressively become part of how we live. It has changed the way in which we communicate and exchange knowledge. Crowdsourcing is a technical innovation that refers to the process of obtaining content by soliciting contributions from a large and diverse pool of people, particularly from online communities. Today, multiple crowdsourcing platforms are available, facilitating the link between researchers and populations of potential participants e. In this chapter, we offer a primer to researchers interested in using MTurk for survey research. First, we present an introduction to crowdsourcing for recruiting survey participants.
Login page for mturk india sign up is presented below. Log into mturk india sign up page with one-click or find related helpful links. Contact Us Remove Site. Mturk India Sign Up Login page for mturk india sign up is presented below. Last Updated: 17th September, The online market place for work. We give businesses and developers access to an on-demand scalable workforce.
A common use of jsPsych is to build an online experiment and find subjects using Mechanical Turk. Once an experiment is available through a web server and data is being saved on the server , connecting the experiment with Mechanical Turk takes only a few additional steps. The jsPsych. When potential subjects view your experiment on Mechanical Turk, they will be able to see a single webpage before deciding whether or not to accept the HIT start the experiment.
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