Are MTurk workers who they say they are?

The internet has the reputation of being a place where people can hide in anonymity, and present as being very different people than who they actually are. Is this a problem on Mechanical Turk? Is the self-reported information provided by Mechanical Turk workers reliable? These are important questions which have been addressed with several different methods. Researchers have examined a) consistency of responding to the same questions over time and across studies b) the validity of responses, or the degree to which the items capture responses that represent the truth from participants. It turns out that there are certain situations in which MTurk workers are likely to lie, but they are who they say they are in almost all cases.

Consistency over time/Reliability:

One way of measuring truthfulness of responses is to examine how individual workers respond at different times to the same questions. In one study, data collected from over 80,000 MTurk workers examined the reliability in reported demographic information over time. This large study found that participants were overwhelmingly consistent when reporting demographic variables across different studies over time, with gender identification being 98.9% consistent, race 98.2% consistent, and birth year being 96.2% consistent, with this slightly lower score being largely due to technical issues rather than Turkers not being truthful (Rosenzweig, Robinson, & Litman, 2017).

Validity

Various forms of validity have been examined in data collected through MTurk, with results showing that data are by and large valid. We will focus on convergent validity, which refers to how a measure is correlated with other measures of known related constructs. Convergent validity of self-reported information at a group level can  be established by examining whether workers are providing logically consistent information. Data collected on TurkPrime show that associations between variables are consistent with what is found in the general population. For example, older Mechanical Turk workers tend to be more religious and more conservative, a pattern that is consistent with the general US population. The reported number of children correlates strongly with age and family status, as do divorce rates.  Self-reported time of day preference is correlated with the time of day that workers are actually active, which is also correlated with a cluster of clinical, personality, and behavioral variables that have been previously reported in the literature in studies of the general population (Unpublished Data). Similar consistent patterns have been observed in health information collected from Mechanical Turk workers. TurkPrime profiled over 10,000 Mechanical Turk workers on over 50 questions relating to physical health, with a factor analysis revealing that symptoms clustered around underlying conditions in the expected way. For example hypertension, high cholesterol, and diabetes formed a single factor. This factor, interpreted to be metabolic syndrome, correlated with other variables such as age and gender in the expected way. The rate of metabolic syndrome increases with age and was higher among men. BMI also correlated with self-reported exercise (See also Litman et al., 2015). Some other examples include the fact that rates of chronic illnesses are significantly higher among smokers compared to non-smokers, and strongly associated with BMI, with both higher and lower than average BMI being predictive of chronic illnesses.

Video tools that are currently in beta testing at TurkPrime are starting to be used to verify participants’ reported demographic characteristics such as gender, and race, and the presence of a second person for dyadic research, with promising initial results indicating that participants are highly truthful.

When Participants are Likely to Lie

Research has additionally examined the reliability of data collected when selection criteria were listed as a prerequisite to enter a study (e.g.“only open to males”). Data show that when participants are incentivized to not be truthful, such as when they are only able to take a lucrative study if they identify as a particular demographic group, they lie (Chandler & Paolacci, 2017; Rosenzweig et al., 2017). For example, in a study with a HIT title that said it was open “for men only”, 44% of participants who entered had previously consistently reported their gender as “female”.

Best Practices

When researchers want to selectively recruit participants on MTurk, they have several options. Some researchers recruit for a specific demographic group by including such specifications/selection criteria in a study open to all workers, and rely on workers to tell the truth when opting in to such a study. Based on the data, this is a mistake that will lead untruthful participants to opt-in. There are, however, several ways to selectively recruit participants that are who they say they are. One option is to use the qualifications system with characteristics already verified by MTurk, or by using TurkPrime’s qualification system. Another option is to run a study open to all workers, ask a series of initial demographic questions, and only have participants who match the desired demographic criteria proceed to the next round in the study, paying even those who were of the wrong demographic for their time. You can create worker groups on TurkPrime based on such pre-screenings which can help you track and subsequently recruit participants who match your criteria of interest.

References:

Chandler, J. J., & Paolacci, G. (2017). Lie for a Dime: When Most Prescreening Responses Are Honest but Most Study Participants Are Impostors. Social Psychological and Personality Science, 8(5), 500-508.

Litman, L., Rosen, Z., Spierer, D., Weinberger-Litman, S., Goldschein, A., & Robinson, J. (2015). Mobile exercise apps and increased leisure time exercise activity: a moderated mediation analysis of the role of self-efficacy and barriers. Journal of medical Internet research, 17(8).

Rosenzweig, C., Robinson, J., & Litman, L. (2017, January). Are They Who They Say They Are?: Reliability and Validity of Web-Based Participants’ Self-Reported Demographic Information. Poster presented at the 18th Society for Personality and Social Psychology Annual Convention, San Antonio, TX.

Strengths and Limitations of Mechanical Turk

Hundreds of academic papers are published each year using data collected through Mechanical Turk. Researchers have gravitated to Mechanical Turk primarily because it provides high quality data quickly and affordably. However, Mechanical Turk has strengths and weaknesses as a platform for data collection. While Mechanical Turk has revolutionized data collection, it is by no means a perfect platform. Some of the major strengths and limitations of MTurk are summarized below.

Strengths

A source of quick and affordable data

Thousands of participants are looking for tasks on Mechanical Turk throughout the day, and can take your task with the click of a button. You can run a 10 minute survey with 100 participants for $1 each, and have all your data within the hour.

Data is reliable

Researchers have examined data quality on MTurk and have found that by and large, data are reliable, with participants performing on tasks in ways similar to more traditional samples. There is a useful reputation mechanism on MTurk, in which researchers can approve or reject the performance of workers on a given study. The reputation of each worker is based on the number of times their work was approved or rejected. Many researchers use a standard practice that relies on only using data from workers who have a 95% approval rating, thereby further ensuring high-quality data collection.

Participant pool is more representative compared to traditional subject pools

Traditional subject pools used in social science research are often samples that are convenient for researchers to obtain, such as undergraduates at a local university. Mechanical Turk has been shown to be more diverse, with participants who are closer to the U.S. population in terms of gender, age, race, education, and employment.

Limitations

There are two kinds of potential limitations on MTurk, technical limitations, and more fundamental limitations with the platform. Many of the technical limitations of MTurk have been resolved through scripts written by researchers or platforms such as TurkPrime, which help researchers do things they were not previously able to do on MTurk including

• Exclude participants from a study based on participation in a previous study
• Conduct longitudinal research
• Make sure larger studies do not stall out after the first 500 to 1000 Workers
• Communicate with many Workers at a time.

There are however several more fundamental limitations to data collection on MTurk:

Small population

There are about 100,000 Mechanical Turk workers who participate in academic studies each year. In any one month about 25,000 unique Mechanical Turk workers participate in online studies. These 25,000 workers participate in close to 600,000 monthly assignments. The more active workers complete hundreds of studies each month. The natural consequence of a small worker  population is that participants are continuously recycled across research labs. This creates a problem of ‘non-naivete’. Most participants on Mechanical Turk have been exposed to common experimental manipulations and this can affect their performance. Although the effects of this exposure have not been fully examined, recent research indicates that this may be impacting effect sizes of experimental manipulations, comprising data quality and the effectiveness of experimental manipulations.

Diversity

Although Mechanical Turk workers are significantly more diverse than the undergraduate subject pool, the Mechanical Turk population is significantly less diverse than the general US population. The population of MTurk workers is  significantly less politically diverse, more highly educated, younger, and less religious compared to the US population. This can complicate the way that data can be interpreted to be reliable on a population level.

Limited selective recruitment

Mechanical Turk has basic mechanisms to selectively recruit workers who have already been profiled. To accomplish this goal Mechanical Turk conducts  profiling HITs that are continuously available for workers.  However, Mechanical Turk is structured in such a way that it is much more difficult to recruit people based on characteristics that have not been profiled. For this reason while rudimentary selective recruitment mechanisms exist there are significant limitations on the ability to recruit specific segments of workers.

Solutions

TurkPrime offers researchers more specific selective recruitment opportunities, and has some features in development to help researchers target participants who are less active and therefore more naive to common experimental manipulations and survey measures. TurkPrime also offers access to PrimePanels, which has access to over 10 million participants, who can be selectively recruited, and are more diverse.

References:

Chandler, J., Mueller, P., & Paolacci, G. (2014). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior research methods, 46(1), 112-130.

Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26(3), 213-224.

Litman, L., Robinson, J., & Abberbock, T. (2017). TurkPrime. com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior research methods, 49(2), 433-442.

Litman, L., Robinson, J., & Rosenzweig, C. (2015). The relationship between motivation, monetary compensation, and data quality among US-and India-based workers on Mechanical Turk. Behavior research methods, 47(2), 519-528.

Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184-188.

Peer, E., Vosgerau, J., & Acquisti, A. (2014). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior research methods, 46(4), 1023-1031.

Reusable Include and Exclude Groups

Problem: You are running a longitudinal study and have identified 1000 workers who you want to allow to take your second phase studies. How do you easily group those workers for easy access. 

Or you want to exclude certain workers from taking a number of your studies and wish to group them for easy exclusion in future studies. How can you do that?

Solution: Use the TurkPrime Worker Groups feature available in the Manage Workers menu. This allows you to create reusable worker groups for either inclusion or inclusion in studies. You simply select whether this group is an include or exclude group, give it a clear name and specify the workers in the group. 

Workers will not know why they were targeted for inclusion or exclusion since the MTurk Qualification name used is a random sequence of characters. The TurkPrime Group Name will not be visible to Workers. This is important so that you can run blind studies (i.e. the workers who are specified for inclusion will know they were targeted because they are exhibiting a certain personality, for example)

When you design your study, and specify Worker requirements, add this group to the worker requirements and all workers in that group will be wither included or excluded, as needed.

Creating Mechanical Turk Custom Panels with TurkPrime.com Worker Groups

Problem: Suppose you need to run a group of HITs open only to participants who are women under 50. You previously ran a HIT and know the Worker IDs that you want to reach, but have no way to email them and limit your survey to only them. How can you proceed?



Solution: TurkPrime.com Worker Groups and Worker Emails

1. TurkPrime recently added a new feature called Worker Groups which allows any MTurk Requester to create a Reusable Worker Group based on MTurk Worker's Worker ID.