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. 2021 Jan 4;8:3. doi: 10.1038/s41597-020-00784-9

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

Yuki Yamada 1,✉, Dominik-Borna Ćepulić 2, Tao Coll-Martín 3, Stéphane Debove 4, Guillaume Gautreau 5, Hyemin Han 6, Jesper Rasmussen 7, Thao P Tran 8, Giovanni A Travaglino 9; COVIDiSTRESS Global Survey Consortium, Andreas Lieberoth 7,✉
PMCID: PMC7782539  PMID: 33398078

Abstract

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.

Subject terms: Psychology, Human behaviour

Measurement(s) psychological measurement • anxiety-related behavior trait • Stress • response to • Isolation • loneliness measurement • Emotional Distress
Technology Type(s) Survey
Factor Type(s) geographic location • language • age of participant • responses to the Coronavirus pandemic
Sample Characteristic - Organism Homo sapiens
Sample Characteristic - Location global
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Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13251776

Background & Summary

In 2020, a new coronavirus pandemic spread across countries worldwide. This resulted not only in a global health crisis, but also in severe economic and socio-psychological consequences. To control the spread of the coronavirus, governments imposed a range of measures, including the closure of schools, workplaces, shopping areas and public amenities, forced isolation, virus-testing, and limits to civil liberties. Inevitably, these changes generated a variety of psychological responses in individuals, which in turn shaped the level of compliance with preventive measures. In fact, extant research on the factors that shape willingness to comply with public health efforts aimed at preventing or slowing the spread of epidemics has highlighted the importance of psychological and social factors1,2—for instance shared trust in state or health authorities3,4—in driving compliance with guidelines and restrictions. The implications of these complex factors to compliance with preventive measures imposed by different governments must be analysed in detail after the crisis. Indeed, the psychological and societal effects are likely to be more pronounced, more widespread, and longer-lasting than the purely somatic effects of the infection5.

To contribute to the understanding of the intersection between pandemic-related physical and behavioural issues, the present document describes a large-scale dataset collected through the collaborative COVIDiSTRESS global survey. The COVIDiSTRESS data collection efforts ran from 30th March to 30th May, 2020 by collaborators from 39 countries and regions with survey forms available in 47 languages and dialects. In total, 173,426 participants were recruited from 179 countries on six continents.

Pandemic outbreaks breed misinformation, and foster fear of contagion as well as uncertainty during the course of their spread5,6. Factors such as concerns regarding the severity of a disease, the perceived reliability of government information, and beliefs in the efficacy of preventive measures can influence individuals’ intentions to comply and engage in preventive behaviours7. Thus, the extent of compliance is influenced by the level of trust in one’s sources of information about a pandemic, as well as the perceived gravity of the disease. Concerns over one’s risk of contracting the disease during a pandemic can be a source of ongoing worry and anxiety as well as stress (e.g. H1N17 and MERS8). These concerns, as well as the confusion generated by the lack of established worldwide or national quarantine protocols, timely information and resources from public health systems9 may contribute to lower levels of compliance. Research indicates that the perception of openness and reliability of governments and health organisations10, levels of trust in media and medical authorities11,12 as well as perceptions of disease’s severity and the efficacy of one’s actions10,13,14 contribute to compliance with recommendations for preventive behaviour.

Both the medical situation and the psychological effects of isolation, confinement and information behavior15,16 need to be considered when prolonged periods of quarantine are implemented. A subset of negative effects on ‘cabin fever’ includes responses varying from anxiety and depression17 to impaired cognitive ability and hostility16,18. Efforts such as closing down schools and workplaces, and calls for people to self-isolate in their homes, are likely to constitute a source of both existential and practical stress unrelated to the fear of contracting the disease. Compliance with medical guidelines has been shown to decrease not just as a result of higher stress levels19, but also of minor everyday stressors such as workplace conflict or household responsibilities20. Prolonged states of emergency and the chronic psychological, social, and economic stressors related to them21,22 may decrease compliance with set behavioural objectives during pandemics. Conversely, social support from groups such as one’s family, friends, and colleagues moderate the effect of concern for the disease or other sources of stress on one’s psychological well-being23,24.

Hence, as an effort to help health authorities and decision makers organize informed responses, we initiated the COVIDiSTRESS open science collaboration. The dataset can help researchers and stakeholders identify nuances in psychological and behavioural risk factors in the context of the COVID-19 pandemic, and assist governments and other organizations in adopting constructive policies appropriate to each country.

Methods

Participants

173,426 people accessed an online survey link to provide their experiences over a period of 62 days (30th March to 30th May. The stored dataset represents 125,306 people who met inclusion criteria (18 years of age and older and gave informed consent). Demographic characteristics for countries with over 200 responses appear in Table 1. Given the urgent call for COVID-19 research, the survey received a waiver to commence data collection from the IRB office at Aarhus University, Denmark. Participants volunteered based on online and media appeals without monetary compensation; excepting some of the Japanese participants received 7 T-points (equivalent to about 0.065 USD) from the crowdsourcing service as a reward.

Table 1.

Sample size, proportions of valid data, age mean and standard deviation across countries with more than 200 participants.

Country N Prop_50 Prop_90 M_age SD_age
Finland 22933 0.854 0.804 43.357 14.170
France 13475 0.833 0.778 33.267 12.760
Denmark 10891 0.817 0.754 42.543 14.277
Mexico 9169 0.791 0.722 37.453 13.830
Lithuania 8255 0.796 0.720 38.553 12.459
Argentina 5923 0.711 0.598 41.593 15.244
Japan 5072 0.910 0.875 44.369 11.312
Bulgaria 4785 0.780 0.675 41.636 13.510
Poland 3088 0.779 0.694 31.315 7.883
Sweden 3055 0.825 0.764 46.477 12.373
Croatia 2965 0.807 0.739 35.408 12.247
Taiwan 2745 0.830 0.752 33.072 11.332
Kosovo 2707 0.615 0.468 29.225 10.058
United States 2314 0.832 0.783 42.857 14.714
Czech Republic 1995 0.787 0.720 33.375 11.506
Italy 1749 0.805 0.723 44.747 15.311
Indonesia 1569 0.723 0.616 31.047 9.572
United Kingdom 1500 0.775 0.701 39.438 12.814
Germany 1443 0.814 0.758 36.711 12.055
Hungary 1438 0.743 0.654 49.022 15.133
Netherlands 1433 0.800 0.748 44.944 14.730
Bosnia and Herzegovina 1288 0.780 0.661 37.256 11.972
Turkey 1199 0.760 0.667 33.533 11.809
Switzerland 1188 0.810 0.757 42.698 17.172
Portugal 1067 0.712 0.630 33.767 13.598
Slovakia 942 0.741 0.667 41.879 12.903
Panama 759 0.735 0.632 39.486 14.635
Brazil 731 0.778 0.703 35.259 13.748
Greece 642 0.822 0.745 41.785 11.622
Belgium 622 0.826 0.756 36.466 12.827
Spain 615 0.761 0.676 38.787 15.405
Philippines 570 0.849 0.777 25.853 11.424
Malaysia 567 0.769 0.709 36.795 14.411
Korea, South 487 0.764 0.671 38.053 10.427
Canada 470 0.811 0.760 41.349 14.540
Bangladesh 421 0.675 0.523 28.088 6.230
Pakistan 360 0.631 0.511 27.053 8.728
Australia 327 0.807 0.749 42.648 13.963
Austria 319 0.743 0.661 38.473 11.717
Romania 282 0.699 0.638 34.053 9.479
Serbia 266 0.816 0.688 38.556 12.651
Ireland 216 0.769 0.667 40.565 10.536
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Note.

N = number of participants; Prop = proportion.

Prop_50 = proportion of participants that have more than 50% of non-missing data.

Prop_90 = proportion of participants that have more than 90% of non-missing data.

M_age = mean age; SD_age = standard deviation of age.

Materials

The full survey form in English can be accessed at 10.17605/OSF.IO/Z39US. The survey consisted of two parts. The first section comprised general demographic data, self-reports about the proximate effects of the COVID-19 pandemic (e.g. isolation status, first-hand experience, attenuated risk), modified version of the Asian Disease problem to examine participants’ risk taking intention under COVID-19 situation25, personality assessment (BFI-S26), Short self-report scale of loneliness27 (SLON-3) based on the UCLA loneliness scale, Perceived Stress Scale (PSS-1028), self-reports about the interpersonal and institutional trust (based on OECD guidelines 2017), and items measuring daily behaviours including compliance with general and social preventive measures. The second part contained sets of more specific items related to people’s experiences of distress and worry during the ongoing outbreak of coronavirus (e.g. access to amenities, loss of work, adapting work, education and social interactions to digital platforms, the social stresses of confinement with adults and children), as well as items which detected copying mechanisms of people during the COVID-19 crisis (e.g. social contact, staying informed, dedicating oneself to preparation, hobbies, religion) and the Social Provisions Scale (SPS-1029). Finally, participants were asked to report information behaviours in times of the coronavirus pandemic, and were invited to add a few lines of text, to illuminate their experience of the COVID-19 crisis beyond the closed-end items. Participants typically supplied their answers on a 6-point Likert scale ranging from ‘Strongly disagree’ to ‘Strongly agree’, with some variation based on established standards, as well as in text boxes to add other relevant factors. Validated short versions of established measures were used if available in local languages. The full list of variables included in the COVIDiSTRESS global survey as well as the response options participants used to answer the survey are available at https://osf.io/v68t9/. To protect participants’ data and avoid sensitive information, participants were not asked about COVID-19 symptoms or other aspects of their medical status. Additionally, no data that would allow identification of participants was collected.

Translation

The survey was translated into 47 languages and adapted to the dialects and vernacular of different regions (Afrikaans, Albanian, Arabic, Bangla, Indonesian, Bosnian, Bulgarian, Chinese [Simplified and Traditional], Croatian, Czech, Danish, Dutch [Belgium, Netherlands], English, Spanish [Argentina, Colombia, Cuba, Mexico, Spain], Filipino, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, isiXhosa, isiZulu, Italian, Japanese, Korean, Lithuanian, Nepali, Persian, Polish, Portuguese [Brazil, Portugal], Romanian, Russian, Slovakian, Serbian, Swedish, Turkish, Urdu, Vietnamese). The translations were completed by a forward translator from the original English version, and then validated through both panel and back-translation processes by separate translators when possible.

Data cleaning

Along with the original data file (COVIDiSTRESS global survey May 30 2020 (choice text).csv), we provide a cleaned data file (COVIDiSTRESS_May_30_cleaned_final.csv) where some cases were removed, and the issues regarding the coding of certain answers were corrected. R code used to clean the data is available online at the Open Science Framework (COVIDiSTRESS global survey30) and in supplementary information. The corrections made were:

  • Filtered out cases without consent and younger than 18 years old.

  • User Language – Bulgarian (BG): For responses between 2020-03-28 13:30:02 UTC and 2020-04-08 01:53:18 UTC, the order of the variable Country was mixed up for people who took the survey in Bulgarian language. Thus, the data was recoded.

  • User Language – Afrikaans (AFR): For responses before 2020-04-07 06:48:00, the order of the variable Country was mixed up for people who took the survey in Afrikaans language. Thus, the data was recoded.

  • User Language – Hebrew (HE): The variable Country was translated and arranged according to the Hebrew alphabetical order. Thus, the data was recoded.

  • User Language – Bengali (BAN): Variables Scale_PSS10_UCLA_6 and Scale_PSS10_UCLA_7 were swapped during translation, so they were swapped back in the data cleaning procedure.

  • Country: Removed dashes in front of the ‘- other’ responses in Country.

  • Start Date: Cases before the official launch date 2020-03-30 were excluded as they were test answers. Soft launch answers from Denmark and Kosovo before the start date were retained.

  • Marital Status: Except for the original English version of the survey, the order of the Dem_maritalstatus variable was mixed up in translations. The variable was recoded to correct this problem. There were some participants who had ‘5’ in Dem_maritalstatus. These responses were recoded as ‘Uninformative response’.

  • Education level and mother’s education level: Removed dashes in front of the response options. There were some participants who had ‘1’ in Dem_edu. These responses were recoded as ‘Uninformative response’.

  • Gender: The variable Dem_gender was inverted for languages SSP (Spanish - Spain) and SME (Spanish - Mexico) in the raw data file. Thus, in these responses, Male was recorded to Female and vice versa.

  • AD_Check, AD_gain and AD_loss: Shorten the response; PSS-10, Corona_concerns, Compliance, BFF, SPS-10, Coping, Expl_media, Distress scale, Trust in the country’s measures: Responses were converted from choice text to numeric.

  • Perceived Loneliness: The scale was initially coded as an extension of the PSS-10 battery. For clarity, the columns were renamed into Scale_PSS10_UCLA_11 through Scale_PSS10_UCLA_13 to Scale_Lon_1 through Scale_Lon_3.

  • Created composite scores: PSS-10, SPS-10, SLON-3, BFF-15.

  • Removed all new lines and “;” from participants’ additional text responses.

  • From 15th May onwards, additional items (Q50-Q62) were included for a location-specific sub-study on war trauma in Bosnia/Herzegovina. These were not part of our pre-registration. These columns were cleaned (see below), but not included in the current report:

  • Renamed new columns for clarity (Q50-Q62): born_92, experience_war, experience_war_TXT, war_injury, loss_during_war, time_spent_in_war, time_spent_in_war_TXT, Scale_UCLA_TRI_1:4 (4 items), PS_PTSD_1:5 (5 items)

  • War-related questions: Removed numbers, periods, and extra spaces in the responses for the experience_war, war_injury, loss_during_war, time_spent_in_war (i.e. “2. Yes” got simplified to “Yes”)

  • TRI_4: Responses were converted from choice text to numeric and composite score for the scale was calculated

  • PS-PTSD: Responses were converted from choice text to numeric

Note that correcting the error-coded variables (Gender, User Language Bulgarian, Afrikaans and Hebrew, Marital Status) is necessary for correct interpretation of the data. None of the other actions described above (e.g., recoding text into numerical values) affect the data interpretation in any way. Apart from filtering out test data (data before the official launch on 2020-03-30) and participants who declared that they are younger than 18, all data was retained. When recoding, all groups present in the raw data file were also preserved. For more details, please see the data cleaning R markdown file. Thereafter, the text description is based on the cleaned data.

Data Records

Raw data and code for cleaning is available at 10.17605/OSF.IO/Z39US30. Figure 1 shows a heat map of the countries from which the data were collected, coloured according to the sample size (n ≥ 200). The main characteristics of the survey are presented in Tables 1 to 6. Information on the basics (Table 1), gender (Table 2), education (Table 3), marital status (Table 4), current risk of infection (Table 5), and current isolation status (Table 6) for countries with their sample size of more than 200 are presented, respectively.

Fig. 1.

Fig. 1

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A world map visualizing the participants in each country. Only countries with n ≥ 200 are coloured.

Table 6.

Proportion of current isolation status across countries with more than 200 participants.

Country Prop_usual Prop_minor Prop_medical Prop_isolated Prop_NA
Finland 0.034 0.604 0.001 0.355 0.006
France 0.046 0.643 0.001 0.302 0.009
Denmark 0.020 0.658 0.001 0.318 0.004
Mexico 0.023 0.322 0.003 0.644 0.007
Lithuania 0.033 0.761 0.001 0.196 0.008
Argentina 0.024 0.320 0.001 0.625 0.029
Japan 0.474 0.513 0.001 0.011 0.002
Bulgaria .031 0.399 0.001 0.537 0.031
Poland 0.012 0.450 0.001 0.524 0.013
Sweden 0.027 0.735 0 0.234 0.003
Croatia .018 0.677 0.000 0.282 0.023
Taiwan 0.182 0.805 0.001 0.009 0.003
Kosovo 0.029 0.327 0.007 0.525 0.113
United States 0.016 0.380 0.001 0.597 0.005
Czech Republic 0.022 0.788 0 0.187 0.003
Italy 0.023 0.613 0.002 0.341 0.021
Indonesia 0.055 0.595 0.001 0.342 0.008
United Kingdom 0.020 0.400 0.001 0.572 0.007
Germany 0.030 0.586 0 0.366 0.017
Hungary 0.032 0.592 0.001 0.336 0.038
Netherlands 0.023 0.671 0 0.290 0.016
Bosnia and Herzegovina 0.041 0.580 0.003 0.355 0.021
Turkey 0.013 0.257 0.004 0.691 0.035
Switzerland .031 0.642 0.001 0.318 0.008
Portugal 0.014 0.340 0.001 0.620 0.024
Slovakia 0.034 0.695 0 0.262 0.008
Panama 0.082 0.617 0.004 0.278 0.020
Brazil 0.008 0.283 0.003 0.699 0.007
Greece 0.012 0.343 0.005 0.625 0.016
Belgium 0.034 0.559 0.002 0.381 0.024
Spain 0.039 0.353 0.002 0.592 0.015
Philippines 0.121 0.761 0.002 0.111 0.005
Malaysia 0.093 0.670 0.002 0.229 0.005
Korea, South 0.230 0.743 0 0.018 0.008
Canada 0.015 0.396 0.004 0.579 0.006
Bangladesh 0.081 0.584 0.029 0.302 0.005
Pakistan 0.072 0.461 0.008 0.456 0.003
Australia 0.043 0.529 0 0.422 0.006
Austria 0.025 0.467 0 0.498 0.009
Romania 0.074 0.553 0 0.330 0.043
Serbia 0.030 0.519 0 0.440 0.011
Ireland 0.009 0.417 0 0.551 0.023
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Note.

Prop_usual = proportion of participants whose life carries on as usual.

Prop_minor = proportion of participants whose life carries on with minor changes.

Prop_medical = proportion of participants who are isolated in medical facility or similar location.

Prop_isolated = proportion of participants who are isolated.

Prop_NA = proportion of missing data for the isolation variable.

Table 2.

Proportion of each gender across countries with more than 200 participants.

Country Prop_female Prop_male Prop_gender_other/not_say Prop_gender_NA
Finland 0.813 0.167 0.018 0.002
France 0.510 0.472 0.016 0.002
Denmark 0.783 0.211 0.004 0.002
Mexico 0.720 0.270 0.006 0.005
Lithuania 0.751 0.242 0.006 0.001
Argentina 0.837 0.151 0.010 0.002
Japan 0.445 0.0.541 0.013 0.001
Bulgaria 0.807 0.172 0.019 0.002
Poland 0.867 0.125 0.008 0
Sweden 0.755 0.233 0.010 0.002
Croatia 0.783 0.212 0.003 0.002
Taiwan 0.700 0.273 0.026 0.001
Kosovo 0.629 0.356 0.011 0.004
United States 0.758 0.221 0.019 0.002
Czech Republic 0.782 0.212 0.006 0.001
Italy 0.761 0.229 0.007 0.003
Indonesia 0.671 0.307 0.016 0.006
United Kingdom 0.766 0.227 0.005 0.001
Germany 0.681 0.302 0.015 0.003
Hungary 0.707 0.287 0.003 0.003
Netherlands 0.738 0.251 0.008 0.0.003
Bosnia and Herzegovina 0.746 0.242 0.005 0.007
Turkey 0.746 0.239 0.013 0.002
Switzerland 0.608 0.382 0.003 0.007
Portugal 0.858 0.138 0.003 0.002
Slovakia 0.753 0.240 0.007 0
Panama 0.752 0.233 0.007 0.008
Brazil 0.735 0.254 0.004 0.007
Greece 0.760 0.232 0.005 0.003
Belgium 0.566 0.424 0.006 0.003
Spain 0.693 0.299 0.003 0.005
Philippines 0.665 0.318 0.018 0
Malaysia 0.741 0.247 0.009 0.004
Korea, South 0.466 0.522 0.008 0.004
Canada 0.668 0.302 0.026 0.004
Bangladesh 0.463 0.527 0.010 0
Pakistan 0.675 0.317 0.003 0.006
Australia 0.746 0.242 0.012 0
Austria 0.693 0.292 0.013 0.003
Romania 0.738 0.252 0.007 0.004
Serbia 0.673 0.316 0.008 0.004
Ireland 0.810 0.176 0.014 0
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Note.

Prop_female = proportion of females.

Prop_male = proportion of males.

Prop_gender_other/not_say = proportion of participants of other genders or participants who did not want to declare their gender.

Prop_gender_NA = proportion of missing data for gender.

Table 3.

Proportion of education level across countries with more than 200 participants.

Country Prop_none Prop_6years Prop_9years Prop_12years Prop_some_college Prop_college Prop_PhD Prop_edu_NA Prop_uninf
Finland 0.023 0.070 0.032 0.137 0.205 0.484 0.041 0.008 0
France 0.009 0.012 0.008 0.082 0.212 0.577 0.098 0.003 0
Denmark 0.004 0.007 0.031 0.107 0.447 0.366 0.034 0.003 0.002
Mexico 0.004 0.001 0.006 0.053 0.093 0.540 0.300 0.003 0
Lithuania 0.001 0 0.003 0.030 0.144 0.769 0.052 0.003 0
Argentina 0.023 0.003 0.011 0.089 0.503 0.275 0.083 0.013 0
Japan 0.083 0.003 0.034 0.242 0.122 0.488 0.026 0.003 0
Bulgaria 0.003 0.000 0.002 0.104 0.228 0.599 0.047 0.015 0
Poland 0.004 0.000 0.008 0.151 0.232 0.547 0.051 0.006 0
Sweden 0.002 0.002 0.019 0.173 0.170 0.552 0.080 0.003 0
Croatia 0.003 0.002 0.006 0.163 0.164 0.608 0.051 0.004 0
Taiwan 0 0.000 0.003 0.046 0.034 0.619 0.297 0.001 0
Kosovo 0.004 0.000 0.003 0.160 0.564 0.242 0.019 0.007 0
United States 0.002 0.000 0.002 0.035 0.187 0.568 0.204 0.002 0
Czech Republic 0.004 0.001 0.004 0.150 0.276 0.515 0.046 0.004 0
Italy 0.019 0.003 0.020 0.201 0.237 0.437 0.070 0.013 0
Indonesia 0.001 0.001 0.003 0.086 0.110 0.760 0.040 0 0
United Kingdom 0 0.003 0.004 0.061 0.183 0.614 0.135 0 0
Germany 0.001 0.001 0.008 0.122 0.201 0.553 0.111 0.003 0
Hungary 0.001 0.002 0.021 0.339 0.307 0.287 0.039 0.006 0
Netherlands 0 0.002 0.001 0.016 0.141 0.770 0.061 0.008 0
Bosnia and Herzegovina 0.003 0.001 0.003 0.166 0.174 0.593 0.047 0.012 0
Turkey 0.003 0.003 0.004 0.047 0.059 0.554 0.328 0.003 0
Switzerland 0.005 0.003 0.030 0.129 0.171 0.566 0.090 0.006 0
Portugal 0.001 0.009 0.045 0.201 0.259 0.437 0.029 0.019 0
Slovakia 0.010 0.011 0.002 0.115 0.170 0.575 0.104 0.014 0
Panama 0.007 0.001 0.007 0.036 0.083 0.466 0.389 0.012 0
Brazil 0 0 0.004 0.031 0.342 0.506 0.114 0.003 0
Greece 0.005 0.005 0.008 0.083 0.128 0.600 0.154 0.019 0
Belgium 0.002 0.010 0.021 0.092 0.228 0.576 0.071 0.002 0
Spain 0.010 0.020 0.041 0.133 0.228 0.470 0.085 0.015 0
Philippines 0.002 0.005 0.007 0.065 0.521 0.367 0.030 0.004 0
Malaysia 0 0 0.002 0.011 0.093 0.596 0.296 0.002 0
Korea, South 0.002 0.002 0.004 0.033 0.043 0.686 0.228 0.002 0
Canada 0.006 0.006 0.004 0.045 0.230 0.566 0.143 0 0
Bangladesh 0.010 0 0 0.036 0.043 0.879 0.026 0.007 0
Pakistan 0 0 0.006 0.064 0.114 0.733 0.072 0.011 0
Australia 0.006 0 0.006 0.067 0.190 0.563 0.165 0.003 0
Austria 0.003 0 0.016 0.082 0.138 0.602 0.154 0.006 0
Romania 0.004 0 0.007 0.131 0.082 0.684 0.089 0.004 0
Serbia 0.004 0.004 0.008 0.113 0.387 0.421 0.064 0 0
Ireland 0 0.005 0.019 0.088 0.319 0.500 0.065 0.005 0
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Note.

Prop_none = proportion of participants that have no education.

Prop_6years = proportion of participants that have up to 6 years of education.

Prop_9years = proportion of participants that have up to 9 years of education.

Prop_12years = proportion of participants that have up to 12 years of education.

Prop_some_college = proportion of participants that have finished some years of college or equivalent.

Prop_college = proportion of participants who have bachelor’s or master’s degrees.

Prop_PhD = proportion of participants who have PhD.

Prop_edu_NA = proportion of participants who have missing data for education variable.

Prop_uninf = proportion of participants with uninformative answers (answer coding errors).

Table 4.

Proportion of marital status across countries with more than 200 participants.

Country Prop_single Prop_married/cohabiting Prop_divorced/widowed Prop_marital_other/not_say Prop_marital_NA
Finland 0.205 0.632 0.106 0.051 0.006
France 0.495 0.418 0.045 0.036 0.006
Denmark 0.233 0.669 0.074 0.013 0.006
Mexico 0.505 0.397 0.076 0.020 0.003
Lithuania 0.208 0.655 0.089 0.042 0.006
Argentina 0.420 0.411 0.109 0.050 0.009
Japan 0.366 0.529 0.066 0.034 0.006
Bulgaria 0.265 0.547 0.128 0.044 0.017
Poland 0.223 0.711 0.025 0.037 0.005
Sweden 0.170 0.675 0.081 0.068 0.007
Croatia 0.352 0.511 0.050 0.069 0.017
Taiwan 0.657 0.272 0.015 0.052 0.005
Kosovo 0.576 0.355 0.013 0.043 0.012
United States 0.302 0.588 0.089 0.018 0.003
Czech Republic 0.396 0.505 0.076 0.019 0.005
Italy 0.301 0.502 0.102 0.091 0.004
Indonesia 0.479 0.475 0.021 0.018 0.007
United Kingdom 0.263 0.619 0.079 0.037 0.003
Germany 0.357 0.550 0.049 0.038 0.007
Hungary 0.179 0.632 0.156 0.026 0.006
Netherlands 0.235 0.652 0.068 0.038 0.007
Bosnia and Herzegovina 0.320 0.506 0.085 0.068 0.021
Turkey 0.515 0.399 0.050 0.028 0.008
Switzerland 0.394 0.485 0.088 0.027 0.006
Portugal 0.538 0.359 0.066 0.031 0.007
Slovakia 0.239 0.596 0.124 0.032 0.010
Panama 0.431 0.473 0.072 0.016 0.008
Brazil 0.529 0.363 0.089 0.016 0.003
Greece 0.512 0.167 0.033 0.280 0.008
Belgium 0.434 0.460 0.058 0.039 0.010
Spain 0.369 0.499 0.080 0.044 0.008
Philippines 0.732 0.191 0.026 0.039 0.012
Malaysia 0.531 0.407 0.034 0.018 0.011
Korea, South 0.439 0.501 0.025 0.025 0.010
Canada 0.311 0.568 0.087 0.028 0.006
Bangladesh 0.544 0.425 0.007 0.021 0.002
Pakistan 0.658 0.311 0.011 0.011 0.008
Australia 0.229 0.584 0.122 0.055 0.009
Austria 0.254 0.633 0.063 0.041 0.009
Romania 0.252 0.660 0.014 0.064 0.011
Serbia 0.365 0.519 0.068 0.049 0
Ireland 0.227 0.676 0.056 0.037 0.005
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Note.

Prop_single = proportion of participants who are single.

Prop_married/cohabiting = proportion of participants who are married or cohabiting.

Prop_divorced/widowed = proportion of participants who are divorced or widowed.

Prop_marital_other/not_say = proportion of participants who live in some other form of community or don’t want to state their marital status.

Prop_marital_NA = proportion of missing data for the marital status variable.

Table 5.

Proportion of current risk of infection across countries with more than 200 participants.

Country Prop_yes Prop_not_sure Prop_no Prop_NA
Finland 0.785 0.057 0.157 0.001
France 0.660 0.087 0.250 0.003
Denmark 0.615 0.069 0.303 0.013
Mexico 0.765 0.049 0.181 0.005
Lithuania 0.667 0.098 0.234 0.001
Argentina 0.782 0.049 0.166 0.003
Japan 0.346 0.101 0.553 0.001
Bulgaria 0.610 0.120 0.265 0.004
Poland 0.857 0.028 0.115 0.000
Sweden 0.757 0.043 0.198 0.002
Croatia 0.694 0.079 0.222 0.005
Taiwan 0.474 0.120 0.402 0.003
Kosovo 0.421 0.140 0.434 0.005
United States 0.750 0.040 0.207 0.003
Czech Republic 0.694 0.068 0.236 0.003
Italy 0.659 0.071 0.264 0.006
Indonesia 0.564 0.173 0.257 0.006
United Kingdom 0.625 0.070 0.303 0.002
Germany 0.649 0.082 0.265 0.003
Hungary 0.776 0.054 0.168 0.003
Netherlands 0.707 0.043 0.243 0.007
Bosnia and Herzegovina 0.620 0.088 0.283 0.009
Turkey 0.761 0.068 0.165 0.006
Switzerland 0.649 0.075 0.269 0.007
Portugal 0.790 0.056 0.152 0.002
Slovakia 0.626 0.085 0.287 0.002
Panama 0.700 0.043 0.237 0.020
Brazil 0.900 0.018 0.078 0.004
Greece 0.698 0.072 0.226 0.005
Belgium 0.635 0.098 0.264 0.003
Spain 0.691 0.068 0.231 0.010
Philippines 0.496 0.135 0.368 0
Malaysia 0.552 0.097 0.344 0.007
Korea, South 0.366 0.012 0.612 0.010
Canada 0.685 0.066 0.249 0
Bangladesh 0.399 0.264 0.337 0
Pakistan 0.386 0.150 0.458 0.006
Australia 0.697 0.049 0.248 0.006
Austria 0.608 0.088 0.295 0.009
Romania 0.681 0.106 0.206 0.007
Serbia 0.624 0.068 0.305 0.004
Ireland 0.750 0.051 0.199 0
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Note.

Prop_yes = proportion of participants whose own or family members are at high risk,

Prop_not_sure = proportion of participants who are not sure,

Prop_no = proportion of participants whose own or family members are not at high risk.

Prop_NA = proportion of missing data for the risk variable.

Data visualization interface

In addition to the raw data, a dedicated Web application was developed to provide a general overview of the COVIDiSTRESS dataset (https://covidistress.france-bioinformatique.fr/). The Web application allows easy and dynamic generation of illustrations like age pyramids, zoomable world maps, and bar plots summarizing the main variables of the survey for each selected country. Two tabs of visualizations are provided: the first contains basic demographic variables like age, gender, and educational level by country; the second tab displays world maps of levels of stress, trust in institutions and concerns for self, friends, family, country, and other countries. The application is based on an R shiny server (https://rstudio.com/products/shiny/shiny-server/), together with the plot.ly31 and ggplot232 graphical libraries to generate dynamic plots. All the generated figures can be exported as PNG files.

Technical Validation

As of 30th May, the participants in our data represented 176 different countries. However, there were instances in which we only had one participant per country (i.e. The Bahamas, Uganda, etc.). For computational purposes, we decided to examine the data quality for 42 countries that had over 200 participants.

Overall, 25 of these 42 countries had more than 1,000 participants. Among these, Finland, France, and Denmark are the three countries with the highest numbers of respondents (over 10,000). At least 62% of the participants provided answers to half of the questions in the survey, and at least 47% responded to 90% of the questions. We added one variable, “answered_all,” that indicates whether a participant answered all questions for users’ information. Of all 125,360 participants included in the cleaned dataset, 42.48% answered all questions. Figure 2 demonstrates the proportion of valid data across 10 countries with the highest number of participants (top 10 countries). The mean age of participants (M = 39.22, SD = 14.09) falls between young- to mid-adulthood, and in most countries, the number of female participants is disproportionately higher. Figure 3 illustrates the distribution of gender in the top 10 countries. Similarly, our sample seems to disproportionately represent people with some levels of higher education (i.e. some college or higher). Figure 4 shows participants’ levels of education in the top 10 countries. Additional details on the sample characteristics (including age, gender, education level, and marital status) can be found in Table 1 through Table 4. The dataset also includes answers to questions related to the respondent’s current likelihood of infection (e.g. risk of infection with COVID-19 in the family and the degree of isolation), as shown in Tables 5 and 6. Given our narrow timeline and the convenience sampling method, we acknowledge that our samples may not be representative of the populations of interest. However, we believe that the data can still be meaningfully used to understand the experiences of certain groups of people during this pandemic.

Fig. 2.

Fig. 2

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The number of participants and proportions of valid data across ten countries with the largest samples.

Fig. 3.

Fig. 3

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The distribution of gender across ten countries with the largest samples (missing data were excluded from this depiction due to very low proportions).

Fig. 4.

Fig. 4

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The distribution of education across ten countries with the largest samples (missing data were excluded from this depiction due to very low proportions).

Aside from some specific questions on COVID-19 (i.e. self-protective behaviours, trust in the government’s agencies, etc.), our data includes several scales that were previously validated within certain populations, including the Asian Disease Problem, PSS-10, SPS-10, BFF-15 (BFI-S), and the SLON-3. Figure 5 illustrates Cronbach’s alphas for these scales in the top 10 countries. In Table 7, we presented several descriptive statistics of each of the aforementioned continuous scales. Below, we described the preliminary statistics of the scales for all 42 countries.

Fig. 5.

Fig. 5

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The Cronbach’s alpha reliability for each scale across ten countries with the largest samples.

Table 7.

Descriptive statistics for continuous scales across all 42 countries with more than 200 participants.

Scale pop_nonmis Mean SD Min Max Alpha
PSS-10 0.898 2.632 734 1 5 0.873
SPS-10 735 4.904 851 1 6 0.920
SLON-3 0.913 2.566 0.994 1 5 771
BFI-S extraversion 0.859 3.950 1.118 1 6 751
BFI-S neuroticism 0.860 3.338 1.052 1 6 0.695
BFI-S openness 859 4.508 921 1 6 0.656
BFI-S agreeableness 0.860 4.433 825 1 6 0.535
BFI-S conscientiousness 0.859 4.356 883 1 6 591
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Note.

Prop_nonmis = proportion of participants that responded to each scale.

Alpha = Cronbach’s alpha.

Asian disease problem

The basic descriptive statistics of the Asian Disease Problem are summarized in Table 8. Specifically, among the 42 countries, at least 91% of the participants responded to this problem. They were randomly assigned to either of the gain or loss condition. Among those who responded, 50.27% were assigned to the gain condition, while 49.73% to the loss condition. Participants in the gain condition selected one of two options, Program A vs. B. Program A was selected by 66.20% of the participants in the gain condition, while 33.80% selected Program B. Those in the loss condition selected one of two options, Program C vs. D. Program C was selected by 36.54% of the participants in the loss condition, while 63.46% selected Program D.

Table 8.

Descriptive statistics for the Asian Disease Problem across countries with more than 200 participants.

Country N Prop_nonmis Prop_gain Prop_program_A Prop_program_B Prop_loss Prop_program_C Prop_program_D
Argentina 5923 0.847 0.502 0.595 0.405 0.498 0.361 0.639
Australia 327 0.905 0.514 0.684 0.316 0.486 0.271 0.729
Austria 319 0.893 0.488 0.640 0.360 0.512 0.288 0.712
Bangladesh 421 0.805 0.507 0.616 0.384 0.493 0.269 0.731
Belgium 622 0.931 0.504 0.671 0.329 0.496 0.443 0.557
Bosnia and Herzegovina 1288 0.866 0.513 0.591 0.409 0.487 0.353 0.647
Brazil 731 0.923 0.508 0.624 0.376 0.492 0.319 .681
Bulgaria 4785 0.871 0.506 0.614 0.386 0.494 0.308 0.692
Canada 470 0.915 0.505 0.664 0.336 0.495 0.366 0.634
Croatia 2965 0.898 .497 0.623 0.377 0.503 0.330 0.670
Czech Republic 1995 0.904 0.492 0.538 0.462 0.508 0.353 0.647
Denmark 10891 0.909 0.501 0.680 0.320 0.499 0.372 0.628
Finland 22933 0.926 0.502 0.742 0.258 0.498 0.407 0.593
France 13475 0.932 0.508 0.710 0.290 0.492 0.438 0.562
Germany 1443 0.920 0.507 0.618 0.382 0.493 0.318 0.682
Greece 642 0.891 0.516 0.664 0.336 0.484 0.361 0.639
Hungary 1438 0.889 0.495 0.645 0.355 0.505 0.342 0.658
Indonesia 1569 0.887 0.504 0.513 0.487 0.496 0.333 0.667
Ireland 216 0.870 0.457 0.663 0.337 0.543 0.333 0.667
Italy 1749 0.842 0.505 0.586 0.414 0.495 0.291 0.709
Japan 5072 0.954 0.507 0.751 0.249 0.493 0.338 0.662
Korea, South 487 0.924 0.511 0.665 0.335 0.489 0.345 0.655
Kosovo 2707 0.803 0.497 0.633 0.367 0.503 0.361 0.639
Lithuania 8255 0.937 0.502 0.626 0.374 0.498 0.302 0.698
Malaysia 567 0.903 0.494 0.557 0.443 0.506 0.382 0.618
Mexico 9169 0.909 0.509 0.593 0.407 0.491 0.371 0.629
Netherlands 1433 0.909 0.474 0.661 0.339 0.526 0.428 0.572
Pakistan 360 0.836 0.505 0.592 0.408 0.495 0.362 0.638
Panama 759 0.810 0.504 0.616 0.384 0.496 0.407 0.593
Philippines 570 0.912 0.508 0.591 0.409 0.492 0.238 0.762
Poland 3088 0.935 0.500 0.600 0.0.400 0.500 0.236 0.764
Portugal 1067 0.906 0.499 0.671 0.329 0.501 0.287 0.713
Romania 282 0.840 0.519 0.569 0.431 0.481 0.298 0.702
Serbia 266 0.865 0.535 0.553 0.447 0.465 0.355 0.645
Slovakia 942 0.904 0.491 0.639 0.361 0.509 0.348 0.652
Spain 615 0.909 0.508 0.673 0.327 0.492 0.349 0.651
Sweden 3055 0.882 0.509 0.693 0.307 0.491 0.393 0.607
Switzerland 1188 0.912 0.505 0.676 0.0.324 0.495 0.437 0.563
Taiwan 2745 0.961 0.487 0.501 0.499 0.513 0.320 0.680
Turkey 1199 0.921 0.493 0.577 0.423 0.507 0.239 0.761
United Kingdom 1500 0.915 0.524 0.690 0.310 0.476 0.371 0.629
United States 2314 0.922 0.488 0.701 0.299 0.512 0.375 0.625
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Note.

N = number of participants

Prop_nonmis = proportion of participants that responded to Asian Disease Problem.

Prop_gain = proportion of participants assigned to the gain condition among those responded to Asian Disease Problem.

Prop_program_A = proportion of participants who selected Program A among those assigned to the gain condition.

Prop_program_B = proportion of participants who selected Program B among those assigned to the gain condition.

Prop_loss = proportion of participants assigned to the loss condition among those responded to Asian Disease Problem.

Prop_program_C = proportion of participants who selected Program C among those assigned to the loss condition.

Prop_program_C = proportion of participants who selected Program D among those assigned to the loss condition.

PSS-10

The basic descriptive statistics of the PSS-10 are summarized in Table 9. Specifically, among the 42 countries, at least 75% of the participants rated this scale. The composite scale score ranges from 1 to 5, with a mean value falling between 2.30 and 3.13. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.66 to 0.90.

Table 9.

Descriptive statistics and Cronbach’s alpha for the PSS across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.868 2.783 0.785 1.000 5 0.892
Australia 327 0.887 2.618 0.761 1.000 5 0.896
Austria 319 0.868 2.611 0.729 1.000 4.5 0.866
Bangladesh 421 0.808 2.830 0.592 1.000 4.3 0.794
Belgium 622 0.913 2.582 0.731 1.000 4.5 0.858
Bosnia and Herzegovina 1288 0.862 2.843 0.670 1.000 5 0.853
Brazil 731 0.896 3.059 0.730 1.100 5 0.882
Bulgaria 4785 0.859 2.848 0.719 1.000 5 0.861
Canada 470 0.894 2.715 0.723 1.000 5 0.880
Croatia 2965 0.890 2.875 0.661 1.000 5 0.860
Czech Republic 1995 0.882 2.694 0.707 1.000 4.9 0.878
Denmark 10891 0.910 2.423 0.717 1.000 5 0.883
Finland 22933 0.923 2.441 0.740 1.000 5 0.897
France 13475 0.905 2.564 0.742 1.000 5 0.856
Germany 1443 0.909 2.606 0.692 1.000 5 0.851
Greece 642 0.907 2.721 0.680 1.000 4.9 0.854
Hungary 1438 0.875 2.739 0.592 1.000 5 0.848
Indonesia 1569 0.857 2.749 0.591 1.000 5 0.837
Ireland 216 0.866 2.528 0.703 1.000 4.9 0.877
Italy 1749 0.893 2.539 0.687 1.000 5 0.861
Japan 5072 0.940 3.019 0.572 1.000 5 0.787
Korea, South 487 0.815 2.709 0.656 1.000 4.9 0.873
Kosovo 2707 0.809 2.861 0.541 1.000 5 0.666
Lithuania 8255 0.894 2.504 0.683 1.000 5 0.870
Malaysia 567 0.877 2.713 0.706 1.000 4.7 0.881
Mexico 9169 0.911 2.723 0.736 1.000 5 0.885
Netherlands 1433 0.909 2.298 0.677 1.000 4.6 0.885
Pakistan 360 0.747 2.883 0.718 1.000 5 0.816
Panama 759 0.851 2.430 0.632 1.000 4.7 0.852
Philippines 570 0.904 3.067 0.624 1.143 5 0.831
Poland 3088 0.892 2.993 0.729 1.000 5 0.894
Portugal 1067 0.882 2.886 0.726 1.100 5 0.884
Romania 282 0.869 2.668 0.651 1.000 4.6 0.881
Serbia 266 0.891 2.712 0.664 1.200 4.4 0.849
Slovakia 942 0.876 2.680 0.676 1.000 4.7 0.866
Spain 615 0.880 2.638 0.732 1.000 5 0.873
Sweden 3055 0.908 2.452 0.687 1.000 5 0.865
Switzerland 1188 0.918 2.378 0.650 1.000 4.5 0.831
Taiwan 2745 0.882 2.686 0.725 1.000 5 0.889
Turkey 1199 0.878 3.128 0.684 1.000 5 0.883
United Kingdom 1500 0.878 2.711 0.743 1.000 4.7 0.884
United States 2314 0.913 2.734 0.744 1.000 5 0.890
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Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

SPS-10

The basic descriptive statistics of the SPS-10 are summarized in Table 10. Specifically, among the 42 countries, at least half of the participants rated this scale. The composite scale score ranges from 1 to 6, with a mean value falling between 3.55 and 5.20. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.88 to 0.94.

Table 10.

Descriptive statistics and Cronbach’s alpha for the SPS across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.595 4.833 0.833 1.000 6 0.890
Australia 327 0.749 4.936 0.875 1.000 6 0.933
Austria 319 0.652 5.184 0.681 2.200 6 0.895
Bangladesh 421 0.558 4.806 0.770 2.100 6 0.901
Belgium 622 0.756 4.860 0.803 1.000 6 0.885
Bosnia and Herzegovina 1288 0.686 4.885 0.786 1.000 6 0.906
Brazil 731 0.688 5.167 0.710 2.375 6 0.904
Bulgaria 4785 0.685 4.808 0.790 1.000 6 0.886
Canada 470 0.770 4.868 0.818 1.400 6 0.910
Croatia 2965 0.737 5.059 0.709 1.500 6 0.893
Czech Republic 1995 0.727 4.925 0.758 1.400 6 0.904
Denmark 10891 0.752 5.203 0.693 1.000 6 0.902
Finland 22933 0.796 5.026 0.786 1.000 6 0.912
France 13475 0.770 4.881 0.805 1.000 6 0.884
Germany 1443 0.749 5.091 0.746 1.200 6 0.901
Greece 642 0.754 5.020 0.691 2.200 6 0.891
Hungary 1438 0.663 4.819 0.791 1.000 6 0.893
Indonesia 1569 0.611 4.590 0.741 1.000 6 0.892
Ireland 216 0.694 5.045 0.702 2.800 6 0.897
Italy 1749 0.735 4.891 0.736 1.000 6 0.891
Japan 5072 0.874 3.548 0.995 1.000 6 0.937
Korea, South 487 0.682 4.722 0.786 1.000 6 0.904
Kosovo 2707 0.498 4.881 0.717 1.700 6 0.878
Lithuania 8255 0.728 4.954 0.710 1.000 6 0.916
Malaysia 567 0.711 4.725 0.799 1.000 6 0.918
Mexico 9169 0.713 5.107 0.803 1.000 6 0.921
Netherlands 1433 0.752 5.029 0.690 1.000 6 0.909
Pakistan 360 0.525 4.750 0.822 1.100 6 0.908
Panama 759 0.631 5.187 0.726 1.100 6 0.914
Philippines 570 0.786 4.684 0.891 1.000 6 0.936
Poland 3088 0.690 5.000 0.743 1.000 6 0.918
Portugal 1067 0.627 5.109 0.682 1.900 6 0.893
Romania 282 0.624 4.890 0.766 2.200 6 0.909
Serbia 266 0.729 5.016 0.709 2.800 6 0.890
Slovakia 942 0.669 4.862 0.790 1.000 6 0.914
Spain 615 0.676 4.970 0.832 1.200 6 0.904
Sweden 3055 0.765 5.119 0.701 1.300 6 0.892
Switzerland 1188 0.765 5.120 0.717 1.000 6 0.901
Taiwan 2745 0.754 4.373 0.856 1.000 6 0.910
Turkey 1199 0.689 4.935 0.805 1.000 6 0.909
United Kingdom 1500 0.706 4.991 0.750 1.700 6 0.906
United States 2314 0.779 5.109 0.758 1.000 6 0.920
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Note.

N = number of participants.

Prop_nonmis = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

SLON-3

The basic descriptive statistics of the SLON-3 are summarized in Table 11. Specifically, among the 42 countries, at least 77% of the participants rated this scale. The composite scale score ranges from 1 to 5, with a mean value falling between 1.89 and 3.05. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.54 to 0.84.

Table 11.

Descriptive statistics and Cronbach’s alpha for the SLON across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.889 2.626 1.036 1.000 5 0.738
Australia 327 0.899 2.701 0.998 1.000 5 0.771
Austria 319 0.893 2.658 0.987 1.000 5 0.765
Bangladesh 421 0.824 2.790 0.856 1.000 5 0.576
Belgium 622 0.923 2.575 1.017 1.000 5 0.811
Bosnia and Herzegovina 1288 0.880 2.905 0.935 1.000 5 0.740
Brazil 731 0.912 2.755 0.913 1.000 5 0.714
Bulgaria 4785 0.884 2.743 1.020 1.000 5 0.737
Canada 470 0.904 2.726 0.959 1.000 5 0.765
Croatia 2965 0.902 2.901 0.894 1.000 5 0.737
Czech Republic 1995 0.893 2.952 0.971 1.000 5 0.761
Denmark 10891 0.922 2.308 0.890 1.000 5 0.720
Finland 22933 0.935 2.647 1.026 1.000 5 0.842
France 13475 0.923 2.420 1.027 1.000 5 0.793
Germany 1443 0.921 2.700 0.997 1.000 5 0.774
Greece 642 0.919 2.543 0.957 1.000 5 0.735
Hungary 1438 0.893 2.806 0.874 1.000 5 0.721
Indonesia 1569 0.872 2.352 0.952 1.000 5 0.799
Ireland 216 0.884 2.611 0.967 1.000 5 0.724
Italy 1749 0.916 2.757 0.973 1.000 5 0.776
Japan 5072 0.951 2.441 0.891 1.000 5 0.780
Korea, South 487 0.817 2.421 0.881 1.000 5 0.712
Kosovo 2707 0.839 2.324 0.884 1.000 5 0.618
Lithuania 8255 0.909 2.571 0.954 1.000 5 0.766
Malaysia 567 0.877 2.462 0.986 1.000 5 0.828
Mexico 9169 0.926 2.494 1.010 1.000 5 0.782
Netherlands 1433 0.911 2.491 0.887 1.000 5 0.786
Pakistan 360 0.769 2.712 1.052 1.000 5 0.699
Panama 759 0.881 2.220 0.837 1.000 5 0.675
Philippines 570 0.918 2.780 0.905 1.000 5 0.719
Poland 3088 0.905 3.052 1.047 1.000 5 0.806
Portugal 1067 0.898 2.592 0.939 1.000 5 0.721
Romania 282 0.879 2.868 0.899 1.000 5 0.724
Serbia 266 0.917 2.825 0.932 1.000 5 0.696
Slovakia 942 0.883 2.963 0.935 1.000 5 0.747
Spain 615 0.901 2.530 1.014 1.000 5 0.771
Sweden 3055 0.918 2.580 0.990 1.000 5 0.807
Switzerland 1188 0.929 2.468 0.936 1.000 5 0.764
Taiwan 2745 0.890 1.887 0.852 1.000 5 0.790
Turkey 1199 0.888 2.781 0.788 1.000 5 0.536
United Kingdom 1500 0.891 2.696 1.001 1.000 5 0.772
United States 2314 0.922 2.672 1.005 1.000 5 0.778
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Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

BFF-15

This term was used for this project. This is more commonly known as the Big Five Inventory-SOEP (BFI-S).

Extraversion

The basic descriptive statistics of this subscale are summarized in Table 12. Specifically, among the 42 countries, at least 71% of participants rated this scale. The composite subscale score ranges from 1 to 6, with a mean value falling between 3.12 to 4.50. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.51 to 0.86.

Table 12.

Descriptive statistics and Cronbach’s alpha for the BFI-S extraversion scale across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.810 3.953 1.002 1.000 6 0.641
Australia 327 0.847 3.786 1.184 1.000 6 0.816
Austria 319 0.796 4.315 1.088 1.000 6 0.813
Bangladesh 421 0.732 4.130 1.061 1.000 6 0.746
Belgium 622 0.867 3.847 1.198 1.000 6 0.792
Bosnia and Herzegovina 1288 0.844 4.444 0.988 1.000 6 0.755
Brazil 731 0.832 4.195 1.062 1.000 6 0.766
Bulgaria 4785 0.838 4.500 0.967 1.000 6 0.713
Canada 470 0.855 3.672 1.143 1.000 6 0.808
Croatia 2965 0.857 4.351 1.009 1.000 6 0.775
Czech Republic 1995 0.834 3.852 1.098 1.000 6 0.820
Denmark 10891 0.877 4.190 1.005 1.000 6 0.709
Finland 22933 0.891 4.148 1.132 1.000 6 0.823
France 13475 0.871 3.796 1.196 1.000 6 0.820
Germany 1443 0.865 4.009 1.109 1.000 6 0.782
Greece 642 0.861 4.353 1.012 1.000 6 0.765
Hungary 1438 0.800 4.226 1.035 1.000 6 0.728
Indonesia 1569 0.790 3.843 .965 1.000 6 0.694
Ireland 216 0.843 3.986 1.081 1.333 6 0.749
Italy 1749 0.870 4.005 1.063 1.000 6 0.765
Japan 5072 0.924 3.117 0.905 1.000 6 0.662
Korea, South 487 0.791 3.513 0.882 1.000 6 0.506
Kosovo 2707 0.752 4.156 0.877 1.000 6 0.526
Lithuania 8255 0.855 3.473 1.009 1.000 6 0.686
Malaysia 567 0.802 3.482 1.071 1.000 6 0.768
Mexico 9169 0.849 3.710 1.145 1.000 6 0.767
Netherlands 1433 0.862 4.082 1.029 1.000 6 0.774
Pakistan 360 0.708 3.916 1.079 1.333 6 0.670
Panama 759 0.810 3.807 1.022 1.000 6 0.647
Philippines 570 0.877 3.668 1.084 1.000 6 0.733
Poland 3088 0.836 3.926 0.999 1.000 6 0.700
Portugal 1067 0.813 4.266 1.057 1.000 6 0.794
Romania 282 0.812 4.199 1.048 1.333 6 0.788
Serbia 266 0.868 4.072 0.941 1.333 6 0.632
Slovakia 942 0.804 4.025 1.000 1.000 6 0.751
Spain 615 0.836 4.139 1.083 1.000 6 0.738
Sweden 3055 0.881 4.205 1.027 1.000 6 0.805
Switzerland 1188 0.882 4.202 1.053 1.000 6 0.794
Taiwan 2745 0.863 3.536 1.148 1.000 6 0.861
Turkey 1199 0.808 4.502 1.003 1.000 6 0.757
United Kingdom 1500 0.839 3.870 1.100 1.000 6 0.768
United States 2314 0.872 3.810 1.203 1.000 6 0.827
Open in a new tab

Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

Neuroticism

The basic descriptive statistics of this subscale are summarized in Table 13. Specifically, among the 42 countries, at least 70% of the participants rated this scale. The composite subscale score ranges from 1 to 6, with a mean value falling between 2.91 and 3.80. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.44 to 0.77.

Table 13.

Descriptive statistics and Cronbach’s alpha for the BFI-S neuroticism scale across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.819 3.763 0.968 1.000 6 0.596
Australia 327 0.844 3.292 1.060 1.000 6 0.709
Austria 319 0.796 3.054 0.973 1.000 5.66667 0.702
Bangladesh 421 0.739 3.197 0.972 1.000 6 0.610
Belgium 622 0.868 3.277 1.018 1.000 6 0.670
Bosnia and Herzegovina 1288 0.843 3.136 0.978 1.000 6 0.646
Brazil 731 0.833 3.602 1.110 1.000 6 0.713
Bulgaria 4785 0.842 3.048 1.002 1.000 6 0.645
Canada 470 0.857 3.439 1.033 1.000 6 0.726
Croatia 2965 0.858 3.204 0.994 1.000 6 0.702
Czech Republic 1995 0.834 3.597 0.994 1.000 6 0.736
Denmark 10891 0.877 2.962 1.087 1.000 6 0.732
Finland 22933 0.892 3.092 1.040 1.000 6 0.722
France 13475 0.872 3.535 1.109 1.000 6 0.735
Germany 1443 0.866 3.167 1.036 1.000 6 0.733
Greece 642 0.860 3.565 0.979 1.000 6 0.590
Hungary 1438 0.809 3.308 0.983 1.000 6 0.664
Indonesia 1569 0.791 3.625 0.767 1.000 6 0.440
Ireland 216 0.838 3.353 1.001 1.000 6 0.715
Italy 1749 0.867 3.358 0.985 1.000 6 0.613
Japan 5072 0.928 3.793 0.982 1.000 6 0.752
Korea, South 487 0.784 3.335 0.916 1.000 6 0.571
Kosovo 2707 0.758 3.387 1.002 1.000 6 0.630
Lithuania 8255 0.856 3.419 0.949 1.000 6 0.681
Malaysia 567 0.804 3.666 0.828 1.667 6 0.506
Mexico 9169 0.852 3.571 0.978 1.000 6 0.637
Netherlands 1433 0.862 2.967 1.026 1.000 6 0.749
Pakistan 360 0.703 3.802 0.920 1.000 6 0.444
Panama 759 0.814 3.362 0.867 1.000 6 0.482
Philippines 570 0.879 3.725 0.883 1.000 6 0.508
Poland 3088 0.838 3.497 0.956 1.000 6 0.646
Portugal 1067 0.812 3.763 1.143 1.000 6 0.763
Romania 282 0.812 3.270 0.987 1.000 6 0.669
Serbia 266 0.872 3.330 0.874 1.333 5.66667 0.525
Slovakia 942 0.809 3.359 0.991 1.000 6 0.762
Spain 615 0.833 3.440 1.058 1.000 6 0.680
Sweden 3055 0.883 2.905 1.026 1.000 6 0.772
Switzerland 1188 0.875 2.937 1.013 1.000 6 0.711
Taiwan 2745 0.863 3.802 0.919 1.000 6 0.690
Turkey 1199 0.810 3.422 1.025 1.000 6 0.674
United Kingdom 1500 0.840 3.361 1.026 1.000 6 0.698
United States 2314 0.869 3.420 1.028 1.000 6 0.693
Open in a new tab

Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

Openness

The basic descriptive statistics of this subscale are summarized in Table 14. Specifically, among the 42 countries, at least 71% of the participants rated this scale. The composite subscale score ranges from 1 to 6, with a mean value falling between 3.36 and 4.97. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.46 to 0.74.

Table 14.

Descriptive statistics and Cronbach’s alpha for the BFI-S openness scale across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.814 4.762 0.861 1.000 6 0.620
Australia 327 0.847 4.528 0.862 1.667 6 0.608
Austria 319 0.799 4.711 0.832 1.333 6 0.597
Bangladesh 421 0.734 4.580 0.704 2.000 6 0.504
Belgium 622 0.870 4.525 0.907 1.000 6 0.611
Bosnia and Herzegovina 1288 0.839 4.668 0.812 1.333 6 0.604
Brazil 731 0.836 4.586 0.898 1.667 6 0.620
Bulgaria 4785 0.843 4.706 0.816 1.000 6 0.586
Canada 470 0.853 4.635 0.881 1.667 6 0.629
Croatia 2965 0.858 4.649 0.820 1.333 6 0.628
Czech Republic 1995 0.832 4.417 0.821 1.000 6 0.616
Denmark 10891 0.875 4.352 0.983 1.000 6 0.607
Finland 22933 0.891 4.664 0.879 1.000 6 0.653
France 13475 0.870 4.431 0.945 1.000 6 0.628
Germany 1443 0.866 4.631 0.864 1.000 6 0.653
Greece 642 0.868 4.556 0.814 1.667 6 0.525
Hungary 1438 0.806 4.113 0.857 1.000 6 0.474
Indonesia 1569 0.788 4.576 0.706 1.000 6 0.618
Ireland 216 0.833 4.321 0.902 1.000 6 0.606
Italy 1749 0.860 4.514 0.872 1.000 6 0.584
Japan 5072 0.927 3.364 0.938 1.000 6 0.740
Korea, South 487 0.786 4.403 0.900 1.667 6 0.693
Kosovo 2707 0.752 4.618 0.762 1.000 6 0.472
Lithuania 8255 0.856 4.436 0.829 1.000 6 0.586
Malaysia 567 0.804 4.365 0.765 2.000 6 0.500
Mexico 9169 0.849 4.886 0.769 1.000 6 0.636
Netherlands 1433 0.864 4.391 0.879 1.000 6 0.579
Pakistan 360 0.706 4.595 0.789 2.000 6 0.456
Panama 759 0.808 4.968 0.740 1.000 6 0.643
Philippines 570 0.881 4.396 0.925 1.000 6 0.646
Poland 3088 0.838 4.436 0.857 1.333 6 0.625
Portugal 1067 0.812 4.401 0.885 1.000 6 0.587
Romania 282 0.812 4.538 0.821 1.000 6 0.593
Serbia 266 0.872 4.587 0.806 2.333 6 0.649
Slovakia 942 0.805 4.622 0.775 1.000 6 0.649
Spain 615 0.828 4.693 0.862 1.667 6 0.689
Sweden 3055 0.882 4.449 0.908 1.000 6 0.647
Switzerland 1188 0.877 4.517 0.852 1.333 6 0.592
Taiwan 2745 0.863 4.200 0.847 1.000 6 0.660
Turkey 1199 0.808 4.721 0.814 1.333 6 0.706
United Kingdom 1500 0.842 4.557 0.851 1.500 6 0.601
United States 2314 0.870 4.652 0.840 2.000 6 0.611
Open in a new tab

Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

Agreeableness

The basic descriptive statistics of this subscale are summarized in Table 15. Specifically, among the 42 countries, at least 71% of participants rated this scale. The composite subscale score ranges from 1 to 6, with a mean value falling between 3.62 and 4.85. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges from 0.30 to 0.67.

Table 15.

Descriptive statistics and Cronbach’s alpha for the BFI-S agreeableness scale across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.817 4.523 0.807 1.000 6 0.295
Australia 327 0.847 4.484 0.814 2.333 6 0.576
Austria 319 0.796 4.414 0.766 2.333 6 0.497
Bangladesh 421 0.736 4.351 0.766 1.000 6 0.424
Belgium 622 0.867 4.451 0.841 1.000 6 0.581
Bosnia and Herzegovina 1288 0.845 4.563 0.778 1.000 6 0.569
Brazil 731 0.834 4.346 0.812 1.667 6 0.501
Bulgaria 4785 0.843 4.382 0.849 1.333 6 0.508
Canada 470 0.851 4.539 0.781 1.333 6 0.546
Croatia 2965 0.859 4.482 0.759 1.333 6 0.550
Czech Republic 1995 0.832 4.049 0.816 1.333 6 0.526
Denmark 10891 0.875 4.549 0.750 1.333 6 0.340
Finland 22933 0.891 4.517 0.781 1.000 6 0.604
France 13475 0.872 4.421 0.872 1.000 6 0.569
Germany 1443 0.868 4.351 0.803 1.000 6 0.531
Greece 642 0.866 4.663 0.726 2.000 6 0.466
Hungary 1438 0.806 4.301 0.832 1.000 6 0.562
Indonesia 1569 0.790 4.322 0.751 2.000 6 0.397
Ireland 216 0.847 4.527 0.764 2.000 6 0.413
Italy 1749 0.866 4.451 0.810 1.000 6 0.518
Japan 5072 0.927 3.619 0.757 1.000 6 0.568
Korea, South 487 0.786 4.351 0.807 1.667 6 0.668
Kosovo 2707 0.757 4.854 0.742 1.000 6 0.536
Lithuania 8255 0.857 4.245 0.778 1.000 6 0.522
Malaysia 567 0.810 4.372 0.783 1.333 6 0.539
Mexico 9169 0.852 4.604 0.833 1.000 6 0.530
Netherlands 1433 0.863 4.672 0.732 1.000 6 0.538
Pakistan 360 0.711 4.391 0.751 2.000 6 0.450
Panama 759 0.809 4.740 0.834 1.333 6 0.471
Philippines 570 0.879 4.481 0.780 1.333 6 0.444
Poland 3088 0.838 4.292 0.755 1.000 6 0.553
Portugal 1067 0.813 4.491 0.780 2.000 6 0.496
Romania 282 0.809 4.529 0.733 1.667 6 0.481
Serbia 266 0.876 4.483 0.799 2.333 6 0.531
Slovakia 942 0.807 4.583 0.779 1.000 6 0.583
Spain 615 0.837 4.607 0.810 1.667 6 0.461
Sweden 3055 0.881 4.707 0.727 1.667 6 0.587
Switzerland 1188 0.883 4.391 0.786 1.667 6 0.531
Taiwan 2745 0.863 4.154 0.833 1.000 6 0.646
Turkey 1199 0.808 4.405 0.849 1.333 6 0.544
United Kingdom 1500 0.841 4.485 0.799 1.667 6 0.547
United States 2314 0.869 4.571 0.791 1.667 6 0.564
Open in a new tab

Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

Conscientiousness

The basic descriptive statistics of this subscale are summarized in Table 16. Specifically, among the 42 countries, at least 70% of participants rated this scale. The composite subscale score ranges from 1 to 6, with a mean value falling between 3.54 and 5.01. The internal consistency of the scale, as measured by Cronbach’s alpha, ranges 0.34 to 0.67.

Table 16.

Descriptive statistics and Cronbach’s alpha for the BFI-S conscientiousness scale across countries with more than 200 participants.

Country N Prop_nonmis Mean SD Min Max Alpha
Argentina 5923 0.817 4.766 0.825 1.000 6 0.558
Australia 327 0.841 4.350 0.800 2.000 6 0.509
Austria 319 0.799 4.556 0.841 1.667 6 0.669
Bangladesh 421 0.736 4.116 0.834 1.500 6 0.536
Belgium 622 0.865 4.129 0.901 1.667 6 0.542
Bosnia and Herzegovina 1288 0.839 4.714 0.782 2.000 6 0.603
Brazil 731 0.832 4.089 0.811 1.000 6 0.352
Bulgaria 4785 0.841 4.884 0.732 1.667 6 0.511
Canada 470 0.851 4.363 0.879 1.333 6 0.641
Croatia 2965 0.858 4.585 0.813 1.333 6 0.635
Czech Republic 1995 0.833 3.814 0.800 1.000 6 0.510
Denmark 10891 0.869 4.576 0.756 1.000 6 0.376
Finland 22933 0.891 4.375 0.844 1.000 6 0.608
France 13475 0.872 4.054 0.929 1.000 6 0.568
Germany 1443 0.868 4.329 0.854 1.000 6 0.588
Greece 642 0.861 4.267 0.779 1.667 6 0.411
Hungary 1438 0.806 4.406 0.867 1.000 6 0.628
Indonesia 1569 0.790 3.993 0.792 1.667 6 0.575
Ireland 216 0.843 4.418 0.869 2.000 6 0.650
Italy 1749 0.864 4.318 0.840 1.333 6 0.487
Japan 5072 0.928 3.536 0.777 1.000 6 0.502
Korea, South 487 0.789 4.123 0.863 1.333 6 0.667
Kosovo 2707 0.752 4.760 0.728 1.000 6 0.404
Lithuania 8255 0.856 4.087 0.757 1.000 6 0.410
Malaysia 567 0.804 4.226 0.804 1.000 6 0.546
Mexico 9169 0.849 4.796 0.804 1.000 6 0.570
Netherlands 1433 0.864 4.561 0.741 1.000 6 0.483
Pakistan 360 0.697 4.218 0.825 2.333 6 0.475
Panama 759 0.812 5.009 0.724 2.333 6 0.545
Philippines 570 0.877 4.036 0.789 1.000 6 0.460
Poland 3088 0.837 4.217 0.815 1.000 6 0.566
Portugal 1067 0.807 4.160 0.760 2.000 6 0.339
Romania 282 0.801 4.394 0.788 1.667 6 0.457
Serbia 266 0.868 4.324 0.823 1.667 6 0.624
Slovakia 942 0.811 4.345 0.810 1.000 6 0.581
Spain 615 0.834 4.578 0.841 1.667 6 0.545
Sweden 3055 0.878 4.530 0.785 1.667 6 0.607
Switzerland 1188 0.881 4.534 0.813 1.333 6 0.583
Taiwan 2745 0.862 3.622 0.832 1.000 6 0.542
Turkey 1199 0.809 4.533 0.820 1.333 6 0.518
United Kingdom 1500 0.835 4.395 0.824 1.000 6 0.576
United States 2314 0.868 4.515 0.800 1.667 6 0.548
Open in a new tab

Note.

N = number of participants.

Prop_nonmissing = proportion of participants that have data on all items of the scale.

Mean = scale mean.

SD = scale standard deviation.

Min = minimal value of the average scale score.

Max = maximal value of the average scale score.

Alpha = Cronbach’s alpha.

Usage Notes

We recommend that any interested researchers use the raw or the cleaned version of the latest extracted data (available at 10.17605/OSF.IO/Z39US). The data was imported and cleaned using the R software for statistical analysis33 and packages tidyverse34, multicon35, qualtRics36, pacman37, and psych38. Before using the dataset, the steps in the Data cleaning section should be followed to ensure that the dataset is ready for analysis. The data cleaning procedure should involve excluding irrelevant cases, correcting some errors in value-coding, and renaming improperly named variables. In addition, the cleaning procedure should encompass recoding choice values to number, creating composite scores, and the estimation of the Cronbach alpha reliabilities for the measured scales (PSS-10, BFF-15, SPS-10, and SLON-3). However, for analysis in individual countries, we recommend checking for tau-equivalence before using Cronbach’s alpha for reliability estimation. If tau-equivalence is not achieved, Omega coefficient is more appropriate as a reliability indicator39,40. Before analysing the data, it should be noted that the answers in variables measuring distress (‘Expl_Distress_no’) are recoded to numeric values 1, 2, 3, 4, 5, and 6, measuring the degree of agreement, and 99, which means that the item does not apply to one’s current situation. Additionally, answers in the variable ‘Trust_countrymeasure’ are recoded on a scale from 0 to 10, where 0 and 10 suggest inappropriate measures (too little or too much) and values around 5 suggest appropriate measures.

To merge the present dataset with a pre-existing cross-cultural dataset by country and date, the variables ‘Country’ and ‘RecordedDate’ should be used.

Finally, the samples in the present dataset are not representative of the populations from which they are drawn (in each country). Thus, users who wish to address this issue may weigh the data by referring to demographic information for each country and apply the appropriate weights for the variables and countries of interest (e.g., age: http://data.un.org/Data.aspx?d=POP&f=tableCode%3A22; gender: https://ourworldindata.org/gender-ratio; education: https://ourworldindata.org/global-education; marital status: https://ourworldindata.org/marriages-and-divorces).

Supplementary information

Figure S1. (58.7KB, pdf)

Acknowledgements

The COVIDiSTRESS 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. All funding information is listed in the supplementary material (Figure S1). We also want to address thanks to the IFB (Institut Français de Bioinformatique, https://www.france-bioinformatique.fr/) for hosting the server Shiny illustrating our results. This research was supported by JSPS KAKENHI Grants JP17H00875, JP18K12015, JP20H04581, JP20K14222, Czech Science Foundation GC19-09265J, Consejo Nacional de Ciencia y Tecnologia (Conacyt), Full National Scholarship - MSc degree (CVU: 613905), Research Foundation Flanders (FWO) postdoctoral fellowship, and The HSE University Basic Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contributions

Contributions from all the authors are listed in the supplementary material (Figure S1).

Code availability

Raw data and R-code for cleaning are available at 10.17605/OSF.IO/Z39US

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A list of authors and their affiliations appears at the end of the paper.

Contributor Information

Yuki Yamada, Email: yamadayuk@gmail.com.

Andreas Lieberoth, Email: andreas@edu.au.dk.

COVIDiSTRESS Global Survey Consortium:

Angélique M. Blackburn, Loïs Boullu, Mila Bujić, Grace Byrne, Marjolein C. J. Caniëls, Ivan Flis, Marta Kowal, Nikolay R. Rachev, Vicenta Reynoso-Alcántara, Oulmann Zerhouni, Oli Ahmed, Rizwana Amin, Sibele Aquino, João Carlos Areias, John Jamir Benzon R. Aruta, Dastan Bamwesigye, Jozef Bavolar, Andrew R. Bender, Pratik Bhandari, Tuba Bircan, Huseyin Cakal, Tereza Capelos, Jiří Čeněk, Brendan Ch’ng, Fang-Yu Chen, Stavroula Chrona, Carlos C. Contreras-Ibáñez, Pablo Sebastián Correa, Irene Cristofori, Wilson Cyrus-Lai, Guillermo Delgado-Garcia, Eliane Deschrijver, Carlos Díaz, İlknur Dilekler, Vilius Dranseika, Dmitrii Dubrov, Kristina Eichel, Eda Ermagan-Caglar, Rebekah Gelpí, Rubén Flores González, Amanda Griffin, Moh Abdul Hakim, Krzysztof Hanusz, Yuen Wan Ho, Dayana Hristova, Barbora Hubena, Keiko Ihaya, Gozde Ikizer, Md. Nurul Islam, Alma Jeftic, Shruti Jha, Fernanda Pérez-Gay Juárez, Pavol Kacmar, Kalina Kalinova, Phillip S. Kavanagh, Mehmet Kosa, Karolina Koszałkowska, Raisa Kumaga, David Lacko, Yookyung Lee, Antonio G. Lentoor, Gabriel A. De Leon, Shiang-Yi Lin, Samuel Lins, Claudio Rafael Castro López, Agnieszka E. Lys, Samkelisiwe Mahlungulu, Tsvetelina Makaveeva, Salomé Mamede, Silvia Mari, Tiago A. Marot, Liz Martinez, Dar Meshi, Débora Jeanette Mola, Sara Morales-Izquierdo, Arian Musliu, Priyanka A. Naidu, Arooj Najmussaqib, Jean C. Natividade, Steve Nebel, Jana Nezkusilova, Irina Nikolova, Manuel Ninaus, Valdas Noreika, María Victoria Ortiz, Daphna Hausman Ozery, Daniel Pankowski, Tiziana Pennato, Martin Pírko, Lotte Pummerer, Cecilia Reyna, Eugenia Romano, Hafize Sahin, Aybegum Memisoglu Sanli, Gülden Sayılan, Alessia Scarpaci, Cristina Sechi, Maor Shani, Aya Shata, Pilleriin Sikka, Nidhi Sinha, Sabrina Stöckli, Anna Studzinska, Emilija Sungailaite, Zea Szebeni, Benjamin Tag, Mihaela Taranu, Franco Tisocco, Jarno Tuominen, Fidan Turk, Muhammad Kamal Uddin, Ena Uzelac, Sara Vestergren, Roosevelt Vilar, Austin Horng-En Wang, J. Noël West, Charles K. S. Wu, Teodora Yaneva, and Yao-Yuan Yeh

Supplementary information

is available for this paper at 10.1038/s41597-020-00784-9.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Lieberoth A, 2020. COVIDiSTRESS global survey. Open Science Framework. [DOI]

Supplementary Materials

Figure S1. (58.7KB, pdf)

Data Availability Statement

Raw data and R-code for cleaning are available at 10.17605/OSF.IO/Z39US

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

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