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The emergence of COVID-19 has changed the state of psychological science: The foreseeable future of how psychologists will conduct research, and on what topics, is unclear. The current article provides a comprehensive perspective on how social psychologists can optimize their research in the wake of the COVID-19 pandemic. Specifically, we (a) highlight psychological phenomena that are most likely to change during the wake of COVID-19; (b) discuss theoretical, methodological, and practical considerations of conducting research on these phenomena; and (c) outline promising directions for key programs of research. We conclude with meta-scientific considerations and critical evaluations of scientific reproducibility. By many projections, society will remain altered by the COVID-19 pandemic permanently, such that a “new normal” will emerge with regards to social norms and perceptions underlying the basic processes psychologists have studied for decades. Such shifts can, in turn, blur the lines between findings that denote failures to replicate and findings that reflect previously absent environmental influences on cognition and behavior. These considerations will be important for how psychologists evaluate theory, consider social-contextual boundary conditions of effects, and conceptualize what it means for science to be truly reproducible.

Supplemental material, Grosz_Supplemental_Material for The Taboo Against Explicit Causal Inference in Nonexperimental Psychology by Michael P. Grosz, Julia M. Rohrer and Felix Thoemmes in Perspectives on Psychological Science

Supplemental material, Ratner_Open_Practices_Disclosure for Depression and Derailment: A Cyclical Model of Mental Illness and Perceived Identity Change by Kaylin Ratner, Jane Mendle, Anthony L. Burrow and Felix Thoemmes in Clinical Psychological Science

Propensity score matching is a tool for causal inference in non-randomized studies that allows for conditioning on large sets of covariates. The use of propensity scores in the social sciences is currently experiencing a tremendous increase; however it is far from a commonly used tool. One impediment towards a more wide-spread use of propensity score methods is the reliance on specialized software, because many social scientists still use SPSS as their main analysis tool. The current paper presents an implementation of various propensity score matching methods in SPSS. Specifically the presented SPSS custom dialog allows researchers to specify propensity score methods using the familiar point-and-click interface. The software allows estimation of the propensity score using logistic regression and specifying nearest-neighbor matching with many options, e.g., calipers, region of common support, matching with and without replacement, and matching one to many units. Detailed balance statistics and graphs are produced by the program., Comment: 30 pages, 6 figures