People of different ages vary greatly in their beliefs and behaviors. For example, a recent Pew report outlines wide generational gaps in people’s opinions on several political issues like presidential job approval, perceptions of racism, views on immigration, and political ideology (Pew Research Center, 2018). Furthermore, some issues, like the use of Medicare, depend on age and therefore are more relevant to older adults than younger ones.
At CloudResearch, we advocate for requesters to treat workers fairly when posting HITs on Amazon’s Mechanical Turk (MTurk). Workers are, after all, the people who make the research possible. Sometimes situations arise in which an MTurk worker is unable to receive payment, despite having completed a survey. Below are two common scenarios in which a worker may not be paid, despite completing a survey:
Studying pairs of people (e.g., married couples, friends, coworkers, etc) is becoming increasingly common in the social and behavioral sciences. Online participant populations, such as Mechanical Turk and other online panels, can potentially serve as a rich source of dyadic...
About a month ago, we published After the Bot Scare blog on workers providing bad quality data on Amazon’s Mechanical Turk. This month, we followed up with our “farmers” to assess the effectiveness of the tools we created to deal with the problem. In this blog, we present data from our follow-up study and evidence to suggest our tools are working.
A Case Study From a Recent JESP Article A new study appearing in the Journal of Experimental Social Psychology suggests Americans strongly believe in economic mobility because they fail to appreciate how vast wealth inequality really is. In this blog,...
By now, even casual users of MTurk have heard about recent concerns of “bots” or low quality data. We’ve written about the topic here and laid out evidence that suggests “bots” are actually foreign workers using tools to obscure their true location (here). Perhaps most importantly, we’ve created two tools to help keep these workers out of your studies. In this blog, we introduce a third tool: the Universal Exclude List.
