Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Author guidelines for journals could help to promote transparency, openness, and reproducibility

Survey experiments have become a central methodology across the social sciences. Researchers can combine experiments’ causal power with the generalizability of population-based samples. Yet, due to the expense of population-based samples, much research relies on convenience samples (e.g. students, online opt-in samples). The emergence of affordable, but non-representative online samples has reinvigorated debates about the external validity of experiments. We conduct two studies of how experimental treatment effects obtained from convenience samples compare to effects produced by population samples. In Study 1, we compare effect estimates from four different types of convenience samples and a population-based sample. In Study 2, we analyze treatment effects obtained from 20 experiments implemented on a population-based sample and Amazon's Mechanical Turk (MTurk). The results reveal considerable similarity between many treatment effects obtained from convenience and nationally representative population-based samples. While the results thus bolster confidence in the utility of convenience samples, we conclude with guidance for the use of a multitude of samples for advancing scientific knowledge.

One of the most notable recent developments in survey research is the increased usage of online convenience samples drawn from Amazon's Mechanical Turk (MTurk). While scholars have noted various social and political differences (e.g., age, partisanship) between MTurk and population-based samples, the breadth and depth of these variations remain unclear. We investigate the extent to which MTurk samples differ from population samples, and the underlying nature of these differences. We do so by replicating items from the population-based American National Election Studies (ANES) 2012 Time Series Study in a survey administered to a sample of MTurk respondents. With few exceptions, we not only find that MTurk respondents differ significantly from respondents completing the 2012 ANES via the Web but also that most differences are reduced considerably when controlling for easily measurable sample features. Thus, MTurk respondents do not appear to differ fundamentally from population-based respondents in unmeasurable ways. This suggests that MTurk data can be used to advance research programs, particularly if researchers measure and account for a range of political and demographic variables as needed.

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