What is Recklessness in Scientific Research? The Frank Sauer Case

On May 22, 2017, administrative law Judge Leslie Rogall of the Department of Health and Human Services’ Departmental Appeals Board, Civil Remedies Division, ruled in favor of the Office of Research Integrity (ORI) concerning its decision to charge former University of California at Riverside biochemistry professor Frank Sauer with research misconduct for fabricating or falsifying digital image data included in three papers and seven grant applications submitted to the National Institutes of Health. More specifically, Sauer was deemed responsible for manipulating, reusing, and falsely labeling images of autoradiograms and gels in his research in epigenetics. One month after this decision, ORI announced its final ruling concerning Sauer, which barred him from serving in any advisory capacity to the Public Health Services and required him to retract affected papers. The case raises some interesting and important questions concerning research integrity because it focused on the legal issue of what constitutes recklessness in scientific research.     

Climate,agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity.     

Choosing the best (wo)man for the job: The effects of mortality salience,sex, and gender stereotypes on leader evaluations

The present research investigated the impact of death-related thoughts on preferences for male and female leaders and tested alternative predictions derived from terror management theory: the stereotype bias effect was predicted to result in a global preference for male leaders (Study1) and a preference for agentic leaders (Study 2), whereas the ingroup bias effect was predicted to result in women favoring female leaders and men preferring male leaders. These hypotheses were examined in two experimental studies wherein participants were presented with a mortality salience or control manipulation and subsequently presented with campaign statements from male and female gubernatorial candidates. Results from Study 1 were in accord with the enhanced ingroup bias predictions: under mortality salience women preferred and voted for the female candidate more so than the male candidate, while men showed the reverse preference. Results from Study 2, which also manipulated gender stereotypic traits of the candidates, support the enhanced stereotype bias effect: under mortality salience female participants preferred and voted for the agentic candidate regardless of sex, and males preferred the agentic male candidate. Results support previous TMT research demonstrating both ingroup bias and stereotype bias processes.     

The impact of blatant stereotype activation and group sex-composition on female leaders

The individual and combined impact of blatant stereotype activation and solo status or mixed-sex groups on the self-appraisals, performance, and anxiety of female leaders was examined across three laboratory studies. The first study utilized a two-condition, two-stage design in which female leaders were exposed to a blatant stereotype threat or control condition after which they completed a leadership task. In the second stage, the threatened leaders received a solo status manipulation (leading a group of men) while the control condition did not. In the second study a 2 (blatant threat, no blatant threat) by 2 (solo status, all-female group) fully factorial design was used to test the hypotheses. Finally, in Study 3, a similar factorial design was used with a mixed-sex, rather than solo, condition. Across the studies it was hypothesized and found that receiving a single stereotype threat would result in a positive, stereotype reactance, response. However, when both threats were combined a stereotype vulnerability response was elicited, as expected. Theoretical and practical implications are discussed.