Ivan Hernandez

Gender and ethnicity are increasingly studied topics within I-O psychology, helpful for understanding the composition of collectives, experiences of marginalized group members, and differences in outcomes between demographics and capturing diversity at higher levels. However, the absence of explicit, structured, demographic information online makes applying these research questions to Big Data sources challenging. We highlight how deep neural networks can be used to infer demographics based on people's names, which are commonly found online (e.g., social media profiles, employee pages, and membership rosters), using broad international data to train and evaluate the effectiveness of these models and find that validity coefficients meet minimum reliability thresholds at the individual level ( r gender = .91, r ethnicity = .80) highlighting their ability to contextualize and facilitate Big Data research. Using empirical data extracted from databases, websites, and mobile apps, we highlight how these models can be applied to large organizational data sets by presenting illustrative demonstrations of research questions that incorporate the information provided by the model. To promote broader usage, we offer an online application to infer demographics from names without requiring advanced programming knowledge.

Four studies investigate a fear of imbalanced minds hypothesis that threatening agents perceived to be relatively mismatched in capacities for cognition (e.g., self-control and reasoning) and emotion (e.g., sensations and emotions) will be rated as scarier and more dangerous by observers. In ratings of fictional monsters (e.g., zombies and vampires), agents seen as more imbalanced between capacities for cognition and emotion (high cognition–low emotion or low cognition–high emotion) were rated as scarier compared to those with equally matched levels of cognition and emotion (Studies 1 and 2). Similar effects were observed using ratings of scary animals (e.g., tigers, sharks; Studies 2 and 3), and infected humans (Study 4). Moreover, these effects are explained through diminished perceived control/predictability over the target agent. These findings highlight the role of balance between cognition and emotion in appraisal of threatening agents, in part because those agents are seen as more chaotic and uncontrollable.