Rethinking current models in social psychology: A Bayesian framework to understand dramatic social change

Before/after Bayes: A comparison of frequentist and Bayesian mixed-effects models in applied psychological research

Bayesian methods are becoming increasingly used in applied psychological research. Previous researchers have thoroughly written about much of the details already, including the philosophy underlying Bayesian methods, computational issues associated with Bayesian model estimation, Bayesian model development and summary, and the role of Bayesian methods in the so-called replication crisis. In this paper, we seek to provide case studies comparing the use of frequentist methods to the use of Bayesian methods in applied psychological research. These case studies are intended to 'illustrate by example' the ways that Bayesian modelling differs from frequentist modelling and the differing conclusions that one may arrive at using the two methods. The intended audience is applied psychological researchers who have been trained in the traditional frequentist framework, who are familiar with mixed-effects models and who are curious about how statistical results might look in a Bayesian context. Along with our case studies, we provide general opinions and guidance on the use of Bayesian methods in applied psychological research.

United We Stand, Divided We Fall: Sociopolitical Predictors of Physical Distancing and Hand Hygiene During the COVID-19 Pandemic

Most health models emphasize individual factors in predicting health behavior. However, in the context of COVID-19 where the immediate response to stopping the spread of the virus requires collective efforts and change, other sociopolitical factors need to be considered. Prior research points to health behaviors being impacted by neighborhood and national social relations, social identification, confidence in government and political orientation. This research, though, is generally piecemeal (or specific), tends to be cross-sectional, and is usually not oriented to pandemics. These issues are addressed in the current research. A two-wave study with a representative sample of Australians (N _{Wave 1} = 3028) gathered during COVID-19 examined sociopolitical factors at the local and national level as predictors of health behaviors one month later. Four models were tested. These encapsulated geographic levels (local or national) and two health behaviors (hand hygiene or physical distance). In the three of the four models, social identification was a significant predictor of health behavior, while controlling for sociodemographic and individual-level measures. There were more mixed results for social relations and confidence in government. There is evidence that to better promote health behaviors sociopolitical factors need to be more prominent in public policy and health behavior models.

A social change framework for addressing collective action: introducing collective inertia

We argue that dramatic social change arising from collective action does not represent the worst-case scenario for group members. Specifically, we introduce the concept of collective inertia: a societal state where group members face a macro system devoid of clearly articulated collective goals and values coupled with dysfunctional social and normative structures. Our analysis emphasizes the dynamic process of social change, involving a shift from one societal state to another. Collective inertia is the one societal state that may have devastating long-term consequences for groups.

Power dynamics in intergroup relations

Power and intergroup relations are complex, multilevel, and dynamic. Using Power Basis Theory, we explain our criteria for deciding whether theory or research addresses intergroup power dynamics: it must (a) address power and not authority or other topics, (b) involve attempted or real change regarding groups and power, or the prevention of change, (c) involve protracted interactions among multiple actors through more than one channel, (d) involve more than one level of social organization (e.g. person, group, superordinate group). We organize our 10-year review by these criteria. Research meeting all our criteria is rare. We explain relevant new theory and new research tools, including multi-level modelling, multi-player games, agent-based models, big data, and machine-learning, that can help fill the gap.