We examined the trade-off between the cost of response redundancy and the gain in output quality on the popular crowdsourcing platform Mechanical Turk, as a partial replication of Kosinski et al. (2012) who demonstrated a significant improvement in performance by aggregating multiple responses through majority vote. We submitted single items from a validated intelligence test as Human Intelligence Tasks (HITs) and aggregated the responses from “virtual groups” consisting of 1 to 24 workers. While the original study relied on resampling from a relatively small number of responses across a range of experimental conditions, we randomly and independently sampled from a large number of HITs, focusing only on the main effect of group size. We found that – on average – a group of six MTurkers has a collective IQ one standard deviation above the mean for the general population, thus demonstrating a “wisdom of the crowd” effect. The relationship between group size and collective IQ was characterised by diminishing returns, suggesting moderately sized groups provide the best return on investment. We also analysed performance of a smaller subset of workers who had each completed all 60 test items, allowing for a direct comparison between a group’s collective IQ and the individual IQ of its members. This demonstrated that randomly selected groups collectively equalled the performance of the best-performing individual within the group. Our findings support the idea that substantial intellectual capacity can be gained through crowdsourcing, contingent on moderate redundancy built into the task request.

We introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies (n=5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers (http://www.riskliteracy.org). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.

How do economists use graphs, and do they use them well? Using Amazon’s Mechanical Turk, I provide evidence to these questions by exploring more than 2600 graphs published in the first issue of the American Economic Review from 1911 to 2017. I find that economists use a lot of line charts – more than 80% of the total sample are line charts. I also find that the share of graphs that use data (as opposed to diagrams) fell over the first half of the century and then increased from about the early 1980s to today, correlated with perceived graph quality.

Mechanical Turk has become an important source of subjects for social science experiments, providing a low-cost alternative to the convenience of using undergraduates while avoiding the expense of drawing fully representative samples. However, we know little about how the rates we pay to “Turkers” for participating in social science experiments affects their participation. This study examines subject performance using two experiments – a short survey experiment and a longer dynamic process tracing study of political campaigns – that recruited Turkers at different rates of pay. Looking at demographics and using measures of attention, engagement and evaluation of the candidates, we find no effects of pay rates upon subject recruitment or participation. We conclude by discussing implications and ethical standards of pay.

Prior research demonstrates that responses to surveys can vary depending on the race, gender, or ethnicity of the investigator asking the question. We build upon this research by empirically testing how information about researcher identity in online surveys affects subject responses. We do so by conducting an experiment on Amazon’s Mechanical Turk in which we vary the name of the researcher in the advertisement for the experiment and on the informed consent page in order to cue different racial and gender identities. We fail to reject the null hypothesis that there is no difference in how respondents answer questions when assigned to a putatively black/white or male/female researcher.