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The COVID-19 pandemic has extensively changed the state of psychological science from what research questions psychologists can ask to which methodologies psychologists can use to investigate them. In this article, we offer a perspective on how to optimize new research in the pandemic's wake. Because this pandemic is inherently a social phenomenon-an event that hinges on human-to-human contact-we focus on socially relevant subfields of psychology. We highlight specific psychological phenomena that have likely shifted as a result of the pandemic and discuss theoretical, methodological, and practical considerations of conducting research on these phenomena. After this discussion, we evaluate metascientific issues that have been amplified by the pandemic. We aim to demonstrate how theoretically grounded views on the COVID-19 pandemic can help make psychological science stronger-not weaker-in its wake.

This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. We discuss similarities and differences between these packages and provide directions on how to use them effectively. We use real data from the Carolina Abecedarian Project to show how an analysis of an RDD can be performed from start to finish.

Ding and Miratrix [1] recently concluded that adjustment on a pre-treatment covariate is almost always preferable to reduce bias. I extend the examined parameter space of the models considered by Ding and Miratrix, and consider slight extensions of their models as well. Similar to the conclusion by Pearl [7], I identify constellations in which bias due to adjustment, or failing to adjust is symmetrical, but also confirm some findings of Ding and Miratrix.

The association between grade retention in first grade and passing the third grade state accountability tests, the Texas Assessment of Knowledge and Skills (TAKS) reading and math, was investigated in a sample of 769 students who were recruited into the study when they were in first grade. Of these 769 students, 165 were retained in first grade and 604 were promoted. Using propensity matching, we created five imputed datasets (average N = 321) in which promoted and retained students were matched on 67 comprehensive covariates. Using GEE models, we obtained the association between retention and passing the third grade TAKS reading and math tests. The positive association between retention and math scores was significant, whereas the association was marginally significant for reading scores. (Contains 1 note and 4 tables.)