What’s in a face? The role of facial features in ratings of dominance, threat, and stereotypicality

Faces judged as stereotypically Black are perceived negatively relative to less stereotypical faces. In this experiment, artificial faces were constructed to examine the effects of nose width, lip fullness, and skin reflectance, as well as to study the relations among perceived dominance, threat, and Black stereotypicality. Using a multilevel structural equation model to isolate contributions of the facial features and the participant demographics, results showed that stereotypicality was related to wide nose, darker reflectance, and to a lesser extent full lips; threat was associated with wide nose, thin lips, and low reflectance; dominance was mainly related to nose width. Facial features explained variance among faces, suggesting that face-type bias in this sample was related to specific face features rather than particular characteristics of the participant. People’s perceptions of relations across these traits may underpin some of the sociocultural disparities in treatment of certain individuals by the legal system.

Extending the Group Actor-Partner Interdependence Model: MSEM Applications

The Group Actor-Partner Interdependence Model (GAPIM) conceptualizes group composition as a relational construct and provides methods for estimating the effects of compositional characteristics on outcomes of interest. This paper extends the GAPIM to a multilevel structural equation model framework, which expands the range of research questions the GAPIM might address, including those based on input-process-outcome models. Simulations, based on group size, number of groups, effect size, and compositional skewness, provide guidance for designing studies to maximize power to detect compositional effects. Discussion addresses composition in general, especially how “deep” characteristics become manifest and meaningful during interaction.

One Factor SEM and Multilevel SEM Model for Patient Satisfaction Data

Structural equation models are very common in medical, social, management and behavioral sciences where researchers established some causal relations between observed variables and latent variable. In structured populations the assumption of independence of observations is often violated and had been ignored by the researchers. As a result with the correlated structure of the error terms, biased estimates of the parameters have been produced that leads towards incorrect statistical inference. Multilevel structural equation model under one factor model has been proposed, estimated and compared with the traditional structural equation model on patient satisfaction data. Multilevel structural equation model produced better estimates than the structural equation models.

Rest, Zest, and My Innovative Best: Sleep and Mood as Drivers of Entrepreneurs’ Innovative Behavior

This study investigates the antecedents of an entrepreneur’s day-level innovative behavior. Drawing on 2,420 data points from a 10-day experience sampling study with 121 entrepreneurs, we find that sleep quality is a precursor to an entrepreneur’s subsequent innovative behavior, in accordance with the effort-recovery model. Moreover, sleep quality is positively related to high-activation positive moods (e.g., enthusiastic, inspired) and negatively related to high-activation negative moods (e.g., tension, anxiety). Our multilevel structural equation model indicates that high-activation positive moods mediate the relationship between sleep quality and innovative behavior on a given day. These results are relevant for managing entrepreneurial performance.