ABSTRACT
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.
Subject(s)
Data Analysis , Research Personnel , Humans , Uncertainty , Reproducibility of ResultsABSTRACT
This study explores how gender and age interact in shaping beliefs about fair pay through a factorial survey experiment conducted with German employees. Respondents evaluated hypothetical worker descriptions varying in age, gender, and earnings. While no gender gap in fair earnings was found for the youngest hypothetical workers, a significant gap favoring men emerged with increasing age. This suggests that male workers receive a higher age premium on fair earnings than female workers. The findings highlight the need to understand how gender interacts with other characteristics to legitimize workplace inequalities.
ABSTRACT
Justice evaluations are proposed to provide a link between the objective level of inequality and the consequences at the individual and societal level. Available instruments, however, focus on the subjective perception of inequality and income distributions. In light of findings that subjective perceptions of inequality and income levels can be biased and subject to method effects, we present the newly developed Justice Evaluation of the Income Distribution (JEID) Scale, which captures justice evaluations of the actual earnings distribution. JEID comprises five items that provide respondents with earnings information for five groups at different segments along the distribution of earnings in a given country. We provide a German-language and an English-language version of the scale. The German-language version was developed and validated based on three comprehensive heterogeneous quota samples from Germany; the translated English-language version was validated in one comprehensive heterogeneous quota sample from the UK. Using latent profile analysis and k-means clustering, we identified three typical response patterns, which we labeled "inequality averse," "bottom-inequality averse," and "status quo justification." JEID was found to be related to normative orientations in the sense that egalitarian views were associated with stronger injustice evaluations at the bottom and top ends of the earnings distribution. With a completion time of between 1.50 and 2.75 min, the JEID scale can be applied in any self-report survey in the social sciences to investigate the distribution, precursors, and consequences of individuals' subjective evaluations of objective differences in earnings.