In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.

This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process.

The paper reports findings from a crowdsourced replication. Eighty-four replicator teams attempted to verify results reported in an original study by running the same models with the same data. The replication involved an experimental condition. A “transparent” group received the original study and code, and an “opaque” group received the same underlying study but with only a methods section and description of the regression coefficients without size or significance, and no code. The transparent group mostly verified the original study (95.5%), while the opaque group had less success (89.4%). Qualitative investigation of the replicators’ workflows reveals many causes of non-verification. Two categories of these causes are hypothesized, routine and non-routine. After correcting non-routine errors in the research process to ensure that the results reflect a level of quality that should be present in ‘real-world’ research, the rate of verification was 96.1% in the transparent group and 92.4% in the opaque group. Two conclusions follow: (1) Although high, the verification rate suggests that it would take a minimum of three replicators per study to achieve replication reliability of at least 95% confidence assuming ecological validity in this controlled setting, and (2) like any type of scientific research, replication is prone to errors that derive from routine and undeliberate actions in the research process. The latter suggests that idiosyncratic researcher variability might provide a key to understanding part of the “reliability crisis” in social and behavioral science and is a reminder of the importance of transparent and well documented workflows.

How does noise generated by researcher decisions undermine the credibility of science? We test this by observing all decisions made among 73 research teams as they independently conduct studies on the same hypothesis with identical starting data. We find excessive variation of outcomes. When combined, the 107 observed research decisions taken across teams explained at most 2.6% of the total variance in effect sizes and 10% of the deviance in subjective conclusions. Expertise, prior beliefs and attitudes of the researchers explain even less. Each model deployed to test the hypothesis was unique, which highlights a vast universe of research design variability that is normally hidden from view and suggests humility when presenting and interpreting scientific findings.