This article was originally submitted for publication to the Editor of Advances in Methods and Practices in Psychological Science (AMPPS) in 2015. When the submitted manuscript was subsequently posted online (Silberzahn et al., 2015), it received some media attention, and two of the authors were invited to write a brief commentary in Nature advocating for greater crowdsourcing of data analysis by scientists. This commentary, arguing that crowdsourced research "can balance discussions, validate findings and better inform policy" (Silberzahn & Uhlmann, 2015, p. 189), included a new figure that displayed the analytic teams' effectsize estimates and cited the submitted manuscript as the source of the findings, with a link to the preprint. However, the authors forgot to add a citation of the Nature commentary to the final published version of the AMPPS article or to note that the main findings had been previously publicized via the commentary, the online preprint, research presentations at conferences and universities, and media reports by other people. The authors regret the oversight.
As the largest immigration policy in 25 years, Deferred Action for Childhood Arrivals (DACA) made deportation relief and work authorization available to 1.7 million unauthorized immigrants. This paper looks at how DACA affects DACA-eligible immigrants' labor market outcomes. I use a difference-indifferences design for unauthorized immigrants near the criteria cutoffs for DACA eligibility. I find DACA increases the likelihood of working by increasing labor force participation and decreasing the unemployment rate for DACA-eligible immigrants. I also find DACA increases the income of unauthorized immigrants in the bottom of the income distribution. I find little evidence that DACA affects the likelihood of attending school. Using these estimates, DACA moved 50,000 to 75,000 unauthorized immigrants into employment. If the effects of Deferred Action for Parents of Americans and Lawful Permanent Residents (DAPA) are similar to DACA, then DAPA could potentially move over 250,000 unauthorized immigrants into employment.
In the United States, over 400,000 individuals are in jail daily waiting for their criminal cases to be resolved. The majority of detainees are held because they cannot post bail. We estimate the impact of being detained pretrial on the likelihood of being convicted and sentence length using data on nearly a million criminal cases in New York City. Causal effects are identified using variation across arraignment judges in their propensities to detain defendants. We find that being detained increases the probability of conviction by 13 percentage points for felony defendants. Although pretrial detention lowers the probability of rearrest while cases are being adjudicated, this reduction in criminal activity is mostly offset by an increase in recidivism within 2 years after disposition. Higher pretrial detention rates among minority defendants explain 40 percent of the black-white gap in rates of being sentenced to prison and 28 percent of the Hispanic-white gap. Excessive bail shall not be required, nor excessive fines imposed, nor cruel and unusual punishments inflicted. (US Constitution, Eighth Amendment)
Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 different analyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results.
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