Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The urban desirability paradox: U.K. urban-rural differences in well-being, social satisfaction, and economic satisfaction.

As the majority of the global population resides in cities, it is imperative to understand urban well-being. While cities offer concentrated social and economic opportunities, the question arises whether these benefits translate to equitable levels of satisfaction in these domains. Using a robust and objective measure of urbanicity on a sample of 156,000 U.K. residents aged 40 and up, we find that urban living is associated with lower scores across seven dimensions of well-being, social satisfaction, and economic satisfaction. In addition, these scores exhibit greater variability within urban areas, revealing increased inequality. Last, we identify optimal distances in the hinterlands of cities with the highest satisfaction and the least variation. Our findings raise concern for the psychological well-being of urban residents and show the importance of nonlinear methods in urban research.

COVID-19 and mental health among at-risk university students: A prospective study into risk and protective factors.

OBJECTIVE: The COVID-19 pandemic has confronted young adults with an unprecedented mental health challenge. Yet, prospective studies examining protective factors are limited. METHODS: In the present study, we focused on changes in mental health in a large sample (N = 685) of at-risk university students, which were measured before and during the pandemic. Network modeling was applied to 20 measured variables to explore intercorrelations between mental health factors, and to identify risk and protective factors. Latent change score modeling was used on a subset of variables. RESULTS: The main findings indicate that (1) mental health problems increased at group level, especially depression-anxiety and loneliness; (2) emotional support during the COVID pandemic was associated with smaller increases in loneliness and depression-anxiety; (3) COVID-related stress predicted increases in depression-anxiety; (4) loneliness acted as a bridge construct between emotional support and changes in mental health. CONCLUSION: To mitigate the impact of the COVID-19 pandemic on the mental health of young adults, is it recommended to focus on interventions that strengthen internal resources (stress-regulating abilities) and reduce loneliness.