Social and moral psychology of COVID-19 across 69 countries

Abstract

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behavior change. To help scholars better understand the social and moral psychology behind public health behavior, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of individual differences and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Full text

Social and moral psychology of COVID-19 across 69
countries
Flavio Azevedo1,*, Tomislav Pavlovi´
c2, Gabriel G. Rˆ
ego3, F. Ceren Ay4,5, Biljana
Gjoneska6, Tom W. Etienne7,8, Robert M. Ross9, Philipp Sch ¨
onegger10,11, Juli ´
an C.
Ria ˜
no-Moreno12,13, Aleksandra Cichocka14, Valerio Capraro15, Luca Cian16, Chiara
Longoni17, Ho Fai Chan18,19, Jay J. Van Bavel20, Hallgeir Sj ˚
astad21, John B. Nezlek22,23,
Mark Alfano24, Michele J. Gelfand25, Mich `
ele D. Birtel26, Aleksandra Cislak22, Patricia L.
Lockwood27,28, Koen Abts29, Elena Agadullina30, John Jamir Benzon Aruta31 , Sahba
Nomvula Besharati32, Alexander Bor33, Becky L. Choma34, Charles David Crabtree35,
William A. Cunningham36, Koustav De37, Waqas Ejaz38, Christian T. Elbaek39, Andrej
Findor40, Daniel Flichtentrei41, Renata Franc2, June Gruber42, Estrella Gualda43,44 ,
Yusaku Horiuchi35, Toan Luu Duc Huynh45, Agustin Ibanez46,47,48, Mostak Ahamed
Imran49, Jacob Israelashvili50, Katarzyna Jasko51, Jaroslaw Kantorowicz52, Elena
Kantorowicz-Reznichenko53, Andr´
e Krouwel54, Michael Laakasuo55, Claus Lamm56 ,
Caroline Leygue57, Ming-Jen Lin58,59, Mohammad Sabbir Mansoor60 , Antoine Marie33,
Lewend Mayiwar61, Honorata Mazepus62,63, Cillian McHugh64 , John Paul Minda65,
Panagiotis Mitkidis39,66, Andreas Olsson67, Tobias Otterbring68,69, Dominic J. Packer70,
Anat Perry50, Michael Bang Petersen33, Arathy Puthillam71, Tobias Rothmund72,
Hernando Santamar´ıa-Garc´ıa73, Petra C. Schmid74, Drozdstoy Stoyanov75, Shruti
Tewari76, Bojan Todosijevi´
c77, Manos Tsakiris78,79,80, Hans H. Tung81,59, Radu G.
Umbres
,82, Edmunds Vanags83, Madalina Vlasceanu84, Andrew Vonasch85, Meltem
Yucel86,87, Yucheng Zhang88, Mohcine Abad89, Eli Adler50 , Narin Akrawi90, Hamza Alaoui
Mdarhri89, Hanane Amara91, David M. Amodio20,92, Benedict G. Antazo93, Matthew
Apps28, Mouhamadou Hady Ba94, Sergio Barbosa95,96, Brock Bastian97, Anton Berg55,
Maria P. Bernal-Z´
arate12, Michael Bernstein98, Michał Białek99, Ennio Bilancini100, Natalia
Bogatyreva30, Leonardo Boncinelli101, Jonathan E. Booth102, Sylvie Borau103, Ondrej
Buchel104,105, C. Daryl Cameron106,107, Chrissie F. Carvalho108, Tatiana Celadin109, Chiara
Cerami110,111, Hom Nath Chalise60, Xiaojun Cheng112, Kate Cockcroft32, Jane Conway113,
Mateo Andres C ´
ordoba-Delgado73, Chiara Crespi111,114, Marie Crouzevialle74, Jo
Cutler27,28, Marzena Cyprya ´
nska22, Justyna Dabrowska115, Michael A. Daniels116,
Victoria H. Davis36, Pamala N. Dayley117, Sylvain Delouv´
ee118, Ognjan Denkovski92,
Guillaume Dezecache119, Nathan A. Dhaliwal116, Alelie B. Diato120, Roberto Di Paolo100,
Marianna Drosinou55, Uwe Dulleck18,19,121,122, J ¯
anis Ekmanis83, Arhan S. Ertan123 , Hapsa
Hossain Farhana49, Fahima Farkhari72, Harry Farmer26, Ali Fenwick124, Kristijan
Fidanovski125, Terry Flew126, Shona Fraser127, Raymond Boadi Frempong128, Jonathan
A. Fugelsang129, Jessica Gale85 , E. Bego˜
na Garcia-Navarro43, Prasad Garladinne76,
Oussama Ghajjou130, Theofilos Gkinopoulos131, Kurt Gray132, Siobh ´
an M. Griffin64,
Bjarki Gronfeldt14, Mert G ¨umren133, Ranju Lama Gurung60, Eran Halperin50, Elizabeth
Harris20, Volo Herzon55, Matej Hruˇ
ska40, Guanxiong Huang134, Matthias F. C. Hudecek135,
Ozan Isler18,19, Simon Jangard67, Frederik J. Jørgensen33, Frank Kachanoff132, John
Kahn35, Apsara Katuwal Dangol60, Oleksandra Keudel136, Lina Koppel137, Mika
Koverola55, Emily Kubin138, Anton Kunnari55, Yordan Kutiyski7, Oscar Laguna7, Josh
Leota139, Eva Lermer140,141, Jonathan Levy142,143, Neil Levy24, Chunyun Li102, Elizabeth U.
Long36, Marina Magli ´
c2, Darragh McCashin144, Alexander L. Metcalf145, Igor Miklou ˇ
si´
c2,
Soulaimane El Mimouni91, Asako Miura146, Juliana Molina-Paredes73, C ´
esar
Monroy-Fonseca147, Elena Morales-Marente43, David Moreau148, Rafał Muda149, Annalisa
Myer87,150, Kyle Nash139, Tarik Nesh-Nash91, Jonas P. Nitschke56, Matthew S. Nurse151,

Yohsuke Ohtsubo152 , Victoria Oldemburgo de Mello36, Cathal O’Madagain89, Michal
Onderco153, M. Soledad Palacios-Galvez43, Jussi Palom¨
aki55, Yafeng Pan67, Zs ´
ofia
Papp154, Philip P¨
arnamets67, Mariola Paruzel-Czachura155,156, Zoran Pavlovi´
c157, C ´
esar
Pay´
an-G ´
omez158, Silva Perander55, Michael Mark Pitman32, Rajib Prasad159, Joanna
Pyrkosz-Pacyna160, Steve Rathje1, Ali Raza161,162, Kasey Rhee163, Claire E. Robertson20 ,
Iv´
an Rodr´ıguez-Pascual43, Teemu Saikkonen164, Octavio Salvador-Ginez57, Gaia C.
Santi110, Natalia Santiago-Tovar165, David Savage166, Julian A. Scheffer106, David T.
Schultner93, Enid M. Schutte32, Andy Scott139, Madhavi Sharma60, Pujan Sharma60,
Ahmed Skali167, David Stadelmann128, Clara Alexandra Stafford65,168,169, Dragan
Stanojevi´
c170, Anna Stefaniak171, Anni Sternisko20, Augustin Stoica172, Kristina K.
Stoyanova173, Brent Strickland89,174, Jukka Sundvall55, Jeffrey P. Thomas97 , Gustav
Tingh ¨
og137, Benno Torgler18,19,175, Iris J. Traast93, Raffaele Tucciarelli176,177, Michael
Tyrala178, Nick D. Ungson179, Mete S. Uysal180, Paul A. M. Van Lange181, Jan-Willem van
Prooijen181, Dirk van Rooy182, Daniel V ¨
astfj¨
all183, Peter Verkoeijen184, Joana B. Vieira67,
Christian von Sikorski185, Alexander Cameron Walker129, Jennifer Watermeyer186, Erik
Wetter187, Ashley Whillans188, Robin Willardt74, Michael J. A. Wohl171, Adrian Dominik
W´
ojcik189, Kaidi Wu190, Yuki Yamada191, Onurcan Yilmaz192, Kumar Yogeeswaran85,
Carolin-Theresa Ziemer72, Rolf A. Zwaan184, Paulo S. Boggio3, and Waldir M. Sampaio3
1Department of Psychology, University of Cambridge, Cambridge, England.
2Institute of Social Sciences Ivo Pilar, Zagreb, Croatia.
3Social and Cognitive Neuroscience Laboratory, Mackenzie Presbyterian University, S˜
ao Paulo, Brazil.
4Department of Economics, Norwegian School of Economics, Bergen, Norway.
5Telenor Research, Oslo, Norway.
6Macedonian Academy of Sciences and Arts, North Macedonia, Republic of North Macedonia.
7Kieskompas - Election Compass, Amsterdam, Netherlands.
8Department of Political Science, Penn State University, University Park, PA, USA.
9Department of Psychology, Macquarie University, Sydney, NSW, Australia.
10Department of Philosophy, University of St Andrews, St Andrews, Scotland.
11School of Economics and Finance, University of St Andrews, St Andrews, Scotland.
12Medicine Faculty, Cooperative University of Colombia, Villavicencio, Colombia.
13Department of Bioethics, El Bosque University, Bogot´
a, Colombia.
14School of Psychology, University of Kent, Canterbury, England.
15Department of Economics, Middlesex University London, London, England.
16Darden School of Business, University of Virginia, Charlottesville, VA, USA.
17Questrom School of Business, Boston University, Boston, MA, USA.
18School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, Australia.
19Center for Behavioural Economics, Society and Technology, Queensland University of Technology, Brisbane,
QLD, Australia.
20Department of Psychology and Neural Science, New York University, New York, NY, USA.
21Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway.
22SWPS University of Social Sciences and Humanities, Pozna´
n, Poland.
23Department of Psychological Sciences, College of William and Mary, Williamsburg, VA, USA.
24Department of Philosophy, Macquarie University, Sydney, NSW, Australia.
25Stanford Graduate School of Business, Stanford University, Stanford, CA, USA.
26School of Human Sciences, Institute for Lifecourse Development, University of Greenwich, London, England.
27Department of Experimental Psychology, University of Oxford, Oxford, England.
28Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, England.
29KU Leuven, Leuven, Belgium.
30National Research University Higher School of Economics (HSE), Moscow, Russia.
31De La Salle University, Manila, Philippines.
32Department of Psychology, University of the Witwatersrand, Johannesburg, South Africa.
33Department of Political Science, Aarhus University, Aarhus, Denmark.
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34X University, Toronto, Canada.
35Department of Government, Dartmouth College, Hanover, NH, USA.
36Department of Psychology, University of Toronto, Toronto, ON, Canada.
37Gatton College of Business and Economics, University of Kentucky, Lexington, KY, USA.
38Department of Mass Communication, National University of Science and Technology (NUST), Islamabad,
Pakistan.
39Department of Management, Aarhus University, Aarhus, Denmark.
40Faculty of Social and Economic Sciences, Comenius University, Bratislava, Slovakia.
41IntraMed, Buenos Aires, Argentina.
42University of Colorado Boulder, Boulder, CO, USA.
43ESEIS/COIDESO [ESEIS, Social Studies and Social Intervention Research Center; COIDESO, COIDESO,
Center for Research in Contemporary Thought and Innovation for Social Development], University of Huelva,
Huelva, Spain.
44Faculty of Social Work, University of Huelva, Huelva, Spain.
45WHU – Otto Beisheim School of Management, Vallendar, Germany.
46Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ib´
a˜
nez, Santiago, Chile.
47Cognitive Neuroscience Center (CNC), University of San Andr´
es, Buenos Aires, Argentina.
48Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity
College Dublin (TCD), Dublin, Ireland.
49Department of Educational and Counselling Psychology, University of Dhaka, Dhaka, Bangladesh.
50Psychology Department, The Hebrew University of Jerusalem, Jerusalem, Israel.
51Institute of Psychology, Jagiellonian University, Krak´
ow, Poland.
52Institute of Security and Global Affairs, Leiden University, The Hague, Netherlands.
53Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, Netherlands.
54Department of Political Science, Vrije University (VU) Amsterdam, Amsterdam, Netherlands.
55Department of Digital Humanities, University of Helsinki, Helsinki, Finland.
56Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria.
57School of Psychology, National Autonomous University of Mexico, Mexico City, Mexico.
58Department of Economics, National Taiwan University, Taipei, Taiwan.
59Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan.
60Tribhuvan University, Kirtipur, Nepal.
61Department of Leadership and Organizational Behavior, BI Norwegian Business School, Oslo, Norway.
62Institute of Security and Global Affairs, Leiden University, Leiden, Netherlands.
63Faculty of Governance and Global Affairs, Leiden University, Leiden, Netherlands.
64Department of Psychology, University of Limerick, Limerick, Ireland.
65Department of Psychology, The University of Western Ontario, London, ON, Canada.
66Center for Advanced Hindsight, Duke University, Durham, NC, USA.
67Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden.
68Department of Management, University of Agder, Kristiansand, Norway.
69Institute of Retail Economics, Stockholm, Sweden.
70Department of Psychology, Lehigh University, Bethlehem, PA, USA.
71Department of Psychology, Monk Prayogshala, Mumbai, India.
72Institute of Communication Science, Friedrich-Schiller University Jena, Jena, Germany.
73Faculty of Medicine, Pontifical Javeriana University, Bogot ´
a, Colombia.
74Department of Management, Technology, and Economics, ETH Z¨
urich, Z ¨
urich, Switzerland.
75Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv, Plovdiv,
Bulgaria.
76Humanities and Social Sciences, Indian Institute of Management, Indore, India.
77Institute of Social Sciences, Belgrade, Serbia.
78Department of Psychology, Royal Holloway, University of London, London, England.
79Center for the Politics of Feelings, School of Advanced Study, University of London, London, England.
80Department of Behavioral and Cognitive Sciences, Faculty of Humanities, Education and Social Sciences,
University of Luxembourg, Luxembourg City, Luxembourg.
81Department of Political Science, National Taiwan University, Taipei, Taiwan.
82Faculty of Political Science, National School for Political Studies and Public Administration, Bucharest,
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Romania.
83Department of Psychology, University of Latvia, Riga, Latvia.
84Department of Psychology, Princeton University, Princeton, NJ, USA.
85Department of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand.
86Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
87Department of Psychology, University of Virginia, Charlottesville, VA, USA.
88School of Economics and Management, Hebei University of Technology, Tianjin, PR China.
89School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco.
90Institute for Research and Development-Kurdistan, Middle East, Iraq.
91Impact For Development, North Africa, Morocco.
92Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
93Department of Psychology, Jose Rizal University, Mandaluyong, Philippines.
94Department of Philosophy, University Cheikh Anta Diop, Dakar, Senegal.
95School of Medicine and Health Sciences, University of Rosario, Bogot´
a, Colombia.
96Moral Psychology and Decision Sciences Research Incubator, University of Rosario, Bogot´
a, Colombia.
97School of Psychological Sciences, University of Melbourne, Parkville, VIC, Australia.
98Department of Psychological and Social Sciences, Penn State Abington, Abington, PA, USA.
99Institute of Psychology, University of Wrocław, Wrocław, Poland.
100IMT School for Advanced Studies Lucca, Lucca, Italy.
101Department of Economics and Management, University of Florence, Florence, Italy.
102Department of Management, London School of Economics and Political Science, London, England.
103Toulouse Business School, University of Toulouse, Toulouse, France.
104Social Policy Institute of the Ministry of Labor, Family and Social Affairs of the Slovak Republic, Bratislava,
Slovakia.
105The Institute for Sociology of the Slovak Academy of Sciences, Bratislava, Slovakia
106Department of Psychology, Penn State University, University Park, PA, USA.
107Rock Ethics Institute, Penn State University, University Park, PA, USA.
108Department of Psychology, Federal University of Santa Catarina, Florian´
opolis, Brazil.
109Department of Economics, University of Bologna, Bologna, Italy.
110IUSS Cognitive Neuroscience (ICoN) Center, Institute for Advanced Study of Pavia, Pavia, Italy.
111Cognitive Computational Neuroscience Research Unit, Neurological Institute Foundation Casimiro Mondino,
Pavia, Italy.
112School of Psychology, Shenzhen University, Shenzhen, PR China.
113Institute for Advanced Study in Toulouse, Universit ´
e Toulouse 1 Capitole, Toulouse, France.
114Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
115Cracow University of Economics, Krak´
ow, Poland.
116UBC Sauder School of Business, University of British Columbia, Vancouver, BC, Canada.
117Psychology Department, University of California - Los Angeles, Los Angeles, CA, USA.
118Laboratory of Psychology: Cognition, Behavior, and Communication (LP3C), Rennes 2 University, Rennes,
France.
119Laboratory of Social and Cognitive Psychology, Clermont Auvergne University, CNRS, Clermont-Ferrand,
France.
120Cavite State University-General Trias City Campus, Cavite, Philippines.
121Crawford School of Public Policy, Australian National University, Canberra, ACT, Australia.
122CESifo, University of Munich, Munich, Germany.
123Department of International Trade, Bo ˘
gazic¸i University, Istanbul, Turkey.
124Hult International Business School Dubai, Dubai, UAE.
125Department of Social Policy and Intervention, University of Oxford, Oxford, England.
126Department of Media and Communications, University of Sydney, Sydney, NSW, Australia.
127Department of Psychiatry, University of the Witwatersrand, Johannesburg, South Africa.
128University of Bayreuth, Bayreuth, Germany.
129Department of Psychology, University of Waterloo, Waterloo, ON, Canada.
130Department of Peace Studies, University of Bradford, Bradford, England.
131Philosophy and Social Studies Department, Rethymno, Greece.
132Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC,
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USA.
133Department of Economics, Koc University, Istanbul, Turkey.
134Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
135University of Regensburg, Regensburg, Germany.
136Graduate School for Transnational Studies, Free University of Berlin, Berlin, Germany.
137Department of Management and Engineering, Link¨
oping University, Link ¨
oping, Sweden.
138Department of Psychology, University of Koblenz-Landau, Landau, Germany.
139Department of Psychology, University of Alberta, Edmonton, Canada.
140LMU Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich,
Germany.
141Ansbach University for Applied Sciences, Ansbach, Germany.
142Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya (IDC), Herzliya, Israel.
143Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
144School of Psychology, Dublin City University, Dublin, Ireland.
145University of Montana, Missoula, MT, USA.
146Graduate School of Human Sciences Human Sciences, Osaka University, Suita, Japan.
147SEELE Neuroscience, Mexico City, Mexico.
148School of Psychology, University of Auckland, Auckland, New Zealand.
149Faculty of Economics, Maria Curie-Skłodowska University, Lublin, Poland.
150Department of Psychology, The City University of New York (CUNY) Graduate Center, New York, NY, USA.
151Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, ACT,
Australia.
152Department of Social Psychology, Graduate School of Humanities and Sociology, University of Tokyo, Tokyo,
Japan.
153Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands.
154Center for Social Sciences, Hungarian Academy of Sciences Center of Excellence, Budapest, Hungary.
155Institute of Psychology, University of Silesia, Katowice, Poland.
156Complutense University in Madrid, Spain
157Department of Psychology, University of Belgrade, Belgrade, Serbia.
158Universidad Nacional de Colombia, Sede de La Paz, La Paz, Colombia.
159Vidyasagar College For Women, Kolkata, India.
160AGH University of Science and Technology, Krak´
ow, Poland.
161Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
162Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.
163Stanford University, Stanford, CA, USA.
164Department of Biology, University of Turku, Turku, Finland.
165Cooperative University of Colombia, Bogot´
a, Colombia.
166Newcastle Business School, University of Newcastle, Callaghan, NSW, Australia.
167Department of Global Economics and Management, University of Groningen, Groningen, Netherlands.
168Brain and Mind Institute, University of Western Ontario, London, ON, Canada.
169Western Interdisciplinary Research Building, University of Western Ontario, London, ON, Canada.
170Department of Sociology, University of Belgrade, Belgrade, Serbia.
171Department of Psychology, Carleton University, Ottawa, ON, Canada.
172National University of Political Studies and Public Administration (SNSPA), Bucharest, Romania.
173Research Institute at Medical University of Plovdiv), Division of Translational Neuroscience, Plovdiv, Bulgaria.
174Department of Cognitive Science, ENS, EHESS, CNRS, Institut Jean Nicod, PSL Research University, Paris,
France.
175CREMA - Center for Research in Economics, Management and the Arts, Basel, Switzerland.
176The Warburg Institute, School of Advanced Study, University of London, London, England.
177Institute of Cognitive Neuroscience, University College London, London, England.
178Institute for Emerging Market Studies, The Hong Kong University of Science and Technology, Kowloon, Hong
Kong.
179Department of Psychology, Susquehanna University, Selinsgrove, PA, USA.
180Psychology Department, Dokuz Eyl¨
ul University, ˙
Izmir, Turkey.
181Department of Experimental and Applied Psychology, VU Amsterdam, Amsterdam, Netherlands.
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182Faculty of Design Sciences, University of Antwerp, Belgium
183Department of Behavioural Sciences and Learning (IBL), Link ¨
oping University, Link ¨
oping, Sweden.
184Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam,
Netherlands.
185University of Koblenz-Landau, Landau, Germany.
186Health Communication Research Unit, School of Human and Community Development, University of the
Witwatersrand, Johannesburg, South Africa.
187Department of Business Administration, Stockholm School of Economics, Stockholm, Sweden.
188Harvard Business School, Harvard University, Cambridge, MA, USA.
189Nicolaus Copernicus University, Toru ´
n, Poland.
190University of California, San Diego, La Jolla, CA, USA.
191Kyushu University, Fukuoka, Japan.
192Department of Psychology, Kadir Has University, Istanbul, Turkey.
*Corresponding author: Flavio Azevedo (fa441@cam.ac.uk)
ABSTRACT
The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the
central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective
behavior change. To help scholars better understand the social and moral psychology behind public health behavior, we present a
dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on
Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the
world to complete a series of individual differences and public health attitudes about COVID-19 during an early phase of the
COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs
and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality
traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and
psychometric evaluations of key variables.
Background & Summary
Well over two years after the official outbreak
1
, it is evident that the COVID-19 pandemic has affected all domains
of human life, including the economic and social fabric of societies
2
as well as people’s physical and mental health
3
.
At the time of writing, the world reached half a billion confirmed infections and up to 18 million deaths
4
. The
detrimental effects of the pandemic extend beyond physical health with evidence of increased stress levels
5
and suicide
rates
6
, along with deterioration of general well-being
7
. Such findings reflect the cautionary warnings by Taylor
8
that
the psychological and societal effects are “likely to be more pronounced, more widespread, and longer-lasting than
the purely somatic effects of the infection” [8, p.23].
In the early stages of the pandemic, when vaccines were not yet available, governments introduced non-
pharmaceutical interventions to reduce the spread of the SARS-CoV-2 virus
9
. Various contact-restricting policies
(e.g., stay-at-home recommendations, curfews, police hours, partial or complete lock-downs) were enacted, and
citizens were advised to adhere to public health recommendations (e.g., hand washing, face masks, and spatial
distancing). It quickly became clear that behavioural science had a major role to play
10
. Recognising the potential
value of social and moral psychology in this process, we assembled a multi-national team of social and behavioural
science researchers.
On April 11
th
, a team of researchers launched a call for international collaboration in social and moral psychology.
The initiative quickly gained momentum, gathering a consortium of over 250 academics worldwide. The aim of this
project was to collect data from as many countries as possible to serve as a public good for the scientific community
by allowing future research to draw on this broad database collected during this early phase of the COVID-19
pandemic. The survey, developed by the initial team, was circulated among the national teams, who provided
feedback, translated it into 32 languages, and disseminated it online. The project concluded with responses from a
total of 51,404 participants from 69 countries, 77 samples, between April 22rd and June 3rd, 2020.
A key goal of the project was to test the hypothesis that national identity predicts support for public health
measures during the COVID-19 pandemic, which has since been confirmed
11,12
. In addition to collecting variables to

test this hypothesis, we collected data on a variety of other social and moral constructs to make of our multi-country
large-scale survey a rich resource for future research. The survey focused on the following areas: on a) COVID-
19 beliefs and compliance behaviours (COVID-19 public health support, COVID-19 risk perception, COVID-19
conspiracy beliefs, and COVID-19 testing behaviour); b) identity and social attitudes (national identification,
national narcissism, and social belonging); c) ideology (political ideology); d) health and well-being (subjective
physical health, a wealth ladder ranking, and psychological well-being); e) moral beliefs and motivation (generosity,
morality as cooperation, moral identity, and moral circle); f) personality traits and dispositions (open-mindedness,
self-esteem, trait optimism, trait self-control, narcissism, and cognitive reflection); and g) demographic variables (i.e.,
sex, age, marital status, number of children, and employment status).
Using this dataset, project team members have pre-registered a variety of secondary hypotheses (see icsmp-
covid19.netlify.app/preregistration), several of which have already been published
11–19
. In this paper, we present
the complete ICSMP datasets to facilitate its findability, accessibility, interoperability, and reuse (FAIR;
20,21
) and
maximize its educational impact22–24.
Methods
Whenever possible, we used articles at Nature’s Scientific Data presenting social sciences data as a blueprint
5,25
.
Given the urgent call for COVID-19 research, this study received an umbrella ethical approval from the University
of Kent (see osf.io/ce638) but also complied with local ethics, norms, and regulations in the countries where the
Data were collected.
Participants
A total of 51
,
404 individuals from 77 samples across 69 countries participated in our survey. The inclusion criteria
were the following: being 18 years of age and older, and giving informed consent (although researchers were
encouraged to, ideally, recruit representative samples regarding age and gender). Data were collected between April
22
rd
and June 3
rd
, 2020. Figure 1displays where the data were collected, coloured according to national sample size.
Figure 2shows when the data were collected in each country.
Demographic variables across countries are summarised in several tables: Table 1shows the number of participants,
the mean proportion of non-missing ‘valid’ answers, and age. Table 2illustrates the distribution of gender; Table
3shows employment status; and Table 4shows marital status and number of children. When multiple samples
were collected within the same country, data were split into numbered subgroups (e.g., for Brazil, which has three
samples, they were flagged as Brazil1, Brazil2 and Brazil3).
For the most part, participants were recruited via professional survey research companies and were incentivised to
participate. In countries that, to our knowledge, did not possess polling infrastructure
26
, incentivising participants
was not feasible. To collect data, leaders relied on online volunteers recruited via media appeals, mailing lists,
advertisements on news aggregators, local communities and bloggers, and private messaging apps such as WhatsApp
or WeChat.
Materials
The measures we used are illustrated in Figure 3.a and Figure 3.b along with the specific items listed for each
measure. In most cases, participants’ responses were collected on a scale from 0 = ‘strongly disagree’ to 10 =
‘strongly agree’, with 5 = ‘neither disagree nor agree’. In some cases, when more appropriate, we used other response
scales (e.g., the generosity measure, where a 0-100% response scale was applied to hypothetical donations). In total,
we collected 98 unique variables and meta-data. To ensure participants’ anonymity, no data that would allow their
identification were collected.
COVID-19 Beliefs and Compliance
Five constructs: COVID-19 public health support, COVID-19 risk perception, COVID-19 conspiracy theory beliefs,
and COVID-19 testing behaviour. The public health support construct, in turn, is composed of three measures:
spatial distancing, physical hygiene, and policy support. These are ad-hoc scales that we developed ourselves.
Identity and Social Attitudes
Three constructs: national identification27, national narcissism28, and social belonging29.
Ideology
One construct: political ideology. Participants self-reported their political orientation according to a single item
on a scale from 0 (“Very left-leaning”) to 10 (“Very right-leaning”). This measure has been shown to account for
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a significant proportion of the variance in voting intentions in American presidential elections between 1972 and
200430 and 201631–33.
Health and Well-Being
Three constructs: subjective physical health, wealth ladder, and psychological well-being. Each of these scales relied
on well-validated34–36.
Moral Beliefs and Motivation
Four constructs: generosity37, morality as cooperation38 , moral identity39, and moral circle40 .
Personality Traits
Six constructs: open-mindedness
41
, self-esteem
42
, trait optimism
43
, trait self-control
44
, narcissism
45
, and cognitive
reflection46.
Demographics
Six questions: age, number of children, employment status, marital status, gender, and urbanicity.
Metadata and Attention Check
An attention check was used to mitigate negative impact on data quality from potential non-human responses
and the likelihood of biasing data and subsequent analyses of low base-rate outcomes—such as endorsement of
COVID-19 conspiracies. We collected typical questionnaire metadata (e.g., start, record, and end dates, duration,
and language). In addition, we created an internal participant ID, added ISO2 and ISO3 country codes, and sample
representativeness.
Translation
The survey instrument was drafted in English and translated into other languages using the standard forward-
backward method (i.e., members of national teams were advised to split members into forward-translating the
survey into the local language and back-translating it into English, and then have the two groups discuss and
resolve discrepancies). In total, the survey instrument was translated into 32 languages, including adaptations of
region-specific dialects or vernaculars. Specifically, from English into Arabic, Bengali, Bulgarian, Croatian, Danish,
Dutch, Finnish, French, German, Greek, Hebrew, Hungary, Italian, Japanese, Korean, Kurdish, Latvian, Macedonian,
Mandarin simplified, Mandarin traditional, Nepali, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian,
Slovak, Spanish, Swedish, Turkish, and Ukrainian.
Data Cleaning
We received individual data files from each national team. To merge these raw data, minor modifications were
introduced, which we delineate in this section. First, we renamed columns to match across data sets, reordered
variables alphabetically, and standardised variable labels. Furthermore, all missing values and values denoting the
absence of a response were converted to NAs (not available). When ambiguous date formats were found (e.g., on start
date, end date, and record date), we manually specified the correct format and standardised them. At the second
stage, we introduced multiple modifications to clean the data for research. Some modifications were introduced
to every national data set, while others were introduced to specific national data sets. To each national data set,
we recoded the attention check (attcheck) into pass (1) or fail (0); standardised generosity items (generosity1-3),
recoded CRT items into intuitive (2), correct (1), and incorrect (0); converted the number of children (children) into
a variable with a fixed range from zero to ten or more; recoded all participants declaring being older than 100 years
old as 100; and we excluded all duplicates (i.e., in case multiple participants were recorded with identical inputs
within a national database, only the first input was retained).
Data Records
All materials associated with the ICSMP COVID-19 project can be found on the project’s repository (comprising
five folders) hosted by the Open Science Framework (OSF, doi.org/10.17605/osf.io/tfsza). The folder named Code
includes an R Markdown document (ICSMP official data.Rmd; osf.io/dwpng) that loads multiple data files (from
each national team), cleans them up, merges them into a single data file, generates a data-driven code-book, and
saves all outputs. It also includes a reproducible report with all reported numbers, analyses and graphs in this article
(Analyses-SciData.html; osf.io/s5c4p; Analyses SciData.Rmd; osf.io/9suyb). The folder named Data includes three
sub-folders. The Raw data sub-folder contains the original and unmodified data files from each national team (country
data files.zip; osf.io/dqmut). The sub-folder named Cleaned data contains the merged and cleaned dataset, which are
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provided in a non-proprietary (ICSMP_cleaned_data.csv; osf.io/ypkrc) and a labelled (ICSMP_cleaned_data.sav;
at osf.io/8tyj9) file formats. The Metadata sub-folder provides a thorough itemised description of the data cleaning
process in both text (Data Cleaning.docx; osf.io/7udpt) and human-readable change-log (human-readable change
log ICSMP.xlsx; osf.io/fydx2).
We also provide a data-driven code-book detailing how each measure was collected—e.g., listing variable names,
variable labels, and label values (dt.codebook.xlsx; osf.io/ecva2). The IRB folder contains both the Internal Review
Board Ethics application (ICSMP Kent Ethics application full.pdf; osf.io/xt9gr) and Ethics approval (ICSMP Kent
Ethics approval.pdf; osf.io/ce638). The folder Sample Type & Representativeness includes the documentation for an
internal survey conducted with national team leaders to assert about the employed survey methodology (Sample
Type & Representativeness.zip; osf.io/fj5xn). The folder Survey Instrument contains the initial English version of
our survey instrument along with its Qualtrics .qsf for reproducibility (Survey Instrument.zip; osf.io/nf48q). In the
sub-folder Translations, we archived all 32 translated survey instruments along with a report on the languages of
conducted surveys per country (i.e., several countries had their surveys in multiple languages per country; Country
and language.xlsx; osf.io/wj7d2).
Data visualisation interface
In addition to the raw data, a dedicated Web application was developed to provide a general overview of the
Social and Moral Psychology of COVID-19 across 69 Countries dataset (icsmp.shinyapps.io/icsmp_covid19). The
application is based on an R shiny server (rstudio.com/products/shiny), together with the leaflet
47
and ggplot2
48
graphical libraries to generate dynamic plots. All the generated figures can be exported as .png files, and all tables
can be exported as .csv files. The Web application allows easy and dynamic generation of illustrations like the
figures with maps for each construct with zoom-able world maps, and static figures and plots for sample and country
characteristics. In addition, all tables are embedded with dynamic features for sorting and filtering. To make it more
accessible for the readers, both tables and figures are downloadable. The Shiny app has two tabs giving general
information about the project and the international consortium. The first tab contains sample descriptions such
as sample size, missing data, and attention checks for each country with a Gantt chart showing the dates of data
collection. The second tab displays world maps of spatial distancing, policy support, national identity, conspiracy
beliefs, national narcissism and morality as cooperation as well as all tables reported in dynamic formats.
Technical Validation
To support the technical quality of the dataset, we conducted an analyses to showcase its reliability (and its diverse
applicability to research questions in social sciences and beyond). For completeness, in the analyses that follow, we
examined all samples—including those with very few observations such as Puerto Rico (N=2), Brazil2 (N=6), and
Panama (N=12).
We evaluated the adopted survey methodology utilised by national teams by conducting an internal survey
to ensure the accuracy of reported sample types. The inspection showed that 28 samples were quota-based
nationally representative samples (36%), 6 used post hoc weights to achieve national representation (8%), and 43
were convenience samples (56%), many of which were from non-WEIRD countries. We codified the results of this
survey into the cleaned data as the variable ‘sample_coding’ and present a summary in Table 5.
Regarding individual-level data quality, Figure 4shows a world map of the 69 countries from which data were
collected, coloured according to overall percentages of missing data (overall mean = 6.0%). Overall, 4.4% of
participants had less than 50% missing data, 7.2% participants had less than 10% missing data, and 24.7% of
participants had 0% missing data. Another indicator of data quality is the rate of attention check fails per country.
On the last screen of the survey, participants were given the following instructions: “Help us get rid of bots: Please
write the number 213 into the comment box.” Participants who wrote “213” were coded as passing the attention
check, participants who wrote anything else were coded as failing the attention check, and those who did not reach
this screen of the survey were coded as missing data. Figure 4also shows (bottom plot) a world map coloured
according to the rate of attention-check fails across countries. Overall, 90.1% of participants passed the attention
check (1.0% failed), and 8.0% did not reach the final screen with the attention check.
The full dataset presents
N
= 51
,
404 cases across 69 countries (from 77 samples, 28 of which are quota-based
nationally representative), with an average sample size of 745 (SD = 549) and a proportion of valid answers of 95%.
The mean age of respondents was 42.93 (SD = 16.04) years and 50.9% were women (44% males, 0.3% others, and
4.8% unreported). The employment status break-down shows 44.8% employed full-time, 10.6% part-time, 8.1%
unemployed, 10% students, 10.1% retired, 11% other, and 5.3% unreported. The overall marital status shows 33%
of respondents were single, 18.7% in a relationship, 42.7% married, and 5.5% unreported. The majority of our
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participants reported having no children (41.6%), with 16.7% having one child, 20.1%, 9.2%, and 3.9% with two,
three and four children, respectively, and 1.7% had more than five or more children (6.9% unreported). We break
down these aggregated results per country. Table 1shows the number of cases and valid answers, Table 2summarises
the distribution of sex, Table 3displays employment status, and Table 4illustrates both marital statuses and the
number of children.
We also examined cross-cultural differences in conspiracy beliefs, morality as cooperation, spatial distancing,
national narcissism, national identification, and policy support for preventative measures across 69 countries in
Figure 5. Additionally, we showcase patterns of associations between these moral and psychological constructs across
age, gender, and ideology in Figure 6. For the association pattern analyses, we excluded samples with less than 490
respondents as recommended for stable correlations49, as well as for the subsequent consistency measure analyses.
To examine internal consistency for the main scales, we calculated Cronbach’s Alpha, Omega, Guttman split-half
reliability, and proportion of variance explained by a unidimensional factor. Table 6shows indices of internal
consistency, by country, for measures of conspiracy beliefs, morality as cooperation, spatial distancing, national
narcissism, national identification, and policy support for preventative measures respectively. We found that the
spatial distancing construct on average has the lowest Cronbach’s alpha, followed by morality as cooperation. On
average, conspiracy beliefs has the highest Cronbach’s alpha, followed by policy support. These patterns hold for the
Omega measures, but when considering Guttman’s split-half reliability, collective narcissism and national identity
yield the lowest values. Furthermore, on average, India tends to show the lowest consistency values over all included
constructs, followed by Nigeria and Israel. The three countries with the highest consistency measures, on average
over all six included constructs, are Norway, Finland, and Iraq. Figure 7show these patterns visually.
Usage Notes
The datasets are shared, cleaned, and ready for analysis. We recommend that interested researchers use the cleaned
version of the data (available at doi.org/10.17605/osf.io/tfsza). The use of the labelled data is also suggested for
convenience as it has all variable levels encoded, thus eliminating the need to consult the codebook when using the
.csv format.
The Data were imported and cleaned using the R software for statistical analysis
50
and packages readr
51
,haven
52
,
readxl53 ,dplyr54 ,psych55,htmltools56 , ımime57,xfun58 ,labelled59,sjlabel led60,codebook61,lubridate62.
As previously noted
5
, those wishing to approximate national representativeness can apply the appropriate survey
weights to demographic and countries of interest when random sampling is used (e.g., sex: https://ourworldindata.org/gender-
ratio; age: http://data.un.org/Data.aspx?d=POPf=tableCode%3A22; education: https://ourworldindata.org/global-
education; marital status: https://ourworldindata.org/marriages-and-divorces).
To minimize misclassification of text-based responses to the cognitive reflection test (CRT) and the attention
check, we used multiple steps of data cleaning using REGEX (regular expressions) as fully detailed in (ICSMP
official data.Rmd; osf.io/dwpng) located in the folder named Code. First, we coded the predefined numerical and
text values as correct (in the case of CRT, also the values predefined as intuitive). Then, iteratively, we screened the
remaining responses and, using REGEX, updated answers. Remaining responses were recoded as incorrect.
Code availability
All raw and cleaned data—as well as the R-code—used for standardising national-teams data, merging, and cleaning
them are available at doi.org/10.17605/osf.io/tfsza.
References
1. Cucinotta, D. & Vanelli, M. Who declares COVID-19 a pandemic. Acta Biomed. 91, 157 (2020).
2.
Nicola, M. et al. The socio-economic implications of the coronavirus and COVID-19 pandemic: A review. Int.
J. Surg. (2020).
3.
Holmes, E. A. et al. Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for
mental health science. The Lancet Psychiatry (2020).
4. Adam, D. et al. Covid’s true death toll: much higher than official records. Nature 603, 562–562 (2022).
5.
Yamada, Y. et al. COVIDiSTRESS global survey dataset on psychological and behavioural consequences of the
COVID-19 outbreak. Sci. Data. 8, 1–23 (2021).
10/44

6.
John, A. et al. The impact of the COVID-19 pandemic on self-harm and suicidal behaviour: A living systematic
review. F1000Research 9, 1097 (2020).
7.
Serafini, G. et al. The psychological impact of COVID-19 on the mental health in the general population. QJM
113, 531–537 (2020).
8.
Taylor, S. The psychology of pandemics: Preparing for the next global outbreak of infectious disease (Cambridge
Scholars Publishing, Newcastle upon Tyne, 2019).
9.
Haug, N. et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nat. Hum. Behav.
4, 1303–1312 (2020).
10.
Van Bavel, J. J. et al. Using social and behavioural science to support COVID-19 pandemic response. Nat.
Hum. Behav. 4, 460–471 (2020).
11.
Van Bavel, J. J. et al. National identity predicts public health support during a global pandemic. Nat. Commun.
13, 517 (2022).
12.
Pavlović, T. et al. Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine
learning. PNAS Nexus (in press) (2022).
13.
Sternisko, A., Cichocka, A., Cislak, A. & Van Bavel, J. J. National narcissism predicts the belief in and the
dissemination of conspiracy theories during the covid-19 pandemic: Evidence from 56 countries. Pers. Soc.
Psychol. Bull. 01461672211054947 (2021).
14.
Nurse, M. S., Ross, R. M., Isler, O. & Van Rooy, D. Analytic thinking predicts accuracy ratings and willingness
to share covid-19 misinformation in australia. Mem. & Cogn. 50, 425–434 (2022).
15.
Cichocka, A. et al. Globalization is associated with lower levels of national narcissism: Evidence from 56
countries. Soc. Psychol. Pers. Sci. (2022).
16.
Cutler, J., Nitschke, J. P., Lamm, C. & Lockwood, P. L. Older adults across the globe exhibit increased prosocial
behavior but also greater in-group preferences. Nat. Aging 1, 880–888 (2021).
17.
Elbaek, C., Mitkidis, P., Aarøe, L. & Otterbring, T. Social class and income inequality is associated with
morality: Empirical evidence from 67 countries. (2021). Preprint at https://www.researchsquare.com/article/
rs-1082570/v1.
18.
Maglić, M., Pavlović, T. & Franc, R. Analytic thinking and political orientation in the corona crisis. Front.
psychology 2711 (2021).
19.
Bonetto, E., Delouvée, S., Mahfud, Y. & Adam-Troian, J. National identification, a social cure for COVID-19?
evidence from 67 countries. Int. J. Soc. Psychiatry 00207640211020036 (2021).
20.
Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Sci. data 3,
1–9 (2016).
21.
Parsons, S. et al. A community-sourced glossary of open scholarship terms. Nat. human behaviour 6, 312–318
(2022).
22.
Pownall, M. et al. Embedding open and reproducible science into teaching: A bank of lesson plans and resources.
Scholarsh. Teach. Learn. Psychol. (2021).
23.
Pownall, M. et al. The impact of open and reproducible scholarship on students’ scientific literacy, engagement,
and attitudes towards science: A review and synthesis of the evidence, 10.31222/osf.io/9e526 (2022).
24.
Azevedo, F. et al. Introducing a framework for open and reproducible research training (FORRT). Preprint at
https://doi.org/10.31219/osf.io/bnh7p.
25. Jong, J. et al. Traumatic life experiences and religiosity in eight countries. Sci. data 7, 1–8 (2020).
26.
Azevedo, F. et al. An international investigation of the psychology of COVID-19. Behavioural and Social
Sciences at Nature Portfolio. Blog post available at https://go.nature.com/3JtupRd (2021).
27.
Postmes, T., Haslam, S. A. & Jans, L. A single-item measure of social identification: Reliability, validity, and
utility. Br. J. Soc. Psychol. 52, 597–617 (2013).
28.
de Zavala, A. G., Cichocka, A., Eidelson, R. & Jayawickreme, N. Collective narcissism and its social consequences.
J. Pers. Soc. Psychol. 97, 1074–1096 (2009).
11/44

29.
Malone, G. P., Pillow, D. R. & Osman, A. The general belongingness scale (GBS): Assessing achieved
belongingness. Pers. Individ. Differ. 52, 311–316 (2012).
30. Jost, J. T. The end of the end of ideology. Am. Psychol. 61, 651–670 (2006).
31.
Azevedo, F., Jost, J. T., Rothmund, T. & Sterling, J. Neoliberal ideology and the justification of inequality in
capitalist societies: Why social and economic dimensions of ideology are intertwined. J. Soc. Issues 75, 49–88
(2019).
32.
Azevedo, F., Jost, J. T. & Rothmund, T. “making america great again”: System justification in the us
presidential election of 2016. Transl. Issues Psychol. Sci. 3, 231 (2017).
33.
Azevedo, F. & Jost, J. T. The ideological basis of antiscientific attitudes: Effects of authoritarianism, conservatism,
religiosity, social dominance, and system justification. Group Process. & Intergroup Relations 24, 518–549
(2021).
34.
Pinquart, M. Correlates of subjective health in older adults: a meta-analysis. Psychol. Aging 16, 414–426
(2001).
35.
Adler, N. E., Epel, E. S., Castellazzo, G. & Ickovics, J. R. Relationship of subjective and objective social status
with psychological and physiological functioning: Preliminary data in healthy, white women. Heal. psychology
19, 586 (2000).
36.
Bjørnskov, C. How comparable are the gallup world poll life satisfaction data? J. Happiness Stud. 11, 41–60
(2010).
37.
Sjåstad, H. Short-sighted greed? focusing on the future promotes reputation-based generosity. Judgm. Decis.
Mak 14, 199–213 (2019).
38.
Curry, O. S., Chesters, M. J. & Van Lissa, C. J. Mapping morality with a compass: Testing the theory of
’morality-as-cooperation’ with a new questionnaire. J. Res. Pers. 78, 106–124 (2019).
39. Aquino, K. & Reed II, A. The self-importance of moral identity. J. Pers. Soc. Psychol. 83, 1423 (2002).
40.
Waytz, A., Iyer, R., Young, L., Haidt, J. & Graham, J. Ideological differences in the expanse of the moral circle.
Nat. Commun. 10, 1–12 (2019).
41.
Alfano, M. et al. Development and validation of a multi-dimensional measure of intellectual humility. PloS
ONE 12, e0182950 (2017).
42.
Scheier, M. F., Carver, C. S. & Bridges, M. W. Distinguishing optimism from neuroticism (and trait anxiety,
self-mastery, and self-esteem): A reevaluation of the life orientation test. J. Pers. Soc. Psychol. 67, 1063 (1994).
43.
Robins, R. W., Hendin, H. M. & Trzesniewski, K. H. Measuring global self-esteem: Construct validation of a
single-item measure and the rosenberg self-esteem scale. Pers. Soc. Psychol. Bull. 27, 151–161 (2001).
44.
Tangney, J. P., Baumeister, R. F. & Boone, A. L. High self-control predicts good adjustment, less pathology,
better grades, and interpersonal success. J. Pers. 72, 271–324 (2004).
45.
Back, M. D. et al. Narcissistic admiration and rivalry: Disentangling the bright and dark sides of narcissism. J.
Pers. Soc. Psychol. 105, 1013 (2013).
46.
Primi, C., Morsanyi, K., Chiesi, F., Donati, M. A. & Hamilton, J. The development and testing of a new version
of the cognitive reflection test applying item response theory (IRT). J. Behav. Decis. Mak. 29, 453–469 (2016).
47.
Graul, C. leafletR: Interactive Web-Maps Based on the Leaflet JavaScript Library (2016). R package version
0.4-0.
48. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).
49.
Kretzschmar, A. & Gignac, G. E. At what sample size do latent variable correlations stabilize? J. Res. Pers.
80, 17–22 (2019).
50.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical
Computing, Vienna, Austria (2019).
51. Wickham, H. & Hester, J. readr: Read Rectangular Text Data (2020). R package version 1.4.0.
52.
Wickham, H. & Miller, E. haven: Import and Export SPSS, Stata and SAS Files (2021). R package version
2.4.1.
53. Wickham, H. & Bryan, J. readxl: Read Excel Files (2019). R package version 1.3.1.
12/44

54.
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation (2021). R package
version 1.0.6.
55.
Revelle, W. psych: Procedures for Psychological, Psychometric, and Personality Research (2021). R package
version 2.1.6.
56.
Cheng, J., Sievert, C., Chang, W., Xie, Y. & Allen, J. htmltools: Tools for HTML (2021). R package version
0.5.1.
57. Xie, Y. mime: Map Filenames to MIME Types (2021). R package version 0.11.
58. Xie, Y. xfun: Supporting Functions for Packages Maintained by Yihui Xie (2021). R package version 0.25.
59. Larmarange, J. labelled: Manipulating Labelled Data (2021). R package version 2.9.0.
60. Lüdecke, D. sjlabelled: Labelled Data Utility Functions (2020). R package version 1.1.6.
61.
Arslan, R. codebook: Automatic Codebooks from Metadata Encoded in Dataset Attributes (2020). R package
version 0.9.2.
62.
Spinu, V., Grolemund, G. & Wickham, H. lubridate: Make Dealing with Dates a Little Easier (2021). R package
version 1.7.10.
Acknowledgements
The ICSMP consortium would like to acknowledge the additional contributions of numerous friends and collaborators
in translating and sharing the COVIDiSTRESS survey, even if contributions were small or the person did not wish
their name included as a member of the consortium.
Author contributions statement
Conceptualization: F.A. Data curation: F.A., T.P., W.M.S., and G.R. Formal analysis: F.A., F.C.A., T.P., T.E.,
and J.C.R. Investigation: F.A. Methodology: F.A. Project administration: F.A. Resources: F.A. Software: F.A. and
T.P. Supervision: F.A. Validation: F.A. and J.K. Visualization: F.A., F.C.A., T.E., H.F.C., L.C., C.L., and J.C.R.
Writing - original draft: F.A., B.G., and P.S. Writing - review editing: ICSMP Collaborators.
Competing interests
André Krouwel (ownership and stocks in Kieskompas BV, a data collector in this project). No payment was received
by the author. No other authors reported a competing interest.
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Figures & Tables
Figure 1. A world map visualizing the number of participants in each surveyed country.
Note: This heat map shows the number of respondents from each country. The gray areas are the countries that
are not covered by the data, and the colour scale shows the size of the sample in accordance with the scale on the
lower left side.
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Figure 2. Gantt Chart illustrating the data collection periods for each surveyed country
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Figure 3.a. International Collaboration on the Social and Moral Psychology of COVID-19: Investigated constructs,
items and variables
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Figure 3.b. International Collaboration on the Social and Moral Psychology of COVID-19: Investigated constructs,
items and variables
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Figure 4. Data quality indicators for each surveyed country
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Figure 5. Cross-cultural differences in Social & Moral Psychology of COVID-19 across 69 countries.
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Figure 6. Cross-cultural differences in associations of Social & Moral Psychology of COVID-19 across
Sociodemographics in 69 countries.
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(Figure 7.A)
(Figure 7.B)
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(Figure 7.C)
(Figure 7.D)
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(Figure 7.E)
Figure 7. Measurements for constructs
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Table 1. Sample size, average proportion of valid answers, age of respondents and the number of data collections in
69 countries
Note: Country = country names in accordance with ISO3 codes, N = number of respondents in each country. <50% and <90% = average
proportion of valid (non NA) answers that are below 0.5 and 0,.9 respectively in the subject level. µAge = mean age and sdAg e = standard
deviation of the age, Multiple datasets = whether there were multiple data collections in the country.
% Valid Answers Age Multiple datasets
Sample Country N <50% <90% µAge sdAge per country
AR Argentina 721 1.00 1.00 47.38 15.29 1
AU Australia 2161 1.00 1.00 46.92 17.59 1
AT Austria 1605 0.90 0.87 49.77 14.13 1
BD Bangladesh 596 0.82 0.67 31.90 10.89 1
BE Belgium 1159 1.00 1.00 46.29 18.67 1
BO Bolivia 29 1.00 1.00 43.41 12.98 1
BR1 Brazil 961 0.99 0.99 39.31 14.57 3
BR2 Brazil_2 1301 0.75 0.67 34.89 13.12 3
BR3 Brazil_3 6 1.00 1.00 40.33 13.14 3
BG Bulgaria 666 1.00 0.96 30.69 11.13 1
CAe Canada_english 792 1.00 1.00 42.70 17.39 2
CAf Canada_french 171 1.00 1.00 46.83 16.97 2
CL Chile 97 1.00 1.00 49.21 15.47 1
CN China 1030 1.00 1.00 43.24 14.02 1
CO Colombia 731 0.99 0.91 37.26 14.68 2
CO2 Colombia_2 546 1.00 1.00 44.91 15.16 2
CR Costa Rica 25 1.00 1.00 44.64 12.73 1
HR Croatia 515 1.00 1.00 45.91 14.56 1
CU Cuba 43 1.00 1.00 48.65 12.73 1
DK Denmark 566 1.00 1.00 48.69 17.54 1
DO Dominican Republic 36 1.00 1.00 40.39 12.46 1
EC Ecuador 148 1.00 1.00 40.63 11.98 1
SV El Salvador 28 1.00 1.00 46.43 11.51 1
FI Finland 698 0.99 0.98 38.64 13.77 1
FR France 1119 1.00 0.99 43.18 16.20 1
DE Germany 1587 1.00 1.00 49.58 16.14 1
GH Ghana 390 0.68 0.49 31.46 7.54 1
GR Greece 640 1.00 1.00 29.77 11.43 1
GT Guatemala 48 1.00 1.00 44.67 13.31 1
HN Honduras 24 1.00 1.00 39.25 14.30 1
HU Hungary 506 1.00 1.00 48.53 16.54 1
IN India 312 0.87 0.81 26.94 8.49 2
IN2 India_2 429 0.94 0.84 36.81 12.05 2
IQ Iraq 1142 0.57 0.48 31.03 14.13 1
IE Ireland 785 0.96 0.95 38.23 14.63 1
IL Israel 1253 1.00 1.00 41.13 15.25 1
IT1 Italy 998 0.99 0.99 46.41 16.26 2
IT2 Italy_2 284 1.00 1.00 47.35 18.07 2
JP Japan 1239 0.96 0.93 47.10 15.21 1
KR Korea 555 0.92 0.89 41.83 13.90 1
LV Latvia 1008 1.00 1.00 45.60 14.11 1
MK Macedonia 726 0.97 0.96 38.13 11.63 1
MX Mexico 804 0.94 0.93 47.81 13.89 2
MX2 Mexico_2 507 1.00 1.00 47.77 13.54 2
MA Morocco 812 0.81 0.71 31.95 12.27 1
NP Nepal 563 0.78 0.61 28.06 7.58 1
NL Netherlands 1297 1.00 0.99 49.63 16.83 1
Continued on next page
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% Valid Answers Age
Sample Country N <50% <90% µAge sdAge Multiple datasets
NZ New Zealand 510 1.00 1.00 45.76 17.62 1
NI Nicaragua 16 1.00 1.00 42.75 14.84 1
NG Nigeria 608 0.93 0.87 32.08 10.81 1
NO Norway 532 1.00 1.00 47.04 17.39 1
PK Pakistan 565 0.90 0.85 26.94 8.38 1
PA Panama 18 1.00 1.00 44.11 17.32 1
PY Paraguay 16 1.00 1.00 38.94 9.33 1
PE Peru 91 1.00 1.00 46.21 14.44 1
PH Philippines 524 0.98 0.96 36.74 12.27 1
PL Poland 1817 1.00 1.00 46.44 17.09 1
PR Puerto Rico 2 1.00 1.00 64.00 16.97 1
RO Romania 500 1.00 1.00 42.26 13.45 2
RO2 Romania_2 505 1.00 0.99 42.53 14.50 2
RU Russian Federation 558 1.00 1.00 45.02 15.46 1
SN Senegal 552 0.62 0.51 34.36 12.43 1
RS Serbia 1070 0.88 0.71 42.92 11.93 1
SG Singapore 564 0.96 0.92 43.06 13.73 1
SK Slovakia 1265 1.00 1.00 44.19 15.88 1
ZA South Africa 939 0.82 0.56 39.90 13.44 1
ES Spain 1090 1.00 0.99 46.01 13.68 1
SE Sweden 1568 1.00 1.00 52.90 15.42 1
CH Switzerland 1056 1.00 1.00 47.94 16.66 1
TW Taiwan 833 1.00 1.00 43.99 13.25 1
TR Turkey 1455 1.00 0.99 37.23 15.24 1
UA Ukraine 577 1.00 1.00 37.45 8.03 1
AE United Arab Emirates 313 0.71 0.59 31.77 8.59 1
GB United Kingdom 550 1.00 1.00 45.66 15.62 1
US United States of America 1506 1.00 0.99 44.23 16.60 1
UY Uruguay 49 1.00 1.00 52.88 13.70 1
VE Venezuela 96 1.00 1.00 46.53 12.97 1
Table 2. Distribution of sex in 69 countries
Note: Country = country names in accordance with ISO3 codes, % Female = Proportion of female respondents in the country, % Male =
proportion of male respondents, % Other = proportion of non-binary respondents and % NA = proportion of the unreported sex.
Country % Female % Male % Other % Unreported
Argentina 0.69 0.31 0.00 0.00
Australia 0.51 0.48 0.01 0.00
Austria 0.46 0.41 0.00 0.13
Bangladesh 0.37 0.31 0.01 0.31
Belgium 0.41 0.59 0.00 0.00
Bolivia 0.59 0.41 0.00 0.00
Brazil 0.49 0.50 0.01 0.01
Brazil_2 0.47 0.19 0.00 0.33
Brazil_3 0.83 0.17 0.00 0.00
Bulgaria 0.65 0.34 0.00 0.01
Canada_english 0.62 0.38 0.01 0.00
Canada_french 0.54 0.46 0.00 0.00
Chile 0.65 0.35 0.00 0.00
China 0.49 0.51 0.00 0.00
Colombia 0.62 0.37 0.00 0.01
Colombia_2 0.63 0.37 0.00 0.00
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Country % Female % Male % Other % Unreported
Costa Rica 0.36 0.64 0.00 0.00
Croatia 0.52 0.48 0.00 0.01
Cuba 0.51 0.49 0.00 0.00
Denmark 0.49 0.51 0.00 0.00
Dominican Republic 0.81 0.19 0.00 0.00
Ecuador 0.55 0.45 0.00 0.00
El Salvador 0.54 0.46 0.00 0.00
Finland 0.45 0.48 0.05 0.02
France 0.55 0.45 0.00 0.00
Germany 0.50 0.50 0.00 0.00
Ghana 0.26 0.53 0.00 0.22
Greece 0.35 0.65 0.00 0.00
Guatemala 0.44 0.56 0.00 0.00
Honduras 0.71 0.29 0.00 0.00
Hungary 0.52 0.48 0.00 0.00
India 0.42 0.38 0.02 0.18
India_2 0.31 0.59 0.01 0.10
Iraq 0.23 0.26 0.01 0.50
Ireland 0.63 0.31 0.00 0.05
Israel 0.51 0.49 0.00 0.00
Italy 0.50 0.49 0.00 0.00
Italy_2 0.66 0.33 0.00 0.01
Japan 0.48 0.46 0.00 0.06
Korea 0.42 0.48 0.00 0.10
Latvia 0.63 0.37 0.00 0.00
Macedonia 0.54 0.43 0.01 0.03
Mexico 0.39 0.53 0.00 0.07
Mexico_2 0.61 0.38 0.00 0.00
Morocco 0.52 0.47 0.01 0.00
Nepal 0.33 0.29 0.01 0.37
Netherlands 0.46 0.54 0.00 0.00
New Zealand 0.50 0.50 0.00 0.00
Nicaragua 0.62 0.38 0.00 0.00
Nigeria 0.49 0.51 0.00 0.00
Norway 0.53 0.46 0.00 0.00
Pakistan 0.46 0.40 0.00 0.14
Panama 0.67 0.33 0.00 0.00
Paraguay 0.88 0.12 0.00 0.00
Peru 0.45 0.55 0.00 0.00
Philippines 0.50 0.50 0.00 0.00
Poland 0.49 0.50 0.00 0.00
Puerto Rico 0.50 0.50 0.00 0.00
Romania 0.52 0.48 0.00 0.00
Romania_2 0.49 0.50 0.00 0.00
Russian Federation 0.53 0.47 0.00 0.00
Senegal 0.37 0.63 0.01 0.00
Serbia 0.53 0.19 0.00 0.28
Singapore 0.51 0.49 0.00 0.00
Slovakia 0.50 0.50 0.00 0.00
South Africa 0.51 0.17 0.00 0.31
Spain 0.33 0.67 0.00 0.00
Sweden 0.40 0.59 0.00 0.00
Switzerland 0.51 0.49 0.00 0.00
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Country % Female % Male % Other % Unreported
Taiwan 0.50 0.50 0.00 0.00
Turkey 0.51 0.49 0.00 0.00
Ukraine 0.52 0.47 0.00 0.00
United Arab Emirates 0.29 0.31 0.00 0.40
United Kingdom 0.51 0.49 0.00 0.00
United States of America 0.51 0.48 0.00 0.00
Uruguay 0.69 0.31 0.00 0.00
Venezuela 0.56 0.44 0.00 0.00
Table 3. Distribution of employment status in 69 countries
Note: Country = country names in accordance with ISO3 codes, % Full = Proportion of full time workers, % Part = proportion of part time
workers, % Unemp. = proportion of unemployed respondents, % Student = proportion of students, % Retired = proportion of retirees, % Other
= proportion of respondents who do not fit in the mentioned categories and % NA = proportion of the unreported employment status.
Country % Full % Part % Unemp. % Student % Retired % Other % Unreported
Argentina 0.45 0.15 0.02 0.08 0.08 0.22 0.00
Australia 0.36 0.18 0.11 0.05 0.23 0.07 0.00
Austria 0.36 0.13 0.02 0.05 0.12 0.20 0.13
Bangladesh 0.18 0.15 0.08 0.21 0.02 0.04 0.32
Belgium 0.28 0.04 0.03 0.25 0.25 0.14 0.00
Bolivia 0.52 0.14 0.07 0.07 0.00 0.21 0.00
Brazil 0.51 0.10 0.11 0.09 0.09 0.09 0.01
Brazil_2 0.25 0.08 0.06 0.16 0.04 0.08 0.33
Brazil_3 0.50 0.00 0.00 0.33 0.00 0.17 0.00
Bulgaria 0.37 0.06 0.06 0.24 0.01 0.23 0.03
Canada_english 0.41 0.12 0.09 0.11 0.18 0.09 0.00
Canada_french 0.00 0.00 0.63 0.05 0.25 0.08 0.00
Chile 0.40 0.16 0.04 0.04 0.07 0.28 0.00
China 0.73 0.01 0.01 0.05 0.20 0.00 0.00
Colombia 0.42 0.07 0.09 0.26 0.05 0.11 0.02
Colombia_2 0.40 0.15 0.04 0.12 0.07 0.22 0.00
Costa Rica 0.68 0.04 0.12 0.00 0.08 0.08 0.00
Croatia 0.48 0.03 0.16 0.05 0.24 0.05 0.00
Cuba 0.74 0.07 0.09 0.02 0.02 0.05 0.00
Denmark 0.41 0.07 0.07 0.10 0.29 0.07 0.00
Dominican Republic 0.56 0.14 0.08 0.11 0.03 0.08 0.00
Ecuador 0.57 0.10 0.06 0.07 0.05 0.14 0.00
El Salvador 0.68 0.07 0.07 0.04 0.00 0.14 0.00
Finland 0.44 0.08 0.09 0.19 0.08 0.10 0.02
France 0.55 0.07 0.07 0.08 0.18 0.05 0.00
Germany 0.37 0.13 0.05 0.07 0.29 0.09 0.00
Ghana 0.31 0.08 0.11 0.22 0.01 0.05 0.22
Greece 0.33 0.10 0.14 0.37 0.03 0.03 0.00
Guatemala 0.56 0.08 0.04 0.04 0.04 0.23 0.00
Honduras 0.46 0.38 0.08 0.04 0.00 0.04 0.00
Hungary 0.44 0.07 0.07 0.05 0.29 0.07 0.00
India 0.31 0.05 0.06 0.33 0.01 0.05 0.18
India_2 0.37 0.11 0.09 0.10 0.05 0.19 0.10
Iraq 0.09 0.08 0.09 0.17 0.01 0.04 0.50
Ireland 0.42 0.12 0.05 0.18 0.06 0.12 0.05
Israel 0.39 0.13 0.15 0.06 0.09 0.18 0.00
Italy 0.42 0.12 0.13 0.08 0.17 0.08 0.00
Italy_2 0.37 0.07 0.04 0.15 0.25 0.11 0.00
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Country % Full % Part % Unemp. % Student % Retired % Other % Unreported
Japan 0.44 0.12 0.16 0.05 0.10 0.06 0.06
Korea 0.49 0.12 0.06 0.08 0.06 0.09 0.10
Latvia 0.63 0.08 0.06 0.07 0.10 0.08 0.00
Macedonia 0.70 0.04 0.07 0.08 0.02 0.06 0.03
Mexico 0.45 0.12 0.08 0.03 0.10 0.16 0.07
Mexico_2 0.52 0.15 0.03 0.05 0.07 0.18 0.00
Morocco 0.38 0.09 0.12 0.29 0.03 0.09 0.01
Nepal 0.25 0.08 0.07 0.19 0.01 0.04 0.37
Netherlands 0.31 0.17 0.04 0.08 0.20 0.19 0.00
New Zealand 0.40 0.16 0.10 0.05 0.16 0.12 0.00
Nicaragua 0.44 0.25 0.06 0.00 0.06 0.19 0.00
Nigeria 0.30 0.14 0.17 0.18 0.01 0.06 0.13
Norway 0.45 0.09 0.03 0.06 0.20 0.15 0.00
Pakistan 0.24 0.05 0.07 0.43 0.01 0.06 0.14
Panama 0.50 0.00 0.11 0.06 0.11 0.22 0.00
Paraguay 0.62 0.38 0.00 0.00 0.00 0.00 0.00
Peru 0.49 0.21 0.07 0.08 0.07 0.09 0.00
Philippines 0.47 0.12 0.15 0.09 0.03 0.11 0.03
Poland 0.37 0.07 0.13 0.07 0.26 0.10 0.00
Puerto Rico 0.50 0.00 0.00 0.00 0.00 0.50 0.00
Romania 0.63 0.04 0.08 0.07 0.13 0.05 0.00
Romania_2 0.58 0.05 0.08 0.08 0.14 0.08 0.00
Russian Federation 0.26 0.20 0.23 0.05 0.24 0.02 0.00
Senegal 0.51 0.05 0.06 0.23 0.01 0.13 0.00
Serbia 0.49 0.03 0.06 0.05 0.05 0.00 0.33
Singapore 0.63 0.06 0.08 0.04 0.05 0.05 0.07
Slovakia 0.48 0.05 0.08 0.07 0.24 0.08 0.00
South Africa 0.39 0.06 0.04 0.05 0.04 0.10 0.31
Spain 0.54 0.07 0.09 0.05 0.13 0.11 0.00
Sweden 0.51 0.06 0.03 0.05 0.27 0.09 0.00
Switzerland 0.37 0.18 0.06 0.07 0.20 0.11 0.00
Taiwan 0.57 0.10 0.06 0.07 0.14 0.07 0.00
Turkey 0.37 0.07 0.11 0.20 0.10 0.16 0.00
Ukraine 0.61 0.14 0.12 0.02 0.02 0.09 0.00
United Arab Emirates 0.30 0.02 0.05 0.18 0.00 0.05 0.40
United Kingdom 0.40 0.17 0.11 0.05 0.17 0.10 0.00
United States of America 0.48 0.11 0.12 0.04 0.18 0.06 0.00
Uruguay 0.53 0.14 0.00 0.06 0.12 0.14 0.00
Venezuela 0.46 0.12 0.07 0.02 0.03 0.29 0.00
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Table 4. Distribution of marital status and number of children in 69 countries
Note: Country = country names in accordance with ISO3 codes, Columns 2-5 shows the proportion of different marital status, NA(MS) = unreported marital status, Columns 6-16 shows
proportion of respondents by the number of children they have and NA(Child.) = prop ortion of unreported number of children.
Marital Status Number of Children
Country Single Relation Married Unreported (MS) 0 1 2 3 4 ⩾4Unreported (Child.)
Argentina 0.29 0.27 0.44 0.00 0.37 0.16 0.26 0.14 0.05 0.02 0.00
Australia 0.37 0.15 0.48 0.00 0.44 0.15 0.24 0.10 0.04 0.02 0.00
Austria 0.20 0.24 0.43 0.13 0.32 0.17 0.23 0.11 0.03 0.01 0.13
Bangladesh 0.33 0.04 0.31 0.32 0.36 0.07 0.10 0.03 0.00 0.01 0.43
Belgium 0.37 0.26 0.36 0.00 0.57 0.12 0.19 0.08 0.03 0.01 0.00
Bolivia 0.38 0.10 0.52 0.00 0.41 0.14 0.28 0.10 0.07 0.00 0.00
Brazil 0.40 0.14 0.45 0.01 0.44 0.23 0.20 0.09 0.02 0.01 0.01
Brazil_2 0.24 0.21 0.22 0.33 0.45 0.10 0.09 0.03 0.00 0.00 0.33
Brazil_3 0.17 0.33 0.50 0.00 0.67 0.33 0.00 0.00 0.00 0.00 0.00
Bulgaria 0.40 0.37 0.21 0.02 0.67 0.16 0.13 0.01 0.00 0.01 0.02
Canada_english 0.40 0.21 0.39 0.00 0.57 0.15 0.16 0.08 0.03 0.01 0.00
Canada_french 0.49 0.22 0.29 0.00 0.58 0.16 0.17 0.06 0.02 0.01 0.00
Chile 0.38 0.18 0.44 0.00 0.32 0.12 0.27 0.20 0.07 0.02 0.00
China 0.11 0.05 0.84 0.00 0.20 0.74 0.06 0.00 0.00 0.00 0.00
Colombia 0.40 0.29 0.30 0.01 0.55 0.16 0.18 0.07 0.02 0.01 0.02
Colombia_2 0.32 0.21 0.47 0.00 0.39 0.16 0.27 0.12 0.03 0.02 0.00
Costa Rica 0.44 0.12 0.44 0.00 0.56 0.08 0.04 0.12 0.16 0.04 0.00
Croatia 0.21 0.14 0.61 0.04 0.34 0.17 0.34 0.10 0.02 0.02 0.01
Cuba 0.23 0.21 0.56 0.00 0.19 0.40 0.23 0.12 0.05 0.02 0.00
Denmark 0.28 0.26 0.46 0.00 0.39 0.16 0.31 0.09 0.02 0.02 0.00
Dominican Republic 0.44 0.25 0.31 0.00 0.50 0.28 0.14 0.08 0.00 0.00 0.00
Ecuador 0.36 0.16 0.48 0.00 0.43 0.18 0.26 0.09 0.03 0.03 0.00
El Salvador 0.39 0.11 0.50 0.00 0.36 0.25 0.21 0.14 0.00 0.04 0.00
Finland 0.37 0.34 0.27 0.02 0.63 0.11 0.15 0.05 0.02 0.03 0.02
France 0.35 0.30 0.35 0.00 0.58 0.20 0.15 0.05 0.01 0.01 0.00
Germany 0.38 0.19 0.43 0.00 0.48 0.19 0.23 0.08 0.01 0.01 0.00
Ghana 0.36 0.11 0.32 0.21 0.00 0.00 0.00 0.00 0.00 0.00 1.00
Greece 0.45 0.38 0.16 0.00 0.86 0.07 0.06 0.01 0.00 0.00 0.00
Guatemala 0.29 0.25 0.46 0.00 0.40 0.17 0.25 0.10 0.06 0.02 0.00
Honduras 0.38 0.17 0.46 0.00 0.58 0.12 0.00 0.04 0.17 0.08 0.00
Hungary 0.30 0.27 0.43 0.00 0.39 0.25 0.26 0.08 0.01 0.01 0.00
India 0.55 0.14 0.13 0.18 0.69 0.03 0.07 0.00 0.00 0.00 0.20
India_2 0.29 0.07 0.55 0.10 0.20 0.29 0.18 0.01 0.00 0.00 0.31
Iraq 0.26 0.04 0.20 0.50 0.30 0.03 0.05 0.04 0.03 0.03 0.52
Ireland 0.32 0.28 0.34 0.05 0.52 0.10 0.17 0.09 0.05 0.02 0.06
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Marital Status Number of Children
Country Single Relation Married Unreported (MS) 0 1 2 3 4 ⩾4Unreported (Child.)
Israel 0.24 0.11 0.55 0.09 0.38 0.12 0.20 0.18 0.06 0.05 0.00
Italy 0.26 0.25 0.49 0.00 0.44 0.25 0.25 0.05 0.01 0.00 0.00
Italy_2 0.23 0.30 0.46 0.00 0.49 0.20 0.25 0.06 0.00 0.00 0.00
Japan 0.35 0.05 0.54 0.06 0.46 0.14 0.23 0.08 0.02 0.00 0.07
Korea 0.35 0.07 0.49 0.10 0.44 0.16 0.25 0.03 0.01 0.00 0.10
Latvia 0.34 0.25 0.42 0.00 0.00 0.32 0.19 0.31 0.12 0.05 0.00
Macedonia 0.30 0.19 0.48 0.03 0.50 0.17 0.26 0.04 0.00 0.00 0.04
Mexico 0.26 0.18 0.49 0.07 0.34 0.13 0.25 0.14 0.04 0.03 0.07
Mexico_2 0.31 0.19 0.50 0.00 0.29 0.18 0.32 0.15 0.04 0.02 0.00
Morocco 0.57 0.09 0.33 0.01 0.70 0.09 0.10 0.06 0.01 0.01 0.02
Nepal 0.36 0.05 0.21 0.37 0.46 0.08 0.06 0.01 0.00 0.00 0.39
Netherlands 0.29 0.27 0.43 0.00 0.41 0.12 0.29 0.13 0.03 0.02 0.00
New Zealand 0.39 0.20 0.41 0.00 0.41 0.16 0.21 0.13 0.06 0.04 0.00
Nicaragua 0.19 0.25 0.56 0.00 0.25 0.12 0.25 0.19 0.12 0.06 0.00
Nigeria 0.42 0.11 0.34 0.13 0.51 0.10 0.12 0.08 0.03 0.02 0.13
Norway 0.32 0.26 0.42 0.00 0.41 0.15 0.24 0.16 0.03 0.01 0.00
Pakistan 0.51 0.10 0.24 0.14 0.66 0.07 0.07 0.02 0.01 0.01 0.15
Panama 0.33 0.17 0.50 0.00 0.44 0.11 0.28 0.11 0.00 0.06 0.00
Paraguay 0.56 0.31 0.12 0.00 0.44 0.06 0.31 0.06 0.12 0.00 0.00
Peru 0.40 0.14 0.46 0.00 0.35 0.20 0.29 0.13 0.01 0.02 0.00
Philippines 0.44 0.15 0.38 0.03 0.46 0.21 0.17 0.09 0.02 0.02 0.03
Poland 0.29 0.21 0.50 0.00 0.33 0.22 0.31 0.10 0.03 0.01 0.00
Puerto Rico 0.00 0.50 0.50 0.00 0.00 0.00 0.00 0.50 0.50 0.00 0.00
Romania 0.32 0.13 0.55 0.00 0.65 0.22 0.11 0.01 0.00 0.00 0.00
Romania_2 0.27 0.19 0.54 0.00 0.40 0.32 0.22 0.04 0.01 0.01 0.00
Russian Federation 0.41 0.15 0.44 0.00 0.39 0.28 0.26 0.05 0.01 0.01 0.00
Senegal 0.48 0.08 0.44 0.00 0.50 0.13 0.11 0.12 0.06 0.07 0.01
Serbia 0.19 0.15 0.38 0.28 0.28 0.16 0.21 0.05 0.01 0.01 0.29
Singapore 0.31 0.08 0.53 0.07 0.44 0.18 0.21 0.08 0.01 0.01 0.07
Slovakia 0.28 0.25 0.47 0.00 0.37 0.18 0.31 0.11 0.03 0.01 0.00
South Africa 0.23 0.16 0.30 0.32 0.32 0.11 0.16 0.07 0.01 0.01 0.32
Spain 0.24 0.27 0.49 0.00 0.49 0.17 0.27 0.06 0.01 0.00 0.00
Sweden 0.27 0.27 0.46 0.00 0.29 0.14 0.33 0.16 0.05 0.03 0.00
Switzerland 0.31 0.28 0.41 0.00 0.46 0.19 0.25 0.08 0.03 0.01 0.00
Taiwan 0.35 0.11 0.54 0.00 0.46 0.16 0.27 0.09 0.02 0.00 0.00
Turkey 0.37 0.06 0.57 0.00 0.42 0.12 0.26 0.11 0.03 0.05 0.00
Ukraine 0.19 0.12 0.62 0.07 0.33 0.39 0.24 0.03 0.01 0.00 0.00
United Arab Emirates 0.26 0.15 0.20 0.40 0.40 0.08 0.07 0.04 0.00 0.01 0.40
United Kingdom 0.33 0.24 0.42 0.00 0.50 0.15 0.24 0.07 0.03 0.01 0.00
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Marital Status Number of Children
Country Single Relation Married Unreported (MS) 0 1 2 3 4 ⩾4Unreported (Child.)
United States of America 0.41 0.11 0.48 0.00 0.47 0.18 0.23 0.08 0.02 0.02 0.00
Uruguay 0.29 0.14 0.57 0.00 0.27 0.22 0.37 0.08 0.06 0.00 0.00
Venezuela 0.28 0.23 0.49 0.00 0.31 0.19 0.30 0.15 0.05 0.00 0.00
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Table 5. Overview of the samples
Sample Coding Samples (Countries) N Samples N Respondents % Countries
% Respon-
dents
Quota-based nationally representative
AU, BR1, CAe, CAf, CH, CN,
DE, FR, HR, HU, IL, IT1, JP,
KR, LV, NG, NO, NZ, PH, PL,
RO, RU, SG, SK, TR, TW, GB,
US
28 26173 0.36 0.51
Post-hoc weights AT, DK, ES, NL, SE, UA 6 6703 0.08 0.13
Convenience
AE, BD, BE, BG, BR2, CO, FI,
GH, GR, IE, IN2, IN, IT2, IQ,
CO2, AR, CL, MX2, PE, VE, CR,
PY, BR3, EC, GT, UY, BO, SV,
PA, HN, CU, NI, DO, PR, MA,
MK, MX, NP, PK, RO2, RS, SN,
ZA
43 18528 0.56 0.36
Total 77 51404 1.00 1.00
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Table 6. Measures of variables for countries
Country Measure Conspiracy
beliefs
Morality
as
cooperation
Spatial
distancing
Collective
narcissism
National
identity
Policy
support
Austria
Cronbach’s
alpha
0.92 0.75 0.75 0.85 0.82 0.86
Austria Omega 0.93 0.75 0.76 0.85 0.87
Austria
Guttman’s
split-half
coefficient
0.94 0.85 0.82 0.76 0.82 0.87
Austria
Proportion of
variance ex-
plained
0.76 0.32 0.39 0.66 0.69 0.57
Australia
Cronbach’s
alpha
0.91 0.80 0.81 0.80 0.82 0.87
Australia Omega 0.92 0.79 0.82 0.80 0.87
Australia
Guttman’s
split-half
coefficient
0.93 0.87 0.85 0.71 0.82 0.86
Australia
Proportion of
variance ex-
plained
0.73 0.40 0.49 0.57 0.70 0.57
Bangladesh
Cronbach’s
alpha
0.87 0.50 0.59 0.71 0.67 0.82
Bangladesh Omega 0.87 0.57 0.62 0.72 0.82
Bangladesh
Guttman’s
split-half
coefficient
0.90 0.77 0.72 0.70 0.67 0.85
Bangladesh
Proportion of
variance ex-
plained
0.63 0.23 0.32 0.47 0.51 0.49
Belgium
Cronbach’s
alpha
0.92 0.65 0.72 0.78 0.87 0.85
Belgium Omega 0.93 0.63 0.73 0.79 0.85
Belgium
Guttman’s
split-half
coefficient
0.94 0.79 0.77 0.67 0.87 0.85
Belgium
Proportion of
variance ex-
plained
0.76 0.22 0.36 0.56 0.77 0.53
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Bulgaria
Cronbach’s
alpha
0.90 0.79 0.75 0.84 0.62 0.87
Bulgaria Omega 0.90 0.80 0.77 0.84 0.88
Bulgaria
Guttman’s
split-half
coefficient
0.91 0.85 0.82 0.75 0.62 0.87
Bulgaria
Proportion of
variance ex-
plained
0.70 0.37 0.42 0.63 0.45 0.60
Brazil
Cronbach’s
alpha
0.92 0.77 0.74 0.84 0.80 0.88
Brazil Omega 0.92 0.76 0.75 0.84 0.88
Brazil
Guttman’s
split-half
coefficient
0.93 0.86 0.80 0.75 0.80 0.86
Brazil
Proportion of
variance ex-
plained
0.74 0.36 0.39 0.64 0.66 0.61
Canada
Cronbach’s
alpha
0.90 0.76 0.73 0.72 0.83 0.91
Canada Omega 0.90 0.76 0.74 0.74 0.91
Canada
Guttman’s
split-half
coefficient
0.92 0.84 0.78 0.63 0.83 0.89
Canada
Proportion of
variance ex-
plained
0.70 0.35 0.37 0.50 0.71 0.66
Switzerland
Cronbach’s
alpha
0.93 0.76 0.78 0.83 0.83 0.90
Switzerland Omega 0.93 0.76 0.79 0.83 0.90
Switzerland
Guttman’s
split-half
coefficient
0.94 0.84 0.83 0.73 0.83 0.88
Switzerland
Proportion of
variance ex-
plained
0.76 0.35 0.44 0.63 0.71 0.65
China
Cronbach’s
alpha
0.89 0.79 0.60 0.50 0.53 0.78
China Omega 0.89 0.79 0.61 0.52 0.79
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
China
Guttman’s
split-half
coefficient
0.89 0.85 0.66 0.49 0.53 0.80
China
Proportion of
variance ex-
plained
0.67 0.36 0.26 0.28 0.36 0.43
Colombia
Cronbach’s
alpha
0.90 0.72 0.57 0.84 0.82 0.83
Colombia Omega 0.90 0.68 0.58 0.84 0.84
Colombia
Guttman’s
split-half
coefficient
0.91 0.85 0.67 0.76 0.82 0.83
Colombia
Proportion of
variance ex-
plained
0.69 0.32 0.25 0.64 0.70 0.51
Germany
Cronbach’s
alpha
0.93 0.81 0.77 0.86 0.82 0.89
Germany Omega 0.93 0.82 0.78 0.87 0.89
Germany
Guttman’s
split-half
coefficient
0.95 0.85 0.82 0.76 0.82 0.89
Germany
Proportion of
variance ex-
plained
0.77 0.41 0.43 0.68 0.70 0.62
Denmark
Cronbach’s
alpha
0.91 0.85 0.76 0.84 0.82 0.92
Denmark Omega 0.91 0.85 0.86 0.84 0.92
Denmark
Guttman’s
split-half
coefficient
0.93 0.89 0.87 0.74 0.82 0.91
Denmark
Proportion of
variance ex-
plained
0.73 0.46 0.59 0.64 0.70 0.71
Spain
Cronbach’s
alpha
0.90 0.66 0.65 0.90 0.84 0.89
Spain Omega 0.90 0.61 0.66 0.90 0.89
Spain
Guttman’s
split-half
coefficient
0.93 0.78 0.74 0.80 0.84 0.88
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Spain
Proportion of
variance ex-
plained
0.70 0.25 0.29 0.76 0.72 0.63
Finland
Cronbach’s
alpha
0.94 0.58 0.75 0.81 0.80 0.85
Finland Omega 0.94 0.47 0.76 0.81 0.85
Finland
Guttman’s
split-half
coefficient
0.95 0.74 0.80 0.74 0.80 0.85
Finland
Proportion of
variance ex-
plained
0.79 0.23 0.39 0.58 0.67 0.55
France
Cronbach’s
alpha
0.92 0.73 0.77 0.86 0.83 0.89
France Omega 0.92 0.72 0.77 0.86 0.89
France
Guttman’s
split-half
coefficient
0.93 0.83 0.81 0.76 0.83 0.85
France
Proportion of
variance ex-
plained
0.75 0.30 0.41 0.67 0.71 0.61
Greece
Cronbach’s
alpha
0.92 0.60 0.76 0.86 0.80 0.85
Greece Omega 0.92 0.65 0.77 0.86 0.86
Greece
Guttman’s
split-half
coefficient
0.92 0.69 0.80 0.77 0.80 0.86
Greece
Proportion of
variance ex-
plained
0.75 0.23 0.41 0.68 0.67 0.55
Croatia
Cronbach’s
alpha
0.92 0.79 0.76 0.89 0.63 0.90
Croatia Omega 0.93 0.79 0.78 0.89 0.90
Croatia
Guttman’s
split-half
coefficient
0.93 0.83 0.79 0.80 0.63 0.88
Croatia
Proportion of
variance ex-
plained
0.76 0.36 0.43 0.73 0.46 0.65
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Hungary
Cronbach’s
alpha
0.87 0.61 0.76 0.88 0.74 0.89
Hungary Omega 0.87 0.66 0.78 0.88 0.89
Hungary
Guttman’s
split-half
coefficient
0.89 0.83 0.81 0.81 0.74 0.88
Hungary
Proportion of
variance ex-
plained
0.63 0.32 0.43 0.71 0.59 0.62
Ireland
Cronbach’s
alpha
0.90 0.76 0.64 0.83 0.83 0.84
Ireland Omega 0.90 0.74 0.66 0.84 0.85
Ireland
Guttman’s
split-half
coefficient
0.91 0.87 0.72 0.73 0.83 0.86
Ireland
Proportion of
variance ex-
plained
0.69 0.36 0.31 0.63 0.71 0.54
Israel
Cronbach’s
alpha
0.86 0.81 0.72 0.84 0.89 0.88
Israel Omega 0.87 0.81 0.73 0.84 0.88
Israel
Guttman’s
split-half
coefficient
0.88 0.87 0.78 0.76 0.89 0.87
Israel
Proportion of
variance ex-
plained
0.63 0.38 0.37 0.64 0.81 0.60
India
Cronbach’s
alpha
0.81 0.77 0.65 0.79 0.61 0.79
India Omega 0.82 0.78 0.78 0.79 0.79
India
Guttman’s
split-half
coefficient
0.87 0.85 0.81 0.69 0.61 0.75
India
Proportion of
variance ex-
plained
0.53 0.34 0.47 0.56 0.44 0.44
Italy
Cronbach’s
alpha
0.93 0.74 0.79 0.90 0.86 0.92
Italy Omega 0.93 0.73 0.80 0.90 0.92
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Italy
Guttman’s
split-half
coefficient
0.93 0.82 0.82 0.79 0.86 0.91
Italy
Proportion of
variance ex-
plained
0.77 0.30 0.45 0.75 0.75 0.71
Japan
Cronbach’s
alpha
0.87 0.69 0.77 0.70 0.71 0.90
Japan Omega 0.88 0.69 0.78 0.72 0.90
Japan
Guttman’s
split-half
coefficient
0.91 0.78 0.80 0.63 0.71 0.88
Japan
Proportion of
variance ex-
plained
0.64 0.27 0.42 0.47 0.55 0.63
Korea
Cronbach’s
alpha
0.91 0.78 0.74 0.64 0.79 0.84
Korea Omega 0.92 0.78 0.75 0.68 0.84
Korea
Guttman’s
split-half
coefficient
0.93 0.86 0.79 0.53 0.79 0.83
Korea
Proportion of
variance ex-
plained
0.74 0.35 0.39 0.45 0.65 0.52
Iraq
Cronbach’s
alpha
0.94 0.77 0.68 0.78 0.83 0.87
Iraq Omega 0.94 0.76 0.74 0.78 0.87
Iraq
Guttman’s
split-half
coefficient
0.94 0.84 0.79 0.70 0.83 0.84
Iraq
Proportion of
variance ex-
plained
0.79 0.35 0.41 0.54 0.72 0.58
Latvia
Cronbach’s
alpha
0.90 0.76 0.73 0.86 0.74 0.89
Latvia Omega 0.90 0.76 0.76 0.86 0.89
Latvia
Guttman’s
split-half
coefficient
0.92 0.82 0.80 0.77 0.74 0.87
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Latvia
Proportion of
variance ex-
plained
0.69 0.32 0.43 0.68 0.59 0.62
Argentina
Cronbach’s
alpha
0.88 0.61 0.51 0.84 0.77 0.85
Argentina Omega 0.89 0.70 0.53 0.85 0.86
Argentina
Guttman’s
split-half
coefficient
0.90 0.83 0.64 0.76 0.77 0.84
Argentina
Proportion of
variance ex-
plained
0.66 0.31 0.22 0.65 0.62 0.55
Mexico
Cronbach’s
alpha
0.91 0.68 0.67 0.84 0.80 0.89
Mexico Omega 0.91 0.75 0.68 0.84 0.89
Mexico
Guttman’s
split-half
coefficient
0.92 0.80 0.76 0.76 0.80 0.88
Mexico
Proportion of
variance ex-
plained
0.72 0.32 0.35 0.63 0.67 0.62
Morocco
Cronbach’s
alpha
0.89 0.64 0.81 0.72 0.71 0.91
Morocco Omega 0.89 0.70 0.82 0.73 0.91
Morocco
Guttman’s
split-half
coefficient
0.89 0.78 0.85 0.65 0.71 0.90
Morocco
Proportion of
variance ex-
plained
0.67 0.29 0.48 0.47 0.55 0.68
Macedonia
Cronbach’s
alpha
0.93 0.78 0.72 0.87 0.89 0.86
Macedonia Omega 0.93 0.78 0.73 0.87 0.87
Macedonia
Guttman’s
split-half
coefficient
0.93 0.85 0.77 0.81 0.89 0.87
Macedonia
Proportion of
variance ex-
plained
0.77 0.34 0.36 0.69 0.80 0.57
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Nigeria
Cronbach’s
alpha
0.85 0.78 0.69 0.77 0.60 0.90
Nigeria Omega 0.85 0.78 0.70 0.77 0.90
Nigeria
Guttman’s
split-half
coefficient
0.88 0.83 0.78 0.69 0.60 0.87
Nigeria
Proportion of
variance ex-
plained
0.59 0.36 0.35 0.52 0.43 0.65
Netherlands
Cronbach’s
alpha
0.93 0.69 0.67 0.81 0.77 0.82
Netherlands Omega 0.93 0.67 0.69 0.82 0.83
Netherlands
Guttman’s
split-half
coefficient
0.94 0.82 0.76 0.74 0.77 0.83
Netherlands
Proportion of
variance ex-
plained
0.76 0.27 0.32 0.60 0.63 0.50
Norway
Cronbach’s
alpha
0.96 0.67 0.65 0.79 0.82 0.87
Norway Omega 0.96 0.68 0.67 0.80 0.87
Norway
Guttman’s
split-half
coefficient
0.96 0.86 0.75 0.69 0.82 0.86
Norway
Proportion of
variance ex-
plained
0.85 0.33 0.31 0.58 0.69 0.58
Nepal
Cronbach’s
alpha
0.89 0.83 0.67 0.67 0.75 0.89
Nepal Omega 0.89 0.83 0.70 0.67 0.89
Nepal
Guttman’s
split-half
coefficient
0.91 0.88 0.76 0.60 0.75 0.86
Nepal
Proportion of
variance ex-
plained
0.67 0.42 0.36 0.41 0.59 0.62
New Zealand
Cronbach’s
alpha
0.92 0.79 0.79 0.81 0.80 0.89
New Zealand Omega 0.92 0.79 0.80 0.81 0.89
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
New Zealand
Guttman’s
split-half
coefficient
0.93 0.86 0.82 0.71 0.80 0.87
New Zealand
Proportion of
variance ex-
plained
0.74 0.38 0.45 0.59 0.66 0.63
Philippines
Cronbach’s
alpha
0.90 0.84 0.72 0.77 0.83 0.88
Philippines Omega 0.90 0.84 0.73 0.77 0.88
Philippines
Guttman’s
split-half
coefficient
0.92 0.89 0.79 0.66 0.83 0.86
Philippines
Proportion of
variance ex-
plained
0.69 0.46 0.37 0.53 0.71 0.59
Pakistan
Cronbach’s
alpha
0.90 0.75 0.73 0.79 0.86 0.83
Pakistan Omega 0.90 0.75 0.74 0.79 0.84
Pakistan
Guttman’s
split-half
coefficient
0.90 0.83 0.80 0.72 0.86 0.80
Pakistan
Proportion of
variance ex-
plained
0.68 0.34 0.37 0.55 0.76 0.51
Poland
Cronbach’s
alpha
0.89 0.74 0.82 0.86 0.89 0.89
Poland Omega 0.90 0.68 0.83 0.86 0.90
Poland
Guttman’s
split-half
coefficient
0.91 0.85 0.85 0.77 0.89 0.89
Poland
Proportion of
variance ex-
plained
0.68 0.32 0.51 0.67 0.79 0.64
Romania
Cronbach’s
alpha
0.92 0.64 0.73 0.86 0.80 0.89
Romania Omega 0.92 0.67 0.75 0.86 0.89
Romania
Guttman’s
split-half
coefficient
0.93 0.81 0.81 0.76 0.80 0.89
Continued on next page
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Romania
Proportion of
variance ex-
plained
0.75 0.29 0.42 0.67 0.66 0.62
Serbia
Cronbach’s
alpha
0.92 0.74 0.74 0.87 0.70 0.84
Serbia Omega 0.92 0.74 0.75 0.87 0.85
Serbia
Guttman’s
split-half
coefficient
0.92 0.83 0.78 0.81 0.70 0.86
Serbia
Proportion of
variance ex-
plained
0.74 0.29 0.39 0.69 0.54 0.54
Russian Federation
Cronbach’s
alpha
0.87 0.76 0.80 0.88 0.68 0.89
Russian Federation Omega 0.87 0.76 0.81 0.89 0.89
Russian Federation
Guttman’s
split-half
coefficient
0.90 0.84 0.85 0.81 0.68 0.88
Russian Federation
Proportion of
variance ex-
plained
0.64 0.32 0.46 0.72 0.51 0.63
Sweden
Cronbach’s
alpha
0.91 0.64 0.75 0.90 0.66 0.80
Sweden Omega 0.91 0.68 0.76 0.90 0.81
Sweden
Guttman’s
split-half
coefficient
0.93 0.83 0.81 0.79 0.66 0.82
Sweden
Proportion of
variance ex-
plained
0.72 0.31 0.39 0.76 0.50 0.46
Singapore
Cronbach’s
alpha
0.93 0.79 0.81 0.72 0.89 0.84
Singapore Omega 0.93 0.79 0.82 0.73 0.85
Singapore
Guttman’s
split-half
coefficient
0.94 0.87 0.85 0.64 0.89 0.85
Singapore
Proportion of
variance ex-
plained
0.76 0.38 0.48 0.49 0.80 0.53
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Slovakia
Cronbach’s
alpha
0.91 0.79 0.77 0.77 0.86 0.85
Slovakia Omega 0.91 0.79 0.78 0.77 0.85
Slovakia
Guttman’s
split-half
coefficient
0.93 0.85 0.82 0.68 0.86 0.85
Slovakia
Proportion of
variance ex-
plained
0.71 0.35 0.42 0.53 0.75 0.54
Senegal
Cronbach’s
alpha
0.88 0.71 0.64 0.77 0.78 0.85
Senegal Omega 0.89 0.71 0.66 0.78 0.86
Senegal
Guttman’s
split-half
coefficient
0.90 0.80 0.75 0.69 0.78 0.85
Senegal
Proportion of
variance ex-
plained
0.66 0.27 0.32 0.54 0.69 0.57
Turkey
Cronbach’s
alpha
0.92 0.76 0.71 0.90 0.93 0.88
Turkey Omega 0.92 0.76 0.72 0.90 0.89
Turkey
Guttman’s
split-half
coefficient
0.93 0.83 0.78 0.79 0.93 0.85
Turkey
Proportion of
variance ex-
plained
0.74 0.32 0.35 0.75 0.87 0.61
Taiwan
Cronbach’s
alpha
0.88 0.84 0.70 0.75 0.78 0.81
Taiwan Omega 0.89 0.84 0.72 0.75 0.81
Taiwan
Guttman’s
split-half
coefficient
0.93 0.89 0.77 0.68 0.78 0.85
Taiwan
Proportion of
variance ex-
plained
0.67 0.43 0.37 0.50 0.64 0.47
Ukraine
Cronbach’s
alpha
0.86 0.80 0.81 0.88 0.80 0.87
Ukraine Omega 0.87 0.80 0.81 0.88 0.87
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Country Measure Conspiracy beliefs
Morality as
cooperation
Spatial distancing
Collective
narcissism
National identity
Policy sup-
port
Ukraine
Guttman’s
split-half
coefficient
0.89 0.87 0.84 0.80 0.80 0.87
Ukraine
Proportion of
variance ex-
plained
0.64 0.37 0.48 0.71 0.66 0.59
United Kingdom
Cronbach’s
alpha
0.89 0.73 0.71 0.84 0.85 0.85
United Kingdom Omega 0.89 0.71 0.72 0.84 0.86
United Kingdom
Guttman’s
split-half
coefficient
0.92 0.87 0.79 0.72 0.85 0.86
United Kingdom
Proportion of
variance ex-
plained
0.67 0.34 0.34 0.65 0.75 0.55
United States of America
Cronbach’s
alpha
0.92 0.82 0.80 0.86 0.77 0.91
United States of America Omega 0.92 0.83 0.81 0.86 0.91
United States of America
Guttman’s
split-half
coefficient
0.93 0.88 0.84 0.78 0.77 0.89
United States of America
Proportion of
variance ex-
plained
0.75 0.42 0.49 0.68 0.62 0.68
South Africa
Cronbach’s
alpha
0.92 0.80 0.68 0.84 0.80 0.86
South Africa Omega 0.92 0.80 0.69 0.84 0.86
South Africa
Guttman’s
split-half
coefficient
0.94 0.88 0.73 0.73 0.80 0.85
South Africa
Proportion of
variance ex-
plained
0.74 0.40 0.32 0.64 0.67 0.55
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