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A multi-modal panel dataset to
understand the psychological
impact of the pandemic
Isabelle van der Vegt
✉ & Bennett Kleinberg
n =n =
n =
Since the start of the pandemic, social and behavioural scientists have collected data on the psychological impact
on individuals of COVID-19 and the measures introduced around it. e global health crisis severely impacted
lives around the world. At the same time, it enabled social scientists across disciplines to study the response
of humans to unprecedented circumstances. Several papers and associated datasets have emerged as a result
of this, including those that adopted a psychological perspective. For instance, the COVIDiSTRESS Global
Survey includes measures such as perceived stress, trust in authorities, and compliance with anti-COVID meas-
ures collected between 30 March and 30 May 2020 from 173,426 individuals across 39 countries and regions1.
Similarly, the PsyCorona dataset consists of data collected at the start of the pandemic (n = 34,526) from 41 societies
worldwide, measuring psychological variables and behaviours such as leaving the home and physical distancing2.
at dataset has been used in follow-up studies to measure, for example, cooperation and trust across societies3
and associations between emotion and risk perception of COVID-194. Others have studied the concept of ‘pan-
demic fatigue’ (i.e., the perceived inability to “keep up” with restrictions), for which there are data available from
eight countries5. Associations between pandemic fatigue and the severity of restrictions were found, in addition
to pandemic fatigue eliciting political discontent.
Of particular promise to understand how individuals fared during and in the aermath of the pandemic are
free-text responses, which allow for more depth and coverage of topics than targeted survey-style data collec-
tion. Some initiatives have used and made available linguistic data on the consequences of the pandemic, usually
from Twitter6,7. In another study, Reddit and survey data were analysed to measure shis in psychological states
throughout the pandemic8. However, both modalities of data were collected from dierent participants, which
does not allow for deeper exploration of ground truth psychological states of text authors by connecting survey
and text modalities. Collecting text and survey data from the same participants is desirable for several reasons.
Firstly, free-text responses enable participants to report their experiences in the pandemic in an unconstrained
manner, potentially oering deeper insight into psychological processes. Secondly, simultaneously obtained sur-
vey responses oer ground truth measures on the psychological variables potentially underlying what is written
about in text. irdly, advances made in the area of natural language processing allow for in-depth quantitative
analyses of the text data, thereby making text data a resource that reaches beyond qualitative analyses typically
1Utrecht University, Department of Sociology, Utrecht University, 3584 CH, Utrecht, The Netherlands. 2Tilburg
University, Department of Methodology and Statistics, 5037 AB, Tilburg, The Netherlands. 3University College
London, Department of Security and Crime Science, London, WC1E 6BT, UK. 4These authors contributed equally:
Isabelle van der Vegt, Bennett Kleinberg. ✉e-mail: i.w.j.vandervegt@uu.nl
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conducted manually. However, collecting data that connect the textual dimension to survey data is costly as
it requires primary data collection and cannot be realised through “found data” (e.g., posts on social media).
Consequently, to date, such datasets are scarce and the lack thereof has impeded how we study the psychological
impact of the pandemic.
e current paper lls that gap and introduces the Real World Worries Waves Dataset (RW3D) oering the
unique combination of ground-truth survey data on emotions with free-text responses describing emotions in
relation to the pandemic. e richness of this dataset allows us to examine, for example, emotional responses
and the content of worries as a consequence of COVID-19. Given the broad scope of potential research ques-
tions and the scarcity and necessity of these data sources, we make this dataset available to the research commu-
nity. Hereaer, we provide detailed background on the data collection procedure, recorded variables, participant
demographics as well as an attrition analysis and descriptive statistics. We also provide evidence for latent clus-
ters of how participants’ emotions changed over time and to what extent they were realistically or overly worried
about various concerns in their lives. Our aim with this paper is to oer detail on a unique resource that could
inspire plenty of research questions.
Methods
Ethics. e data collection was approved by the departmental ethics review board at University College
London. No personal data were collected from participants and all participants provided informed consent for
participation and for their data to be shared.
e dataset was collected in three waves in April of 2020, 2021 and 2022. Data collection started
in April 2020 on the crowdsourcing platform Prolic with an initial sample size of n = 2500. We then contacted
the same participants through the crowdsourcing platform one year later about a follow-up data collection and
made participation slots available for all participants whose data were collected in the rst wave. at procedure
was repeated another year later with those participants whose data were collected in wave 2. is resulted in
sample sizes of n = 1839 in 2021, and n = 1227 in 2022. See Fig.1 for an overview of the data collection procedure
and retention across waves.
In all data collection phases, participants were informed about the purpose of the study, namely, to collect
data about emotions and worries regarding the pandemic (see Supplemental Materials Table1 for the full task
intro and debrief). Participants started with the self-rated emotions questionnaire and the single emotion selec-
tion, then proceeded to the textual expressions and nally provided control variables (wave 1 and 2) and life
events and psychological stressor variables (wave 3 only, see Fig.1).
Fig. 1 Data collection procedure and retention across waves.
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Only UK-based Prolic users who used Twitter (at least once a month) as per Prolic’s prescreening were
eligible for participation. Upon completion of the survey, each participant was paid GBP 0.50. Even though
the eective time spent on the task was somewhat longer than anticipated, we did not adjust the rewards so as
not to introduce a change in reward as a confounding variable for the repeated-measures design. e task was
administered through Qualtrics.
Timing and societal context. e rst wave of data collection occurred in early April 2020, when the UK
was under lockdown with death tolls increasing. Queen Elizabeth II had just addressed the nation and then Prime
Minister Boris Johnson was admitted to hospital due to COVID-19 symptoms9. In wave 2 (April 2021), many
people in the UK had been vaccinated, and schools, retail and the hospitality sectors were (partially) re-opening.
e delta-variant of the Coronavirus had just been identied at this time10. Finally, in wave 3 (April 2022) all
travel restrictions for those entering the UK had been lied, the Omicron variant was surging and news around
the Partygate aair (i.e., a political scandal surrounding parties held at Downing Street during lockdown) was
ongoing11.
Demographic variables. We obtained participants’ demographics from Prolic. ese are data that reg-
istered participants volunteered to provide and consist of their age, gender, country of birth, nationality, rst
language, employment status, student status, country of birth, country of residence as well as their participation
on the crowdsourcing platform (number of tasks completed and approved). We have added one demographic
question in the survey about their native language (as this may dier from their rst language).
Participants were on average 37.10 years old (SD = 11.98) in April 2022, of which 68.4% were female (31.4%
male, remaining: prefer not to say, see Supplemental Materials Table2). e vast majority (90.5%) indicated
the UK as their country of birth and as their current country of residence (99.7%), which matches the recruit-
ment pre-selection that we made. Regarding their employment status, in 2020, 52.4% indicated being full-time
employed, 22.7% in part-time work and 10.5% not in paid work (e.g., retired). Interestingly, the percentage of
people in full-time work decreased somewhat in 2022 (42.4%). Similarly, the percentage of students decreased
from 16.9% in 2020 to 10.9% in 2022.
Emotion data. Self-rated emotions. Participants were asked to indicate on a 9-point scale how worried they
were about the Corona situation (with labels at 1 = not worried at all; 5 = moderately worried; 9 = very worried)
and how they felt at this moment about the Corona situation. For the latter, they indicated how strongly they felt
each of the following eight emotions (1 = none at all; 5 = moderately; 9 = very much): anger, disgust, fear, anxiety,
sadness, happiness, relaxation, desire12. e scale judgments were indicated using a slider in steps of 1 with labels
at the extremes and in the middle for orientation.
Single emotion selection. Of the eight emotions listed above (i.e., excluding worry), each participant was asked
“If you have to choose just one, which of the emotions below best characterises how you feel at this moment?”.
Table1 shows the descriptive statistics for the emotion variables (self-rated scale values and discrete choice).
While the pattern overall suggests improvement, in that the positive emotions increase while the negative ones
decrease, there are latent patterns at play. Previous work using earlier waves of this data found clusters of par-
ticipants in how their emotion scores changed from 2020 to 202110 and we provide additional evidence for
sub-groups below.
Text data. We elicited two textual responses from each participant. e rst text data were obtained through
the following instruction: “Please write in a few sentences how you feel about the Corona situation at this very
moment. is text should express your feelings at this moment.” Participants typed their response in a text eld
and received a prompt if their response was shorter than 500 characters. e second text response was obtained
directly thereaer aimed at eliciting a shorter, Tweet-length text as follows: “Suppose you had to express your
current feeling about the Corona situation in a Tweet (max. 280 characters). Please write in the text box below”.
In this case, the participants were prompted if their text input was shorter than 10 or longer than 280 characters.
Emotion MWav e1 SDWa ve 1 prop.Wa ve 1 MWav e2 SDWav e2 prop.Wa ve 2 MWave 3 SDWav e3 prop.Wav e3
worry 6.67 1.70 — 5.07 2.03 — 3.98 2.16 —
anger 3.76 2.18 0.04 3.47 2.35 0.08 2.85 2.19 0.06
disgust 3.06 2.12 0.01 2.79 2.16 0.02 2.39 2.03 0.03
fear 5.63 2.30 0.09 3.77 2.30 0.02 2.85 2.05 0.02
anxiety 6.51 2.30 0.58 5.05 2.52 0.36 4.09 2.45 0.30
sadness 5.55 2.31 0.15 4.64 2.57 0.19 3.48 2.35 0.13
happiness 3.55 1.84 0.01 4.29 1.98 0.05 4.76 2.10 0.07
relaxation 3.83 2.05 0.12 4.54 2.25 0.23 5.14 2.35 0.38
desire 2.73 1.90 0.01 3.42 2.19 0.05 3.22 2.09 0.02
Tab le 1. Descriptive statistics per wave (M, SD) for the self-rated emotions (scale: 1 = not at all; 5 = moderately;
9 = very much) and the proportion of individuals who chose the respective emotion as “best tting” emotion.
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e corpus descriptives (Supplementary Materials Table3) show a stable length of both long and short
texts over the three waves. In total, the corpus consists of 430,751 tokens (2020: 145,348; 2021: 144,191; 2022:
141,212). Figure2 shows example texts written over all three waves.
In the rst two waves (April 2020 and 2021), we recorded two sets of
control variables: the self-rated ability to express emotions in text and Twitter usage. We decided to drop these
from the third wave. e rationale for dropping these variables was that we assumed these to change little within
the individual and we already had two measurements (wave 1 and 2) that correlated substantially (see Table2).
Emotion expression. As a potential control for the link between self-reported emotions in a survey and the
expression of emotion in text, we asked participants to indicate on a 9-point scale (1 = not at all; 5 = moderately;
9 = very well) how well they (i) could express their feelings in general, (ii) how well in the Tweet-size text, and
(iii) how well in the longer text.
Twitter usage. As an additional potential confounding variable specically for the Tweet-size text we asked
about participants’ Twitter usage. Using a 9-point scale (1 = never; 5 = every month; 9 = every day), participants
indicated how oen they (i) are on Twitter, (ii) send Tweets themselves, and (iii) participate in conversations on
Twitter.
e most recent wave (April 2022) included two additional constructs that
replaced the control variables from the previous waves. To better understand potential moderating variables of
participants’ emotional adjustment in the pandemic and their textual expression, we collected data on important
life events during the pandemic and used a crisis coping questionnaire13.
Life events. Participants were asked retrospectively about any important events or changes in their life that
have happened to them over the past two years. First, they were asked whether “anything - positive or negative
- in [their] life [has] over the past two years impacted how [they] dealt with the Corona situation?” ose who
answered yes were then asked to describe the event, date the event (month and year) and rate the event’s impact
on a scale from −10 (very negative) to +10 (very positive). If there was an additional event, participants could
also submit one more (for a maximum of two events). All life events were subsequently qualitatively coded by
the authors to arrive at overarching categories. For instance, being red, changing jobs, and obtaining a rst
job aer college were mapped to the category ‘job’; getting married, nding a partner, and a break-up were all
mapped to ‘romantic’.
A third of the participants (33.9%) reported a signicant life event during data collection. e most com-
mon life event category was ‘death’ (e.g., a death in the family), which was almost exclusively rated as a negative
life event (97.6%). Life events related to work (e.g., a job change) were also common, which most participants
(69.9%) rated with a positive intensity (Table3). Other life events such as ‘mental health’ (e.g., experiencing
panic attacks, receiving a mental health diagnosis) and ‘nancial’ (e.g., paying o loans, loss of income) show a
more ambivalent pattern and were rated as positive and negative with approximately equal proportion. Most life
events occurred in December 2021 (median). See Table4 for examples of each life event category.
Fig. 2 Text data of a single participant (long text and Tweet-size text).
Var iabl e MWave1 SDWa ve 1 MWav e2 SDWav e2 r
Emotion expression general 6.90 1.72 6.84 1.71 0.45 [0.40; 0.49]
Emotion expression short text 5.95 2.15 6.02 2.06 0.35 [0.30; 0.40]
Emotion expression long text 7.05 1.84 7.08 1.77 0.39 [0.34; 0.44]
Using Twitter 6.24 2.79 5.94 2.86 0.68 [0.65; 0.71]
Sending Tweets 3.77 2.55 3.45 2.49 0.73 [0.70; 0.75]
Conversations on Twitter 3.53 2.43 3.29 2.38 0.68 [0.65; 0.71]
Tab le 2. Descriptives for the control variables collected in wave 1 and 2 (M, SD). All variables correlated
substantially albeit somewhat stronger for the Twitter usage variables than for emotion expression ratings.
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Stressors during crisis. To measure psychological stressors, we used a part of the Crisis Coping Assessment
Questionnaire (CCAQ)13. Specically, we asked several items from two perspectives: how worried they were
about a range of concerns over the past two years (we refer to this below as the worry score) and how problematic
each of the concerns turned out to be (the problem score). For each perspective, participants answered on a
9-point scale (1 = did not worry me at all/not problematic at all; 9 = worried me extremely/extremely problem-
atic) to the following 12 concerns: their own physical health, mental health, and safety, the physical and mental
health and safety of people they love, losing their job, not having enough money to survive, getting basic every-
day things (food, etc.), social unrest, separation from their family, a close person being violent.
Responses to the CCAQ showed that participants were most worried about the physical safety and mental
health of their loved ones. e extent to which these stressors occurred in reality showed that participants’ own
mental health and that of their loved ones were impacted (Table5). For all concerns measured, the worry score
was never exceeded by the actual problem score. at is, participants were consistently more worried about an
issue than that it turned out to be a problem. We see that such a worry-problem discrepancy is not evenly dis-
tributed across concerns from the CCAQ; below we provide evidence for two latent clusters of participants on
that worry-problem discrepancy.
Data Records
e RW3D dataset is available on the Open Science Framework at https://osf.io/9b85r/14.e repository also
contains all supplementary materials and a variable code book with detail and naming conventions for the full
dataset.
e dataset contains columns for emotion ratings, long and short texts, linguistic metadata (number of
characters, punctuation) and demographics separated per wave, indicated by the suxes ‘_wave1’, ‘_wave2’,
‘_wave3’. For data collected in wave 3, we additionally provide - where applicable - up to two descriptions of life
events and their associated impact ('life event’ variables), as well as all participants’ responses to the CCAQ scale
(‘ccaq’ variables). Please see the codebook for a full description of each column.
Technical Validation
is section describes (i) the steps taken to ensure data quality through participant exclusion criteria and (ii)
how data derivatives were obtained.
Data retention and exclusion. Aer each wave of data collection, we excluded participants based on
two text-based criteria: if the long text was not written in the English language, as determined with the cld R
package (https://cran.r-project.org/web/packages/cld3/index.html) or contained more than 20% punctua-
tion tokens, participants were excluded. e latter was applied to remove participants who lled their textual
response with superuous continuous punctuation (e.g., dots, commas, exclamation marks) to reach the charac-
ter length requirement. Both criteria were deemed necessary to ensure text data quality. For the third wave, the
English-language criterion resulted in the exclusion of 38 participants and the punctuation criterion in a further
four participants to be excluded (aer the English-language criterion was already applied). e retention over the
years was 70.3% and 67.1% in the second and third wave, respectively (see Supplementary Materials Table2 for
sample descriptives over the three waves).
Data derivatives. We obtained two kinds of derivatives from the data, one based on the text data and the
other on the emotion and CCAQ questionnaires. From the text data, we arrived at higher-order topics that pro-
vide an overarching theme for each written text and can be used to study what participants are writing about.
e psychological variables (emotion scales and CCAQ) were mapped to higher-order psychological constructs
Event Prop. M (SD) intensity Median intensity Prop. neg. intensity Prop. pos. intensity
no life event 66.15 NA NA NA NA
death 7.20 −8.43 (2.83) −10 [−10;6] 97.59 2.41
job 7.20 3.08 (6.3) 6 [−10;10] 28.92 69.88
romantic 3.12 −0.33 (7.95) −4 [−10;10] 52.78 47.22
family 3.04 −1.88 (7.48) −6 [−10;10] 65.71 37.14
reproduction 3.04 5.94 (5.97) 8 [−10;10] 17.14 82.86
health 2.52 −5 (6.38) −8 [−10;10] 75.86 24.14
move 2.34 5.85 (4.93) 8 [−8;10] 11.11 88.89
health of family 1.91 −7.91 (2.58) −8 [−10;0] 95.45 0.00
mental health 1.48 −1.18 (7.65) −4 [−10;10] 52.94 47.06
education 0.69 −2.75 (5.23) −5 [−8;4] 62.5 37.5
nancial 0.61 −0.29 (7.61) −4 [−10;10] 57.14 42.86
lifestyle 0.43 9.6 (0.89) 10 [8;10] 0.00 100.00
friendship 0.26 1.33 (9.87) 6 [−10;8] 33.33 66.67
Tab le 3. Summary of the life events data collected during the third wave with intensity (M, SD, Median) and
proportion of participants who indicated a positive and negative intensity, between −10 (very negative) to +10
(very positive).
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characterised by latent clusters of participants on the emotion change (from 2020 to 2021 and from 2021 to 2022)
as well as the discrepancy between their worry and problem score (i.e., the extent to which their worry about a
concern was aligned with how problematic that concern turned out to be).
Topics. To capture overarching themes in the text data, we constructed a correlated topic model using the stm
R package15 for the text data for each data collection wave. is probabilistic model is based on the assumption
that a piece of text consists of a mix of topics, which in turn are a mix of words with probabilities of belonging
to a topic15,16. Table6 shows the top three most prevalent topics per wave for the long texts (see Supplementary
Materials Tables4, 5 for a full list of topics and terms for long and short texts). We have assigned labels to each
topic based on the most frequent terms per topic.
Higher-order psychological clusters. Earlier work found evidence for latent clusters within the data in the
change of emotions from wave 1 to wave 210. We assessed whether there were additional emotion clusters in
this extended dataset and also in the discrepancy between the worry score and the actual problem score on the
concerns listed in the CCAQ. For each concept, we proceeded as follows (Fig.3): we took the delta value of the
emotion ratings for two time shis (wave 2 minus wave 1, and wave 3 minus wave 2) and used the delta between
the CCAQ worry score and problem score (worry minus actual problem score). For each change - emotion
change from 2020 to 2021, emotion change from 2021 to 2022 - and the worry-problem discrepancy, we then
ran k-means clustering17. We decided on the number of clusters through convergence of the scree plot and the
Silhouette method. For all three delta values, there was evidence of two clusters.
e emotion clusters (Table7) for the change from 2020–2021 were characterised by one group of partic-
ipants (44.4% of the sample) showing a marked improvement in emotional well-being, while another group
(55.6%) showed emotional responses resembling resignation (i.e., these participants reported higher anger,
Event category Example 1 Example 2 Example 3
death
We have lost 8 people, family and
friends, to covid only one with
underlying health condition. Very sad
time.
Death of grandparents My father passed away
job My wife got a new job
I got a new job during lockdown
which meant I was no longer forced
into Work 5 days a week but stay at
home half the week - I felt safer
Job loss due to government lockdown
romantic Getting into a committed relationship I have been dating the most beautiful
woman ever. breakup
family Having to move back home and isolate
caused me to realise how much I dont
like my family my son moved out My Mother in Law accusing me of not
letting her son have a vaccine.
reproduction Grandughter born has given us reason
to feel positive 2 years ago I had a baby We had our 2nd child a few days before
we went into the rst national lockdown
in March 2020
health
Getting Covid was a huge thing for me
and it scared me enough to know that I
never want to take risks and get it again
having been in hospital with it.
Finally had an operation I had been
waiting 2 1/2 years for. I’ve gained a lot of weight and my health
has suered too quite a lot
move I moved home to a new
apartment,needed a change of scenery
and life is good here. I bought a house with my husband moving to a dierent city
health of family
My father was ill, I live in Scotland and
he lives in England. I have to be careful
as I am immunocompromised, but
seeing my father was more important
than the risk to myself, so I travelled
My nan took seriously I’ll and we’ve
missed 2 years of being able to
visit and spend time with her she
has dementia and now no longer
remembers us
My father went blind and broke a bone
in his back in a fall which robbed him of
mobility as well as sight. It led to his latest
fall and a positive COVID test.
mental health I was diagnosed with depression. I started taking antidepressants
I stopped caring and putting my time and
energy into being invested in the daily
news of corona. Made my anxiety and
depression more stable.
education Started teacher training Pgce Going to university Finishing uni
nancial Loss of income Initial reaction of nancial markets
to the pandemic. I lost a large chunk
of my retirement savings.
Work dried up for my partner and he is
self employed so had an impact on our
daily life.
lifestyle I changed my lifestyle, started on a
low-carb diet, started exercising and
meditating and making time for myself.
It give me the push to do better with
my running and lose some weight
e key thing is to be kind to myself. Try
to eat healthily. Take some exercise where
we can. I had stick to sleep routine. Pace
myself. Take time to do the things I enjoy.
Even if I can’t go outside.
friendship
e groups of friends I felt close by
had moved away during the pandemic,
in the country I had been living in for
3 years.
End of a relationship with a best
friend, le one day and didnt see
them again. Break down of friendships
Tab le 4. Examples (verbatim)of life events per category.
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disgust and sadness but also lower worry and fear). e subsequent year’s clusters on the change from 2021
to 2022 showed again a well-coping group of participants (40.5%) with a very similar pattern to the earlier
well-coping cluster, with the exception of no increase in desire. is was juxtaposed with a maladjusted group
of participants (59.6%) who overlapped somewhat with the resignation cluster but which we termed dierently
due to this group’s increase in fear and anxiety and decrease in desire.
With regards to the worry-problem discrepancy clustering (Table8), the larger of the two clusters (58.2%)
was characterised by a markedly stronger “over-worry” (i.e., they indicated to worry about the various concerns
much more than that they turned out to be a problem). Over-worrying was particularly evident on questions
about the physical health and safety of loved ones. In contrast, the group that we termed the realistic worriers
(41.8%) show consistently lower worry-problem discrepancies. Merely on the questions about someone close
being violent (domestic violence) both groups were in agreement (low worry and low problem score).
Understanding and addressing the psychological impact of the COVID-19 pandemic, and possibly preparing
for the impact of future global crises, remains an ongoing research challenge. One of the impediments is high
quality data that connects dierent modalities of how individuals experienced the pandemic. e current dataset
paper introduced the RW3D, a repeated-measures dataset of UK participants, combining psychological varia-
bles examined via survey methods with rich textual responses. e explanatory relationships of coping in the
pandemic are yet poorly understood. With the RW3D, we can examine via panel models to what extent life
events, concerns raised in text data or socio-demographics changes (e.g., job loss) and variables (e.g., gender),
explain changes over time into the various emotional response styles. Gaining insights into these complex rela-
tionships could also be a way forward to target interventions at those who most need it. Importantly, the inclu-
sion of various control variables in the dataset allows researchers to control for potential confounds.
Moreover, we can also learn about some fundamental aspects of the relationship between text data and psy-
chological variables. By connecting the modalities, we can test to what extent ground truth emotions are pre-
dictable from text data and whether a lagged design can help anticipate emotion changes at a later moment
based on text data in previous years. Similarly, since we know about participants’ life events and stressors, we
can assess how these are retrievable from the text data. One implicit assumption of plenty of applied text-based
research is that these psychological variables are apparent from text data, but rich datasets to critically assess that
assumption are scarce.
Var iabl e Mworry SDworr y Mactual SDactual
Own physical safety 5.16 2.23 3.58 2.34
Own mental state 5.69 2.40 4.92 2.63
Own safety 4.73 2.24 2.72 1.96
Physical safety loved ones 6.85 1.94 4.33 2.45
Mental health loved ones 6.31 2.09 4.77 2.40
Safety loved ones 6.22 2.20 3.57 2.36
Losing job 3.45 2.52 2.45 2.24
Financial problems 5.06 2.59 3.88 2.63
Getting basics 4.65 2.32 3.55 2.33
Social unrest 4.45 2.14 3.10 2.13
Being separated from family 5.13 2.63 4.13 2.65
Violence close person 1.67 1.49 1.44 1.30
Tab le 5. Summary of worries about psychological stressors and how problematic each stressor turned out to
be (M, SD) on a scale of 1–9 (1 = did not worry me at all/not problematic at all; 9 = worried me extremely/
extremely problematic).
wave topic % documents terms
wave 1 rule following 10.29 peopl, feel, see, mani, die, govern, rule, think, wil l, follow
wave 1 how long will this last? 10.16 will, worri, feel, famili, normal, back, long, know, life, hope
wave 1 worry about loved ones 9.53 worri, also, famili, friend, time, anxious, work, home, feel, will
wave 2 hope for normality 15.04 will, feel, normal, hope, back, get, vaccin, thing, look, forward
wave 2 missing normality 14.38 work, want, friend, famili, feel, see, miss, time, abl, home
wave 2 anxiety 11.91 will, worri, vaccin, concern, also, feel, covid, anxious, eect, virus
wave 3 normal life 9.99 normal, back, now, life, live, covid, worri, feel, get, can
wave 3 still worried 8.70 covid, feel, still, worri, get, peopl, test, though, aect, don’t
wave 3 new variants 8.67 still, variant, vaccin, will, case, virus, new, concern, number, peopl
Tab le 6. Top 3 most prevalent topics for the long texts in each wave, with assigned labels and most frequent terms.
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Limitations. Some limitations need to be considered when making use of this dataset. First of all, the data
were collected from UK participants only. While this allows for a rich analysis of the UK due to country-specic
circumstances (e.g., infection spread, government responses), the results may not be generalisable to other pop-
ulations. Second, some variables were collected retrospectively requiring participants to report signicant life
events up to 2 years aer they happened and to think back about worries and actual problems of crisis coping.
ird, while there is considerable spread in demographics, the dataset does not make use of nationally represent-
ative sample. A way to mitigate that concern post-hoc might be to weigh sample characteristics according to their
prevalence in the UK population18.
Fig. 3 Clustering approach for two emotion changes and for the worry-problem discrepancy. e k-means
algorithm showed an ideal number of clusters for k = 2 as per the Elbow method and the Silhoutte method.
Emotion 2021 well-coping 2021 resignation 2022 well-coping 2022 maladjusted
anger −1.68 0.83 −2.38 0.56
anxiety −3.16 −0.11ns −2.82 0.30
desire 1.24 0.25 0.27ns −0.52
disgust −1.26 0.53 −1.74 0.51
fear −3.53 −0.53 −2.52 0.17
happiness 1.85 −0.14ns 1.65 −0.34
relaxation 2.21 −0.48 2.24 −0.53
sadness −2.77 0.56 −3.27 0.28
worry −2.40 −0.97 −1.79 −0.62
Size 44.4% 55.6% 40.5% 59.6%
Tab le 7. Means per emotion for the latent emotion clusters at the 2020–2021 and 2021–2022 change (all
sign. dierent from 0 at p < 0.01, two-sided, except for those with ns). A positive value denotes an increase in
the respective emotion score in the later wave, while a negative value denotes a decrease.e two wellcoping
clusters show similar patterns that suggest an increase in positive and a decrease in negative emotions. e
resignation (2021) and mal-adjusted cluster show some overlap but dier in their change of desire and fear.
Var iabl e Realistic worriers Over-worriers
Own physical safety 0.76 2.74
Own mental health 0.35 1.37
Own safety 1.17 3.17
Physical health loved ones 1.21 4.33
Mental health loved ones 0.69 2.72
Safety loved ones 1.35 4.47
Losing job 0.59 1.57
Financial problems 0.66 1.91
Getting basic needs 0.53 1.88
Social unrest 0.85 2.04
Separation from family 0.54 1.64
Domestic violence 0.22 0.24
Size 41.8% 58.2%
Tab le 8. Clustering on the worry-problem discrepancy (all sign. dierent from 0 at p < 0.01, two-sided).
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There is no custom code associated with this data descriptor. For data(pre)processing and obtaining data
derivatives, we used existing R packages. is included cld for English language checks, quanteda19 and stringr
(https://stringr.tidyverse.org/) for text metadata (number of characters, tokens, punctuation), the stm package15
for constructing topic models, and the factoextra package (https://rpkgs.datanovia.com/factoextra/) for the
determination of the number of clusters for obtaining the higher-order psychological constructs.
Received: 1 February 2023; Accepted: 2 August 2023;
Published: xx xx xxxx
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Author contributions
Both authors contributed equally to the conceptualisation of the data collection, the study design, the analysis of
the data and the writing and nalising of the manuscript.
e authors declare no competing interests.
Additional information
Supplementary information e online version contains supplementary material available at https://doi.org/
10.1038/s41597-023-02438-y.
Correspondence and requests for materials should be addressed to I.v.d.V.
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