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.

In this article, we examine whether cross-national studies disclose enough information for independent researchers to evaluate the validity and reliability of the findings (evaluation transparency) or to perform a direct replication (replicability transparency). The first contribution is theoretical. We develop a heuristic theoretical model including the actors, factors, and processes that influence the transparency of cross-national studies and provide an overview of the measures currently taken to improve research transparency. The second contribution is empirical, in which we analyze the level of transparency in published cross-national studies. Specifically, using a random sample of 305 comparative studies published in one of 29 peer-reviewed social sciences journals (from 1986 to 2016), we show that, even though all the articles include some methodological information, the great majority lack sufficient information for evaluation and replication. Lastly, we develop and propose a set of transparency guidelines tailored for reporting cross-national survey research.

For the first time, this study examined both cross-sectional and longitudinal effects of contextual cultural and economic characteristics of individual formal volunteering. A study sample of 116,380 respondents from 33 countries and four waves from the European Values Study (1981-2008) was used. The hierarchical logistic models indicate that a long-standing theoretical idea regarding the positive relationship between contextual religiosity and formal volunteering is not supported by European data. Specifically, I found that people living in secular and economically equal countries are more likely to engage in voluntary activities. Longitudinally, there is a decrease in formal volunteering over 27 years; however, none of the cultural and economic country-level variables explain variation across time. These differential cross-sectional and longitudinal effects highlight the need to use repeated cross-sectional data.

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.