Social cohesion predicts COVID-19 vaccination intentions and uptake

COVID-19 vaccination is widely regarded as an individual decision, resting upon individual characteristics and demographic factors. In this research, we provide evidence that psychological group membership, and more precisely, social cohesion—a multidimensional concept that encompasses one's sense of connectedness to, and interrelations within, a group—can help us understand COVID-19 vaccination intentions (Study 1) and uptake (Study 2). Study 1 is a repeated-measures study with a representative sample of 3026 Australians. We found evidence that social cohesion can be conceptualised as a multidimensional structure; moreover, social cohesion at Wave 1 (early in the COVID-19) predicted greater vaccination intention and lower perceived risk of vaccination at Wave 2 (4 months later). In Study 2 (a cross-sectional study, N = 499), the multidimensional structure of social cohesion was associated with greater uptake of vaccine doses (in addition to willingness to receive further doses and perceived risk of the vaccine). These relations were found after controlling for a series of demographic (i.e., sex, age, income), health-related factors (i.e., subjective health; perceived risk; having been diagnosed with COVID-19), and individual differences (political orientation, social dominance orientation, individualism). These results demonstrate the need to go beyond individual factors when it comes to behaviours that protect groups, and particularly when examining COVID-19 vaccination—one of the most important ways of slowing the spread of the virus.     

The culture of perceptual expertise and the other-race effect

In our commentary, we propose that the ORE can be viewed as a form of perceptual expertise. Like experts, we recognize own-race faces at the subordinate level as individuals and novices when recognize other-race faces at the basic level of race. Applying a perceptual expertise account, we explain the ORE in terms of its cognitive, neural, and motivational factors. We suggest that by creating a culture of “other-race” expertise, improvements in other-race face recognition can be achieved.     

Factor analysis by generalized least squares

Aitken's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are seale free and asymptotically efficient. The estimates are computed by a rapidly converging Newton-Raphson procedure. A new technique is used to deal with Heywood cases effectively.The work on this project was done when the first author was Research Statistician at Educational Testing Service, Princeton, N. J. The second author was in part supported by a grant from the Research Committee of the University of Wisconsin Graduate School. The authors wish to thank Michael Browne for many helpful comments and Marielle van Thillo for valuable assistance in the numerical computations.