This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Migrant selectivity refers to the idea that immigrants differ in certain characteristics from individuals who stay behind in their country of origin. In this article, we describe the selectivity profiles of recent migrants to Germany with respect to educational attainment, age and sex. We illustrate how refugees differ from labour migrants, and we compare the profiles of Syrian refugees who successfully completed the long journey to Europe to Syrian refugees who settled in neighbouring Lebanon or Jordan. We rely on destination-country data from the IAB-BAMF-GSOEP Survey of Refugees, the Arab Barometer, and the German Microcensus, as well as on a broad range of origin-country data sources. Regarding sex selectivity, males dominate among refugees in Germany, while among economic migrants, sex distributions are more balanced. Relative to their societies of origin, labour migrants are younger than refugees. At the same time, both types of migrants are drawn from the younger segments of their origin populations. In terms of educational attainment, many refugees compare rather poorly with average Germans’ attainment, but well when compared to their origin populations. The educational profiles for labour migrants are mixed. Finally, Syrians who settle in Germany are younger, more likely to be male and relatively better educated than Syrians migrating to Jordan or Lebanon.

This article depicts the selectivity profiles of first-generation immigrants of multiple origins in 18 European destinations and investigates whether educational selectivity is relevant to their labour market performance. The theoretical account starts from the premise that the relative position individuals occupy in the educational distribution of their origin country represents—frequently unmeasured—characteristics such as motivation, skills, and resources, which matter for immigrants’ incorporation into the labour market in their destination countries. The empirical analyses are based on data from the European Social Survey for the destination countries, and from the Barro–Lee Educational Attainment Dataset for the origin countries. The findings reveal that immigrants are mostly positively selected with regard to their educational attainment. At the same time, they point to considerable variation in the degree of selectivity across migrants from different regions of the world, as well as across different destinations. Results of linear multilevel regression models of occupational status indicate that over and above the absolute level of educational attainment, first-generation immigrants profit from a favourable position in the educational distribution of their origin country. Conversely, there are indications that selectivity is negatively associated with the likelihood of being employed.

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.