Title: Psychological science in the Wake of COVID-19: Social, methodological, and meta-scientific considerations
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. more »« less
Ruisch, Benjamin Coe; Moore, Courtney; Granados Samayoa, Javier; Boggs, Shelby; Ladanyi, Jesse; Fazio, Russell(
, Political Psychology)
The COVID‐19 disease pandemic is one of the most pressing global health issues of our time. Nevertheless, responses to the pandemic exhibit a stark ideological divide, with political conservatives (versus liberals/progressives) expressing less concern about the virus and less behavioral compliance with efforts to combat it. Drawing from decades of research on the psychological underpinnings of ideology, in four studies (totalN = 4441) we examine the factors that contribute to the ideological gap in pandemic response—across domains including personality (e.g., empathic concern), attitudes (e.g., trust in science), information (e.g., COVID‐19 knowledge), vulnerability (e.g., preexisting medical conditions), demographics (e.g., education, income) and environment (e.g., local COVID‐19 infection rates). This work provides insight into the most proximal drivers of this ideological divide and also helps fill a long‐standing theoretical and empirical gap regarding how these various ideological differences shape responses to complex real‐world sociopolitical events. Among our key findings are the central role of attitude‐ and belief‐related factors (e.g., trust in science and trust in Trump)—and the relatively weaker influence of several domain‐general personality factors (empathic concern, disgust sensitivity, conspiratorial ideation). We conclude by considering possible explanations for these findings and their broader implications for our understanding of political ideology.
Highlights
Stark ideological differences exist across a wide range of attitudinal and behavioral indices of pandemic response, with more conservative individuals reliably exhibiting less concern about the virus. These findings illustrate the extent to which the pandemic has become politicized.
A range of factors contribute to this ideological gap in pandemic response, but some are substantially more important than others.
Several factors that have received attention in public and academic discourse about the pandemic appear to contribute little, if at all, to the ideological divide. These include news following, scientific literacy, perceived social norms, and knowledge about the virus.
The most critical factors appear to be trust in scientists and trust in Trump, which further highlights the politicization of COVID‐19 and, importantly, the antagonistic nature of these two beliefs. Efforts to change and, especially, disentangle these two attitudes have the potential to be effective interventions.
Kwon, K. Hazel; Pellizzaro, Kirstin; Shao, Chun; Chadha, Monica(
, American Behavioral Scientist)
The spread of misinformation through a variety of communication channels has amplified society’s challenge to manage the COVID-19 pandemic. While existing studies have examined how misinformation spreads, few studies have examined the role of psychological distance in people’s mental processing of a rumor and their propensity to accept self-transformed narratives of the message. Based on an open-ended survey data collected in the U.S. ( N = 621) during an early phase of the pandemic, the current study examines how psychological distance relates to the transformation and acceptance of conspiratorial narratives in the context of the COVID-19 pandemic. Two instances of misinformation are examined, both of which were widely heard at the time of data collection: the role of (a) Bill Gates and (b) government during the outbreak of the pandemic. This study uses topic modeling techniques to capture distinctive topical attributes that emerged from rumor narratives. In addition, statistical analyses estimate the psychological distance effects on the salience of topical attributes of a rumor story and an individual’s propensity to believe them. Findings reveal that psychological distance to the threats of COVID-19 influences how misinformation evolves through word-of-mouth, particularly in terms of who is responsible for the pandemic and why the world finds itself in the current situation. Psychological distance also explains why people accept the message to be true. Implications for misinformation and rumor psychology research, as well as avenues for future research, are discussed.
McGill, Monica M.; Snow, Eric; Vaval, Luronne; DeLyser, Leigh Ann; Wortel-London, Stephanie; Thompson, Angelica(
, ACM Transactions on Computing Education)
Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing. Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students? Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS. Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators (n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators ( n = 185), analyzed the data, and considered how these findings shape research conducted over the last year. Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases. Practitioner perspectives also indicated that schools serving students from lower-income families had 1) a greater decrease in the number of students who received information about CS/CTE pathways; 2) a greater decrease in the number of girls enrolled in CS classes; 3) a greater decrease in the number of students receiving college credit for dual-credit CS courses; 4) a greater decrease in student attendance; and 5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses.
Villanustre, Flavio; Chala, Arjuna; Dev, Roger; Xu, Lili; LexisNexis, Jesse Shaw; Furht, Borko; Khoshgoftaar, Taghi(
, Journal of Big Data)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
This virtual special issue (VSI) collects together 19 papers published in Organization Science that explore how organizations learn from crises. The objective is to discuss insights that can help us understand the COVID-19 pandemic crisis, implications that existing research carries for organizations’ abilities to keep hard-earned lessons after the storm passes, and opportunities that the current phenomenon offers for future inquiry in this domain. Organizations, large and small, in scores of countries, have suspended normal operations. To survive, many organizations have adapted by shifting almost all human-to-human interactions online while facing an ethical dilemma and a tense tradeoff between public health and economic well-being. We take stock of the research on organizational learning from crises, summarize useful knowledge for managing the current crisis, and provide directions for future research.
@article{osti_10336923,
place = {Country unknown/Code not available},
title = {Psychological science in the Wake of COVID-19: Social, methodological, and meta-scientific considerations},
url = {https://par.nsf.gov/biblio/10336923},
DOI = {10.31234/osf.io/6gjfm},
abstractNote = {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.},
journal = {Perspectives on psychological science},
volume = {17},
number = {2},
author = {Rosenfeld, D.L. and Balcetis, E. and Bastian, B. and Berkman, E.T. and Bosson, J.K. and Brannon, T.N. and Burrow, A.L. and Cameron, C.D. and Chen, S. and Cook, J.E. and Crandall, C. and Davidai, S. and Dhont, K. and Eastwick, P.W. and Gaither, S. E. and Gangestad, S.W. and Gilovich, T. and Gray, K.J. and Haines, E.L. and Haselton, M.G. and Haslam, N. and Hodson, G. and Hogg, M.A. and Hornsey, M.J. and Huo, Y.J. and Joel, S. and Jost, J.T. and Kachanoff, F. and Kraft-Todd, G. and Leary, M.R. and Ledgerwood, A. and Lee, R.T. and Loughnan, S. and MacInnis, C.C. and Mann, T. and Murray, D.R. and Parkinson, C. and Pérez, E.O. and Pyszczynski, T. and Ratner, K. and Rothgerber, H. and Rounds, J.D. and Schaller, M. and Silver, R. C. and Spellman, B. A. and Strohminger, N. and Swim, J. K. and Thoemmes, F. and Urganci, B. and Vandello, J.A. and Volz, S. and Zayas, V. and Tomiyama, A.J.},
}
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