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Special Issue: Relationships in the time of COVID 19
Coping with global
uncertainty: Perceptions of
COVID-19 psychological
distress, relationship quality,
and dyadic coping for romantic
partners across 27 countries
Ashley K. Randall
1
,GabrielLeon
1
,EmanueleBasili
2
,Tam
´
as Martos
3
, Michael Boiger
4
,
Michela Baldi
2
, Lauren Hocker
1
, Kai Kline
1
, Alessio Masturzi
2
, Richmond Aryeetey
5
,
Eran Bar-Kalifa
6
,SusanD.Boon
7
,LuisBotella
8
,TomBurke
9,10
, Katherine B. Carnelley
11
,
Alan Carr
10
,ArobinduDash
12
,MimiFitriana
13
,StanleyO.GainesJr.
14
,
Sarah Galdiolo
15
, Claire M. Hart
11
, Susanna Joo
16
, Barani Kanth
17
,
Evangelos Karademas
18
, Gery Karantzas
19
, Selina A. Landolt
20
,LouiseMcHugh
10
,
Anne Milek
21
,EddieMurphy
10,22
,JeanC.Natividade
23
,AldaPortugal
24,27
,
A
´lvaro Quin
˜ones
28
, Ana Paula Relvas
26,27
,PingkanC.B.Rumondor
29
,PetrutaRusu
30
,
Viola Sallay
3
, Luis Angel Saul
31
, David P. Schmitt
14
,LauraSels
32
, Sultan Shujja
33
,
Laura K. Taylor
10,34
,S.BurcuOzguluk
35
, Leslie Verhofstadt
32
, Gyesook Yoo
36
,
Martina Zemp
37
, Silvia Donato
38
, Casey J. Totenhagen
39
, Rahel L. van Eickels
37
,
Adnan Adil
33
, Emmanuel Anongeba Anaba
5
, Emmanuel Asampong
5
,
Sarah Beauchemin-Roy
40
, Anna Berry
10
,AudreyBrassard
41
,
Susan Chesterman
19
,LizzieFerguson
19
,GabrielaFonseca
26,27
, Justine Gaugue
15
,
Marie Geonet
42
, Neele Hermesch
21
, Rahmattullah Khan Abdul Wahab Khan
43
,
Laura Knox
19
, Marie-France Lafontaine
44
, Nicholas Lawless
19
,
Amanda Londero-Santos
45
,SofiaMajor
25,46
,TiagoA.Marot
23
, Ellie Mullins
19
,
Pauldy C. J. Otermans
14
, Ariela F. Pagani
47
, Miriam Parise
38
, Roksana Parvin
12
,
Mallika De
12
,KatherineP
´
eloquin
40
,B
´
arbara Rebelo
26,27
, Francesca Righetti
48
,
Daniel Romano
19
, Sara Salavati
7
,StevenSamrock
1
, Mary Serea
14
,
Chua Bee Seok
49
, Luciana Sotero
26,27
,OwenStafford
50
,
Christoforos Thomadakis
18
,CigdemTopcu-Uzer
51
, Carla Ugarte
52
,WahYunLow
53
,
Petra Simon-Z´
ambori
3
,ChingSinSiau
54
,Diana-S
ˆ
ınziana Duca
28
, Cornelia Filip
28
,
Hayoung Park
16
,SineadWearen
22
, Guy Bodenmann
20
, and Claudia Chiarolanza
2
1
Arizona State University, USA
2
Sapienza-University of Rome, Italy
3
University of Szeged, Hungary
4
University of Amsterdam, the Netherlands
5
University of Ghana, Ghana
6
Ben-Gurion University of the Negev, Israel
7
University of Calgary, Canada
8
Ramon Llull University, Spain
9
National University of Ireland Galway, Ireland
10
University College Dublin, Ireland
Journal of Social and
Personal Relationships
1–31
ªThe Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/02654075211034236
journals.sagepub.com/home/spr
J S P R
Abstract
Following the global outbreak of COVID-19 in March 2020, individuals report psychological
distress associated with the “new normal”—social distancing, financial hardships, and
increased responsibilities while working from home. Given the interpersonal nature of
stress and coping responses between romantic partners, based on the systemic transac-
tional model this study posits that perceived partner dyadic coping may be an important
moderator between experiences of COVID-19 psychological distress and relationship
quality. To examine these associations, self-report data from 14,020 people across 27
countries were collected during the early phases of the COVID-19 pandemic (March–July,
2020). It was hypothesized that higher symptoms of psychological distress would be
reported post-COVID-19 compared to pre-COVID-19 restrictions (Hypothesis 1),reports
of post-COVID-19 psychological distress would be negatively associated with relationship
quality (Hypothesis 2), and perceived partner DC would moderate these associations
(Hypothesis 3). While hypotheses were generally supported, results also showed
interesting between-country variability. Limitations and future directions are presented.
11
University of Southampton, UK
12
IUBAT—International University of Business
Agriculture & Technology, Bangladesh
13
International University of Malaya-Wales, Malaysia
14
Brunel University London, UK
15
University of Mons, Belgium
16
Yonsei University, Korea
17
Pondicherry University, India
18
University of Crete, Greece
19
Deakin University, Australia
20
University of Zurich, Switzerland
21
University of Mu¨nster, Germany
22
Health Service Executive, Ireland
23
Pontificia Universidade Cat `
olica do Rio de Jaineiro,
Brazil
24
University of Madeira, Portugal
25
Center for Research in Neuropsychology and
Cognitive Behavioral Intervention (CINEICC),
University of Coimbra, Portugal
26
Faculty of Psychology and Educational Sciences of
University of Coimbra
27
Center for Social Studies (CES-UC) of University
of Coimbra
28
Universidad de Tarapac´
a, Chile
29
Bina Nusantara University, Indonesia
30
University of Suceava, Romania
31
Universidad Nacional de Educaci´
on a Distancia,
Spain
32
Ghent University, Belgium
33
University of Sargodha, Pakistan
34
Queen’s University Belfast, UK
35
TED University, Turkey
36
Kyung Hee University, Korea
37
University of Vienna, Austria
38
Universit`
a Cattolica del Sacro Cuore-Milan, Italy
39
University of Alabama, USA
40
Universit´
e de Montr´
eal, Canada
41
Universit´
e de Sherbrooke, Canada
42
University of Louvain / Haute Ecole L ´
eonard de
Vinci, Belgium
43
Education University of Sultan Idris, Malaysia
44
University of Ottawa, Canada
45
Universidade Federal do Rio de Janeiro, Brazil
46
University of the Azores, Portugal
47
University of Urbino, Italy
48
VU Amsterdam, the Netherlands
49
Universiti Malaysia Sabah, Malaysia
50
Maynooth University, Ireland
51
University of Arizona, USA
52
Universidad Adolfo Ib´
an
˜ez, Chile
53
Universiti Malaya, Malaysia
54
Universiti Kebangsaan Malaysia, Malaysia
Corresponding author:
Ashley K. Randall, Counseling and Counseling
Psychology, Arizona State University, Tempe, AZ
85281, USA.
Email: Ashley.K.Randall@asu.edu
2Journal of Social and Personal Relationships XX(X)
Keywords
COVID-19, distress, dyadic coping, multination, relationship quality
Originating in Wuhan, China in December 2019, the coronavirus, commonly known as
COVID-19, quickly spread across the globe throughout 2020. Declared a global pan-
demic by the World Health Organization (WHO, 2020) on March 11, 2020, much of the
world was, and continues to remain, ill-equipped to face COVID-19 and its effects, with
over 3.7 million reported deaths as of June 10, 2021 (https://www.worldometers.info/
coronavirus/). Individuals across the world have reported increased stress since the start
of the pandemic and associated country restrictions; much of which is tied to both social
and economic concerns (Chiarolanza et al., under review).
The experience of stress and resulting coping efforts have important implications for
both individual and relational health, especially during COVID-19 (Pietromonaco &
Overall, 2020). In particular, the ways in which romantic partners rely on one another to
cope with stress are inextricably linked to risk of disease morbidity and mortality (Loving
& Slatcher, 2013). Indeed, individuals who perceive their partner to be responsive to them
in the face of stress report better sleep quality, show decreases in cortisol responses, and
report better relationship quality (for a review see Stanton et al., 2020). Given the
importance of romantic partners’ coping responses for mitigating stress’ deleterious effects
on individual and relational well-being (Randall & Bodenmann, 2017), drawing upon the
systemic transactional model of dyadic coping (Bodenmann et al., 2016), this study
examined how perceptions of partners’ dyadic coping behaviors moderated the association
between COVID-19 psychological distress and relationship quality across 27-nations
during the early stages of the COVID-19 pandemic (March–July, 2020).
Associations between psychological distress, relationship quality, and perceived
partner dyadic coping as a moderator
Experiences of stress are ubiquitous for individuals around the world, and chronic
experiences of stress are commonly associated with symptoms of psychological distress,
namely depression and anxiety (Goyal et al., 2014). According to Bodenmann’s (2005)
stress divorce model, one partner’s experience of stress can cause them [the stressed
partner] to retreat, thus decreasing the communication and quality time spent with their
romantic partner. Over time, if not dealt with, stress can cause both partners to experience
mutual alienation and disdain for one another, ultimately resulting in relationship dis-
solution. Family systems theorists acknowledge the interconnectedness between members
in a system, and in particular how members (here romantic partners) can work together to
mitigate stress’ deleterious effects (Bodenmann et al., 2016; Lazarus & Folkman, 1984).
According to the systemic transactional model (Bodenmann et al., 2016), romantic
partners play an important role in helping one another cope with stress when individual
resources are depleted. Once a partner (verbally or nonverbally) communicates their stress
to their partner (Partner B), Partner B evaluates and responds either positively (e.g.,
providing empathy) or negatively (e.g., dismissing the concern), a process defined as
dyadic coping (DC). As denoted above, a partner’s dyadic coping behavior can be
Randall et al. 3
classified as either positive or negative. Importantly, only positive DC is considered a
universally important relationship maintenance behavior (Randall & Messerschmitt-Coen,
2019); one that is associated with higher individual and relationship well-being (Falconier
et al., 2016).
While the systemic transactional model (Bodenmann, 2005) was originally developed
and subsequently applied to understand stress and coping processes in the face of nor-
mative daily stressors (for a review see Falconier et al., 2015), it has recently been applied
to understand the experience of more severe stressors, such as critical life events (Bod-
enmann et al., 2016). Nevertheless, exploring the critical role perceived partner DC may
have during the face of a major, ecological, stressor has largely remained unexamined (for
a notable exception see Bar-Kalifa, et al., in press). Responses to natural disasters, such as
the aftermath of the Great East Japan Earthquake, can be ambivalent in nature (Uchida
et al., 2014).Research from Uchida and colleagues (2014) found participants reported both
temporarily heightened negative affect as well as increased overall eudaimonic well-being;
the latter was related to participants’ valuing social connectedness more in the face of
uncertainty and disaster. This study suggests that perceived partner’s DC may be one way
in which people experience social connectedness, which may provide buffering effects
against psychological distress associated with COVID-19. While most research on
COVID-19 to date has examined individual and societal level coping efforts, to our
knowledge, this study is the first to investigate how romantic partners’ perceived one
another to help them cope with stress from the early phase of the COVID-19 pandemic
(March–July, 2020).
Present study
Perhaps for the first time in our history, the ongoing COVID-19 pandemic presents an
opportunity to examine how individuals around the world are experiencing a common
stressor. This exceptional, yet unfortunate, opportunity allows us to test fundamental tenets of
relationship science, specifically applied to the systemic transactional model of dyadic coping
(Bodenmann et al., 2016). As such, the goal of the present study was to test the following pre-
registered (https://osf.io/s7j52) hypotheses (H) in this 27-nation cross-sectional study.
H1: Given symptoms of psychological distress are common responses to threat,
such as the COVID-19 pandemic (WHO, 2021), it is hypothesized that higher
symptoms of psychological distress will be reported post-COVID-19 com-
pared to pre-COVID-19 restrictions.
H2: Given distress is negatively associated with relationship quality (Randall &
Bodenmann, 2017), it is hypothesized that post-COVID-19 psychological
distress will be negatively associated with relationship quality.
H3: Given the well-documented association between dyadic coping and relation-
ship quality (see Falconier et al., 2015 for a meta-analysis), it is hypothesized
that perceived partner DC will moderate the association between post-COVID
psychological distress and relationship quality, such that positive DC will
weaken the association (H3a), whereas negative DC will exacerbate the asso-
ciation (H3b).
4Journal of Social and Personal Relationships XX(X)
Romantic partners’ cultural contexts supply a “blueprint for how to cope: how
meaning is given to events, what is considered stressful, which coping behaviors are
acceptable, and what roles and competencies are valued” (Kayser & Revenson, 2016,
p. 287; see also Kim et al., 2008). Simply put, couples navigate emotional situations in
culturally specific ways (Boiger et al., 2020). For individuals around the world, positive
and negative DC have been found to be associated with beneficial and detrimental
outcomes, respectively (Falconier et al., 2016). Given the novelty of the situation, we did
not formulate predictions for specific cultural differences; however, these were explored
for each of the above hypotheses.
Method
The supplementary file contains specific country-level information related to IRB
approval, recruitment and participants, compensation, dates of data collection, and the
translation of measures, where applicable.
Participants
Participants had to meet the following inclusion criteria to participate: (1) at least
18 years of age, (2) in a romantic relationship for at least 1 year, and (3) living together
with their partner in their respective country.
1
A total of 14,020 people across 27
countries participated in the study. Most were female (n¼10,845; 77.4%), on average
36 years of age (SD ¼11.38) and self-identified as heterosexual (n¼12,040; 91.1%).
On average, participants reported being in a relationship for 11.37 years (SD ¼10.17).
Across the 27 countries, most participants were married (n¼7,466; 57.6%); 4,455 in a
committed relationship (34.3%), and 1,038 were engaged (8%). See Table 1 for specific
country-level information.
Procedure
Participants were recruited from various social media sites, such as Facebook, and
listservs in the respective countries. Interested participants were directed to online sur-
vey links that contained the informed consent and screening questionnaire to determine
eligibility. Eligible participants were automatically directed to the research ques-
tionnaire, which took approximately 30 minutes to complete.
Measures
Descriptive information for all measures appears in Table 2.
Psychological distress. Psychological distress related to pre-and post-COVID-19 restric-
tions was measured with the Depression, Anxiety, and Stress Scale-21 (DASS-21;
Lovibond & Lovibond, 1995). Participants responded to the items twice, once reflect-
ing on their experiences pre-COVID-19 restrictions and once reflecting on their
experiences post-COVID-19 restrictions. Participants rated 21 items (e.g., “I found it
hard to wind down”) on a 4-point Likert scale ranging from 0 ¼did not apply to me at all
Randall et al. 5
Table 1. Sociodemographic characteristics, gender and sexual orientation, relationship characteristics of participants.
Sociodemographic characteristics of participants
Age Time Married to Partner Time Known Partner
Time in Romantic Rela-
tionship
Have
Children
Currently
Student
Currently
Working
N M SD Range N M SD Range N M SD Range N M SD Range N % N % N %
North
America
and West
Europe
Austria 571 28.76 5.86 41 130 4.01 3.91 22.33 — — — — 554 5.84 4.12 33.08 581 18 581 53 403 87
Belgium 855 36.57 12.66 73 327 14.29 12.88 51.25 732 13.43 11.51 66.67 748 11.99 10.88 77.5 865 61 497 7 — —
Canada 272 36.33 11.94 65 97 13.57 12.40 53.75 219 12.38 10.45 57.5 227 10.64 9.32 53.75 299 40 — — — —
Germany 947 36.53 7.8 61 580 8.54 6.15 44.83 685 12.92 7.55 48.92 734 11.72 6.89 46.17 964 81 — — 923 67
Greece 501 36.84 12.15 58 237 15.63 11.59 54 491 13.16 11.49 55 479 11.94 11.44 55 487 44 — — — —
Ireland 849 36.23 10.4 58 333 8.41 9.14 53.67 605 57 9.07 57 630 9.33 8.3 56.5 850 64 838 15 850 63
Italy 606 41.53 11.61 52 435 15.47 11.71 45.67 826 16.69 12.4 59 828 14.69 11.49 51.17 850 52 — — 585 65
Netherlands 1046 34.22 11.81 57 309 11.13 10.30 50.25 876 10.87 9.35 51.33 910 9.68 8.79 49.75 487 44 1046 24 906 88
Portugal 528 39.41 10.07 51 270 14.58 10.86 45 523 16.64 10.72 51.83 525 14.44 10.15 49.17 536 56 535 7 498 86
Spain 364 39.83 10.22 55 364 7.69 10.08 50.5 365 15.55 10.6 52 365 13.63 10.15 49.42 365 44 365 26 365 64
Switzerland 419 35.49 12.09 68 144 12.59 11.82 57 371 12.36 11.09 64 381 10.41 9.9 59 419 36 419 34 419 70
United
Kingdom
391 35.3 13.26 64 158 13.29 12.20 53.92 357 12.05 11.14 55 361 10.53 10.49 54.08 395 36 — — — —
United
States
445 39.5 14.57 65 264 12.73 13.30 57.92 340 14.44 12.6 59.83 359 12.28 12 58.5 446 42 83 8 115 62
East Europe
Hungary 458 40.94 12.17 51 264 15.78 12.82 49.75 457 16.71 12.49 54.58 458 14.64 11.72 49.25 458 64 — — — —
Romania 537 36.89 10.34 53 471 13.34 9.684 49 — — — — 290 12.64 9.521 40 538 71 — — 381 57
Asia
Bangladesh 200 25.26 9.02 61 81 6.24 9.32 39.17 176 5.34 6.52 39.25 175 4.52 6.62 39.75 200 18 37 11 37 57
India 511 33.14 9.92 45 507 8.57 9.14 53 503 9.69 9.36 53 — — — — 511 38 — — 511 43
Indonesia 416 31.26 7.35 46 302 6.4 6.51 41.17 275 9.5 6.8 39.83 316 7.92 5.94 39.08 422 62 421 9 420 69
Malaysia 195 43.21 11.65 49 168 15.07 10.81 50.25 195 18.9 11.06 48.42 — — — — 195 81 195 14 195 57
(continued)
6
Table 1. (continued)
Sociodemographic characteristics of participants
Age Time Married to Partner Time Known Partner
Time in Romantic Rela-
tionship
Have
Children
Currently
Student
Currently
Working
N M SD Range N M SD Range N M SD Range N M SD Range N % N % N %
Pakistan 517 33.09 10.25 60 96 4.88 7.5 44 58 8.27 10.43 44 68 7.13 10.11 43.92 517 76 — — 517 74
South Korea 540 43.95 9.06 41 536 14.68 10.3 41.25 540 17.87 10.14 44.33 540 17 10.14 43.92 540 84 — — 540 81
Turkey 141 36.89 9.51 52 48 8.78 8.74 44 52 8.39 6.8 32.92 49 9.11 8.86 44.58 143 59 142 19 143 60
Middle East
Israel 575 28.15 6.81 68 479 2.09 6.07 49.92 515 6.22 6.16 52.92 544 5.65 5.89 48.92 574 15 — — — —
Africa
Ghana 304 38.34 7.95 43 182 10.45 7.55 40.08 149 11.98 7.34 37.83 145 9.79 6.72 34.33 310 19 250 26 248 71
Middle and
South
America
Brazil 662 39.87 11.38 59 501 12.68 11.4 56.25 520 15.02 11.55 62.58 547 13.36 10.86 55.25 668 53 668 31 668 70
Chile 424 42.14 9.67 52 424 11.83 9.56 47.75 424 16.82 10.85 68.75 424 14.12 9.81 54 424 68 424 8 424 78
Oceania
Australia 495 32.26 10.43 62.25 176 8.99 9.26 46.58 439 8.77 8.49 52.83 445 7.6 8.24 52.92 505 31 — — 504 73
Gender and sexual orientation of participants
Male Female Nonbinary Gender Fluid Other Heterosexual Bisexual Lesbian Gay Queer Other
N % N %N% N %N% N % N% N % N%N%N%
North
America
and West
Europe
Austria 71 12 508 87 2 0 0 0 0 0 507 87 49 8 8 1 7 1 5 1 5 1
Belgium 60 7 795 92 5 1 2 0 0 0 792 92 10 1 17 2 5 1 14 2 27 3
(continued)
7
Table 1. (continued)
Gender and sexual orientation of participants
Male Female Nonbinary Gender Fluid Other Heterosexual Bisexual Lesbian Gay Queer Other
N % N % N% N % N% N % N% N % N%N%N%
Canada 44 15 248 83 4 1 2 1 0 0 256 86 25 8 4 1 1 0 7 2 6 2
Germany 151 16 806 84 1 0 1 0 0 0 928 97 20 2 5 1 3 0 2 0 2 0
Greece 121 24 381 76 0 0 0 0 0 0 473 94 16 3 6 1 7 1 0 0 0 0
Ireland 124 15 724 85 1 0 0 0 1 0 761 90 31 4 17 2 26 3 6 1 8 1
Italy 273 32 572 67 3 0 0 0 1 0 801 94 17 2 6 1 22 3 2 0 2 0
Netherlands 65 6 973 93 5 0 4 0 0 0 870 83 110 11 24 2 8 1 12 1 23 2
Portugal 82 15 453 85 0 0 0 0 1 0 513 96 11 2 8 1 3 1 0 0 1 0
Spain 64 18 298 82 2 1 0 0 1 0 335 92 18 5 7 2 5 1 0 0 0 0
Switzerland 61 15 355 85 2 0 1 0 0 0 375 89 27 6 4 1 7 2 2 0 4 1
United
Kingdom
107 27 287 72 3 1 0 0 0 0 340 86 30 8 10 3 4 1 8 2 5 1
United
States
72 16 364 82 7 2 1 0 1 0 376 84 36 8 14 3 5 1 10 2 4 1
East Europe
Hungary 108 24 350 76 0 0 0 0 0 0 442 98 3 1 0 0 5 1 0 0 1 0
Romania 62 12 475 88 0 0 0 0 0 0
Asia
Bangladesh 104 51 96 48 1 0 0 0 1 0 158 78 12 6 1 1 20 10 0 0 3 2
India 149 29 362 71 0 0 0 0 0 474 93 20 4 0 0 0 0 4 1 13 3
Indonesia 85 20 336 80 0 0 0 0 1 0 371 91 6 1 1 0 1 0 2 0 26 6
Malaysia 45 23 150 77 0 0 0 0 0 0
Pakistan 216 42 301 58 0 0 0 0 0 0 517 100 0 0 0 0 0 0 0 0 0 0
South Korea 286 53 254 47 0 0 0 0 0 0 532 99 3 1 1 0 1 0 0 0 3 1
Turkey 31 22 110 77 1 1 0 0 0 0 117 84 8 6 0 0 0 0 1 1 13 9
Middle East
Israel 88 15 487 85 0 0 0 0 1 0 544 95 14 2 3 1 3 1 1 0 8 1
Africa
Ghana 155 50 154 50 0 0 0 0 1 0 245 84 24 8 0 0 0 0 0 0 23 8
(continued)
8
Table 1. (continued)
Gender and sexual orientation of participants
Male Female Nonbinary Gender Fluid Other Heterosexual Bisexual Lesbian Gay Queer Other
N % N %N% N %N% N % N% N % N%N%N%
Middle and
South
America
Brazil 165 25 497 74 4 1 1 0 1 0 594 89 43 6 14 2 15 2 1 0 1 0
Chile 114 27 306 72 2 0 1 0 1 0 408 96 3 1 1 0 9 2 0 0 3 1
Oceania
Australia 201 40 294 58 8 2 0 0 1 0 405 81 56 11 10 2 9 2 14 3 9 2
Relationship characteristics of participants
In a Committed Relationship Engaged—Living Together Married
N%N%N%
North America and West Europe
Austria 380 65 69 12 123 21
Belgium 330 38 131 15 386 45
Canada 144 49 28 10 122 41
Germany 236 25 50 5 676 70
Greece 238 47.5% 23 4.6% 240 47.9%
Ireland 316 38 83 10 373 45
Italy 331 39 33 4 462 54
Netherlands 620 59 70 7 357 34
Portugal 91 17 170 32 275 51
Spain 155 42 14 4 196 54
Switzerland 243 58 29 7 147 35
United
Kingdom
200 50 28 7 168 42
(continued)
9
Table 1. (continued)
Relationship characteristics of participants
In a Committed Relationship Engaged—Living Together Married
N%N%N%
United
States
118 27 30 7 297 67
East Europe
Hungary 143 32 40 9 264 59
Romania 81 15 34 6 420 79
Asia
Bangladesh 92 46 75 37 35 17
India* — — — — — —
Indonesia 56 13 10 2 355 84
Malaysia 17 9 4 2 174 89
Pakistan 0 0 0 0 517 100
South Korea 4 1 0 0 536 99
Turkey 20 14 4 3 116 83
Middle East
Israel 348 61 52 9 175 30
Africa
Ghana 27 9 7 2 276 89
Middle and
South
America
Brazil 78 12 29 4 560 84
Chile 153 36 10 2 261 62
Oceania
Australia 272 54 38 8 195 39
Note. * Did not administer specific demographic questions (i.e., missing data).
10
Table 2. Country-level descriptive statistics.
preDASS postDASS PRQC
M SD Range Alpha M SD Range Alpha M SD Range Alpha
North America and West Europe
Austria 7.83 7.30 0–42 .94 8.18 7.27 0–35 .94 112.14 13.78 30–126 .95
Canada 1.40 2.59 0–15 .86 10.34 7.67 0–40 .94 105.90 14.67 41–126 .95
Belgium 9.13 8.42 0–42 .94 10.88 9.90 0–45 .95 105.11 18.89 18–126 .96
Germany 6.96 5.55 0–30 .91 9.40 7.50 0–38 .94 102.05 19.58 18–126 .97
Greece 7.78 6.24 0–37 .92 7.80 6.46 0–33 .92 107.78 15.35 26–126 .95
Ireland 9.09 6.56 0–39 .92 9.73 7.78 0–43 .94 106.73 18.02 20–126 .96
Italy 10.26 5.62 0–39 .92 9.05 6.46 0–45 .94 107.35 17.54 32–126 .95
Netherlands 8.10 6.05 0–40 .91 9.90 7.47 0–45 .93 110.47 13.90 32–126 .94
Portugal* 8.29 7.20 0–43 .94 9.36 8.38 0–45 .96 30.24 5.58 5–35 .93
Spain 7.40 5.94 0–42 .93 8.46 6.16 0–42 .91 105.68 14.07 44–126 .94
Switzerland 6.45 6.70 0–42 .93 7.24 7.03 0–34 .93 105.98 16.22 31–126 .95
United Kingdom 9.45 6.99 0–41 .93 11.09 8.47 0–45 .95 106.38 18.80 25–126 .97
United States 8.94 6.57 0–43 .91 11.81 8.51 0–45 .94 107.63 14.89 18–126 .95
East Europe
Hungary 8.03 7.32 0–43 .93 10.68 8.63 0–42 .93 108.50 21.01 30–126 .95
Romania* 8.65 5.61 0–36 .91 8.65 6.52 0–41 .94 91.14 13.24 32–105 .96
Asia
Bangladesh 13.58 8.70 0–35 .93 12.57 10.08 0–39 .96 118.94 10.82 18–126 .93
India 7.31 7.84 0–41 .93 11.29 10.02 0–45 .94 110.12 18.91 27–126 .97
Indonesia 9.85 6.88 0–34 .92 9.81 8.29 0–43 .95 103.46 17.90 33–126 .95
Malaysia 3.79 5.75 0–45 .95 7.36 8.37 0–45 .97 100.76 17.23 34–126 .96
Pakistan 9.15 8.50 0–45 .94 8.44 9.49 0–45 .96 113.84 14.62 36–126 .95
South Korea 10.24 9.08 0–45 .96 9.34 9.90 0–45 .97 91.97 22.82 18–126 .98
Turkey 9.37 6.49 0–34 .92 9.86 8.84 0–44 .96 105.07 18.84 31–126 .96
Middle East
Israel 6.76 5.58 0–33 .90 8.65 7.30 0–44 .92 112.62 13.10 41–126 .95
(continued)
11
Table 2. (continued)
preDASS postDASS PRQC
M SD Range Alpha M SD Range Alpha M SD Range Alpha
Africa
Ghana 4.96 4.71 0–28 .87 4.27 5.63 0–38 .94 114.01 15.87 42–126 .94
Middle and South America
Brazil 9.51 8.02 0–41 .94 11.51 9.39 0–43 .95 102.19 17.81 20–126 .95
Chile 5.64 4.86 0–31 .90 8.49 7.14 0–45 .93 105.44 15.01 39–126 .95
Oceania
Australia 10.38 7.29 0–37 .94 11.43 8.69 0–44 .95 105.87 16.95 44–126 .95
Positive DC Negative DC
M SD Range Alpha M SD Range Alpha
North America and West Europe
Austria 3.70 0.75 1–5 .81 1.84 0.78 1–5 .74
Canada 3.71 0.73 1–5 .83 1.96 0.80 1–5 .80
Belgium 3.94 0.95 1–5 .89 2.24 1.06 1–5 .79
Germany 3.41 0.82 1–5 .85 2.00 0.87 1–5 .79
Greece 3.57 0.79 1–5 .83 2.19 0.83 1–5 .71
Ireland 3.73 0.76 1–5 .84 1.97 0.82 1–5 .77
Italy 3.52 0.81 1–5 .84 1.72 0.69 1–5 .75
Netherlands 3.71 0.60 1–5 .77 1.94 0.70 1–5 .71
Portugal 3.71 0.80 1–5 .88 2.06 0.80 1–5 .78
Spain 3.65 0.74 1–5 .84 2.08 0.80 1–5 .72
Switzerland 3.60 0.78 1–5 .82 1.75 0.72 1–5 .75
United Kingdom 3.61 0.76 1–5 .81 2.20 0.90 1–5 .75
United States 3.08 0.56 1–5 .56* 2.99 0.50 1–5 .14*
(continued)
12
Table 2. (continued)
Positive DC Negative DC
M SD Range Alpha M SD Range Alpha
East Europe
Hungary 3.48 0.89 1–5 .86 1.83 0.81 1–5 .74
Romania 3.63 0.86 1–5 .90 2.16 0.90 1–5 .81
Asia
Bangladesh 3.57 0.74 1–5 .70 3.08 0.76 1–5 .43*
India 3.79 0.99 1–5 .88 2.42 0.99 1–5 .70
Indonesia 3.70 0.80 1–5 .87 2.11 0.89 1–5 .78
Malaysia 3.49 0.88 1–5 .92 2.16 0.89 1–5 .82
Pakistan 3.68 0.79 1–5 .85 2.11 0.94 1–5 .73
South Korea 3.42 0.80 1–5 .89 3.37 0.88 1–5 .79
Turkey 3.57 0.83 1–5 .88 2.30 0.85 1–5 .72
Middle East
Israel 3.85 0.66 1–5 .76 1.78 0.67 1–5 .59*
Africa
Ghana 3.72 0.78 1–5 .90 2.14 0.80 1–5 .80
Middle and South America
Brazil 3.69 0.77 1–5 .85 2.15 0.83 1–5 .75
Chile 3.68 0.82 1–5 .86 3.92 0.80 1–5 .71
Oceania
Australia 3.71 0.71 1–5 .82 1.92 0.82 1–5 .80
Note. Alpha coefficients tend to underestimate true reliability (McNeish, 2018). As such, omega is reported for alpha coefficients below Nunnally and Bernstein’s (1994) rule-of-
thumb for acceptable alpha values (a¼.70); Positive DC in U.S., o¼.71; Negative DC in U.S., o¼.44; Negative DC in Bangladesh, o¼.69; Negative DC in Israel, o¼.64.
preDASS ¼symptoms of psychological distress rated prior to each country’s COVID-19 restrictions; postDASS ¼symptoms of psychological distress rated after restrictions
were in place; PRQC ¼Perceived Relationship Quality Component Inventory; Positive DC ¼perceived partner positive dyadic coping; Negative DC ¼perceived partner
negative dyadic coping; Portugal and Romania administered shorter versions of the PRQC, denoted in text.
13
to 3 ¼applied to me very much, or most of the time. A total score is calculated, where
higher scores reflect higher psychological distress. Reliabilities for pre-COVID-19
psychological distress scores ranged from .86 (Canada) to .96 (South Korea), with an
average aof .93 across countries. Reliabilities for post-COVID-19 psychological distress
scores ranged from .91 (Spain) to .97 (Malaysia and South Korea), with an average aof
.93 across countries. A multilevel confirmatory factor analysis demonstrated that the
structural models were invariant across within-country and between-country levels (see
supplementary file).
Perceived relationship quality. Relationship quality was measured using the Perceived
Relationship Quality Component Inventory (PRQC; Fletcher, 2000). Participants rated
18 items (e.g., “How happy are you with your relationship?”) on a 7-point Likert scale
ranging from 1 ¼not at all to 7 ¼extremely. A total score is calculated, where higher
scores reflect higher relationship quality. Reliabilities ranged from .93 (Bangladesh) to
.98 (South Korea), with an average aof .96 across countries.
Perceived partner DC. Perceptions of partner DC were measured using the Dyadic Coping
Inventory (DCI; Bodenmann, 2008), which assesses participants’ perceptions of their
partners’ coping behaviors when they are experiencing stress. Similar to Papp and Witt
(2010), perceived partner positive DC was calculated by averaging 2 items from each of
the three subscales of the DCI: emotion-focused coping (e.g., “My partner shows
empathy and understanding”), problem-focused coping (e.g., “My partner helps me to
see stressful situations in a different light”), and delegated coping (e.g., “When I am too
busy my partner helps me out”). Perceived partner negative DC was calculated by
averaging the 4-item negative DC subscale (e.g., “My partner blames me for not coping
well enough with stress”). Participants rated each item on a 5-point Likert scale ranging
from 1 ¼very rarely to 5 ¼very often. Reliabilities for positive DC ranged from .56
(U.S.) to .92 (Malaysia), with an average aof .85 across countries. Reliabilities for
negative DC ranged from .14 (U.S.) to .82 (Malaysia), with an average aof .79 across
countries.
Control variables
The analyses controlled for gender (coded as male/female) and one’s own self-reported
stress communication behavior, given that partner’s dyadic coping behavior is pre-
dicated on the notion that partners first communicate their stress to their partner (Bod-
enmann et al., 2016). Stress communication was measured using the stress
communication subscale in the DCI (Bodenmann, 2008).
Data screening procedures
After initial data screening by each country’s team, the resulting datasets were further
screened for indicators of careless responding (Bru
¨hlmann et al., 2020; Curran, 2016). In
each country datasets, three indicators were calculated for the responses of the psy-
chological scales (in sum, 114 items): percentage of missing responses, long string index
14 Journal of Social and Personal Relationships XX(X)
(i.e., the highest number of same responses consecutively in a row) and person-total
correlation (i.e., Pearson-correlation coefficient between the individual responses and
the sample level averages of the same items). The calculation of long string index was
based on 72 items, which included the DCI (37 items; Bodenmann, 2008), PRQC (18
items; Fletcher et al., 2000), and other measures not related to the present study.
Country-level distributions for person-total correlations (PTCs) and long string
indices (LSIs) were calculated. For PTC, we calculated the cutoff value according to the
following procedure: We searched for the lowest country-level average PTC (.78),
subtracted two standard deviations (2*.25) that resulted in a rounded .30 value which
was uniformly used for all country datasets. This cutoff value was more strict than 0.00
recommended by Bru
¨hlmann and colleagues (2020), however, the number of screened
cases was relatively low. For LSI, analysis showed that scores of 19 and above were
uncommon, which also met the recommendation of Bru
¨hlmann and colleagues (2020);
that is, more than half of the item number of the longest questionnaire (in our case, DCI
with 37 items). Finally, cases with missing responses above 25%were also considered as
ineligible for inclusion in the final dataset and the subsequent data imputation procedure
(Schlomer et al., 2010). Please see Table 2 in the supplementary file for the number of
cases screened by country.
Analytic plan
Hypothesis 1. It was hypothesized that all participants would report higher levels of
psychological distress post-COVID-19 restrictions compared to before these restrictions
were in place (i.e., pre-COVID-19). To test this, participant-level difference scores for
pre- and post-COVID-19 distress were computed to conduct an unconditional random
intercepts model that took the form of:
Difference in Psychological Distressij ¼b0þm0j þeij ð1Þ
where the outcome is difference in psychological distress for participant iin country j. b
0
represents the estimated average change in psychological distress across all countries, m
0j
represents the average deviation of participants in country jfrom b
0
, and e
ij
represents
the deviation of person ifrom the average change in psychological distress in country j.
All models were fit using restricted maximum likelihood in “lme4” (Bates et al.,
2020) in RStudio version 1.3.96 (RStudio Team, 2020). After fitting the random inter-
cepts model, the best linear unbiased predictions were used to recover country-specific b
coefficients (i.e., conditional modes). The conditional modes from each country can be
thought of as a weighted average between the average effect across all participants (i.e.,
the fixed effect) and the average effect for participants in country j(i.e., a least-squares
fit line to people in country j). Conditional modes were computed using a penalized
weighted least-squares estimation procedure written in the function “ranef()” in “lme4”
(see Bates et al., 2015 for a technical definition). The premise of this procedure is that, if
the variance of between-country effects is high, the country-specific least-squares fit line
will be weighted more heavily; conversely, if the variability in within-country effects is
high, the fixed effect from the model will be weighted more heavily. In sum, this pro-
cedure allowed us to derive country-specific coefficients with 95%confidence intervals
Randall et al. 15
to graphically depict differences in coefficients across countries (Figure 1, Figure 2,
Panels A and C). Because random effects are assumed to be normally distributed with a
mean of zero, the conditional modes were centered around the fixed effect estimate to
ease interpretation and to allow readers to distinguish between the fixed effect (dotted
line) and zero (solid line).
Hypothesis 2. It was hypothesized that there would be a negative association between
post-COVID-19 psychological distress and relationship quality. To test this, linear
mixed effects modeling was used to control for pre-COVID-19 psychological distress
(i.e., preDASS), gender, and stress communication, while allowing intercepts and slopes
Figure 1. The dotted line in panel A denotes the average difference (i.e., fixed intercept) in pre-
and post-COVID-19 (psychological) distress (b
0
¼2.33). Dotted lines in panels B, C, and D
represent the estimated fixed effect of each variable on relationship quality. Country-specific
coefficients (i.e., conditional modes) are centered around the fixed effect with 95% confidence
intervals.
16 Journal of Social and Personal Relationships XX(X)
to vary across countries. Prior to conducting the analyses, postDASS scores were dis-
aggregated into between- (i.e., country-level mean; postDASS
j
) and within-country (i.e.,
each participant’s deviation from their country-level mean; postDASS jpostDASS ij )
components. Moreover, intercepts and slopes were allowed to vary across countries for
all within-country predictors, pending model convergence.
To identify the optimal random structure, an unconditional random intercept model
with relationship quality as the outcome and country as the clustering variable was
Figure 2. Panels A and C illustrate the fixed effects for the interaction term and country-specific
coefficients represented by the dotted line and are centered around the fixed effect with 95%
confidence intervals. Panels B and D illustrate the interactions decomposed at þSD, mean, and -1
SD, respectively, and slopes are plotted with 95% confidence intervals. Post-COVID-19 (psy-
chological) distress is measured as the deviation of each participant from their country’s mean level
of post-COVID-19 distress.
Randall et al. 17
conducted. The intraclass correlation (ICC) for this model was 0.09, indicating that
approximately 9%of the variance in relationship quality could be explained by a per-
son’s country of residence. While low, the ICC was retained as a random intercept. Next,
the fixed effects for preDASS, gender, stress communication, postDASS
j
, and
postDASSjpostDASSij were added to the model. This model was a better fit than
the unconditional model, w
2
(5) ¼3240.5, p< 0.001. Next, a random effect for
postDASSjpostDASSij was added; however, this yielded multiple convergence
warnings. Following the suggestion of Bates et al. (2020), the model was fit using dif-
ferent optimizers to evaluate the consistency of estimates across models. If estimates are
relatively consistent across optimizers, this would suggest that convergence warnings are
admissible. Estimates and random effects across several optimizers were identical;
therefore, the SBplx algorithm in NLopt (i.e., NLOPT_LN_SBPLX) that uses local
approximation, and is gradient-free, did not trigger any convergence warnings was used
(Johnson, 2021). The final model converged with random effects for gender, stress
communication, and postDASSjpostDASSij , but not preDASS, and this model
proved to be a better fit than the model with only fixed effects and random intercepts,
w
2
(9)
¼
301.5, p< 0.001. Therefore, the final model took the form:
Relationship Qualityij ¼b0þb1preDASSðÞþb2GenderðÞþb3Stress Com:ðÞ
þb4ðpostDASSjÞþb5ðpostDASSjpostDASSijÞ
þm0j þm1j GenderðÞþm2j Stress Com:ðÞþm3jðpostDASS j
postDASSijÞþeij
ð2Þ
where the relationship quality of person iin country jis modeled by a fixed intercept (b
0
),
fixed effects for each predictor (b
1
...b
5
), a country-specific random intercept (m
0j
),
country-specific random effects (m
1j ...
m
3j
), and a person-specific residual error term (E
ij
).
Similar to the procedure outlined for H1, country-specific slope coefficients were
derived with 95%confidence intervals for postDASSjpostDASS ij , gender, and stress
communication (stress com.). These coefficients are represented in Figure 1, Panels B,
C, and D, respectively.
Australia, Portugal, and Romania. Key variables were missing from the Australian,
Portuguese, and Romanian datasets, which precluded including data from these countries
in the models above. Specifically, the Australian team did not include measures of stress
communication, and the Portuguese and Romanian teams used a shortened version of
relationship quality. To address this, individual multiple regression models were con-
ducted for participants from these countries, and the results are presented below.
Hypothesis 3. It was hypothesized perceived partner DC would moderate the association
between COVID-19 psychological distress and relationship quality. To test this, parti-
cipants’ perceived positive DC (PDC) and negative DC (NDC) were included in two
alternate models to test if perceived DC moderated the association between COVID-19
psychological distress and relationship quality. PDC and NDC were disaggregated into
18 Journal of Social and Personal Relationships XX(X)
between- (PDCj
;
NDCj) and within-country (PDC jPDC ij ;NDCjNDCij)
components.
PDC. Fixed and random effects were included for PDC j,PDC jPDC ij , and an
interaction term (PDCjPDCij *postDASS jpostDASS ij ). The model failed to
converge using various optimizers; therefore, random effects for gender and stress
communication were dropped, and the model converged successfully using the
NLOPT_LN_SBPLX optimizer. The final model fit better than the model depicted in
Equation 2, w
2
(3) ¼3339.8, p< 0.01, and took the form:
Relationship Qualityij ¼b0þb1preDASSðÞþb2GenderðÞþb3Stress Com:ðÞ
þb4ðpostDASSjÞþb5ðpostDASSjpostDASSijÞ
þb6ðPDCjÞþb7ðPDCjPDCijÞþb8ðPDC jPDCij
postDASSjpostDASSijÞþm0j þm1j ðpostDASSj
postDASSijÞþm2j ðPDCjPDCij Þþm3jðPDC jPDC ij
postDASSjpostDASSijÞþeij
ð3Þ
with fixed effects for PDC j(b
6
), PDCjPDCij (b
7
), and the interaction term (b
8
), and
random effects for PDC j(m
2j
) and the interaction term (m
3j
). Similar to hypothesis 1 and
2, country-specific interaction terms with 95%confidence intervals are depicted gra-
phically (Figure 2, Panel A).
NDC. Fixed and random effects for NDC j,NDC jNDC ij , and an interaction term
(NDCjNDCij *postDASSjpostDASSij were added to Equation 2 and the model
failed to converge. Therefore, similar to PDC, the random effects for gender and stress
communication were dropped and the model converged successfully, and fit better than
the baseline model from Equation 2, w
2
(3) ¼1694.8, p< 0.01. The final model took the
same form as Equation 3. Country-specific interaction terms with 95%confidence
intervals are depicted graphically (Figure 2, Panel C).
Results
Hypothesis 1. On average, participants reported higher psychological distress after the
COVID-19 restrictions were in place than before (b
0
¼2.33, 95%CI ¼[1.24, 3.41]).
However, there appeared to be nontrivial between-country variation in the extent to
which distress was perceived as higher after country-specific COVID-19 restrictions
were in place (m
0
¼2.81). To parse this variation, country-specific intercept coeffi-
cients were graphically represented in Figure 1, Panel A and centered around the
average difference in pre- and post-COVID-19 psychological distress (b
0
¼2.33;
depicted by a dotted vertical line).
A visual inspection of Figure 1, Panel A suggests that participants in 19 of 27
countries reported higher post-COVID-19 psychological distress (i.e., 95%CI were
above zero, depicted by a solid vertical line). On average, participants in 11 of
Randall et al. 19
27 countries (e.g., Canada, India, Malaysia, and the USA) reported differences in pre-
and post-COVID-19 psychological distress that were above-average when compared to
other countries (i.e., 95%CIs were above the dotted line). Conversely, participants in 5
of 27 countries did not report higher post-COVID-19 psychological distress (e.g.,
Greece, Indonesia, and Romania; 95%CI includes zero), and 3 of 27 countries reported
lower post-COVID-19 psychological distress (i.e., Italy, Pakistan, and South Korea; 95%
CI were below zero).
Hypothesis 2. On average, participants with higher stress communication reported higher
relationship quality (b
3
¼8.63, 95%CI ¼[7.58, 9.69]). Countries with higher post-
COVID-19 psychological distress reported neither lower nor higher relationship quality
(b
4
¼0.05, CI 95%¼[0.56, 0.45]). However, individuals who reported above-
average post-COVID-19 psychological distress relative to others in their country
reported lower relationship quality (b
5
¼0.18, CI 95%¼[0.25, 0.12]). All fixed
effects and random effects are reported in Table 3, and country-specific slope coeffi-
cients for post-COVID-19 psychological distress, gender, and stress communication are
depicted Figure 1, Panels B, C, and D, respectively.
Table 3. Parameter estimates for the model with relationship quality as the outcome (Hypothesis
2).$32#
95% CI
Fixed Effects Estimate SE Df t Lower Upper p
(Intercept) 76.34** 2.71 23.07 28.15 71.02 81.66 < .001
Controls
preC19 Distress 1.64** 0.20 11195.96 8.33 2.02 1.25 < .001
Gender 2.38** 0.59 18.76 4.01 3.54 1.22 < .001
Stress Comm. 8.63** 0.54 23.57 16.01 7.58 9.69 < .001
Predictors
postC19 Distress
(between)
0.05 0.26 22.35 0.21 0.56 0.45 0.83
postC19 Distress
(within)
0.18** 0.03 30.04 5.99 0.25 0.12 < .001
Correlations
Random Effects Var. SD (Intercept) postC19 Distress Gender
(Intercept) 164.90 12.84
postC19 Distress
(within)
0.02 0.12 0.02
Gender 5.25 2.29 0.30 0.37
Stress Comm. 6.25 2.50 0.97 0.01 0.40
Residual 214.87 14.66
Note. p < 0.05*; p < 0.01**; preC19 Distress ¼symptoms of psychological distress rated prior to each
country’s specific COVID-19 restrictions; postC19 Distress ¼symptoms of psychological distress rated after
these restrictions were in place; Stress Comm. ¼stress communication.
20 Journal of Social and Personal Relationships XX(X)
Overall, countries appeared to differ significantly in the association between post-
COVID-19 psychological distress and relationship quality. As shown in Figure 1, Panel
B, the negative association between post-COVID-19 psychological distress and rela-
tionship quality held in 18 out of 24 countries (i.e., 95%CIs were above zero). This
association was negligible in Bangladesh, Israel, Pakistan, South Korea, Turkey, and the
USA (i.e., 95%CIs includes zero), and was most pronounced in Germany, Hungary,
Indonesia, and Italy (i.e., 95%CIs were below dotted line—the average effect across
countries).
Hypothesis 3
Perceived Partner Positive DC. At the between-country level, countries that reported
above-average perceived partner positive DC relative to other countries reported higher
relationship quality (b
6
¼7.98, 95%CI ¼[0.52, 15.44]; similarly, individuals who
reported above-average perceived partner positive DC relative to others in their country
reported higher relationship quality (b
7
¼10.24, 95%CI ¼[9.02, 11.47]). Furthermore,
a significant positive interaction between perceived partner positive DC and post-
COVID-19 psychological distress indicated that, on average, the negative association
between post-COVID-19 psychological distress on relationship quality was attenuated in
those who perceived higher perceived partner positive DC relative to others in their
country (b
8
¼0.14, 95%CI ¼[0.09, 0.18]). Country-specific coefficients of this
interaction term are depicted in Figure 2, Panel B. Perceived partner positive DC
moderated the negative association between post-COVID-19 psychological distress and
relationship quality in 18 out of 28 countries (i.e., 95%CI were above zero). However,
the associations were negligible in Bangladesh, Canada, Chile, Ghana, and Spain (95%
CI includes zero) and were particularly pronounced in Greece and Hungary (95%CIs
were above the average effect for all other countries).
After decomposing the interaction at 1SD and þ1SD, as shown in Figure 2, Panel B,
simple slopes analyses revealed higher perceived partner positive DC mitigated the
negative association between post-COVID-19 psychological distress and relationship
quality. Specifically, slope of b
5
was not significantly different from zero in participants
who reported positive DC at þ1SD above country mean (b¼0.01, 95%CI [0.06,
0.03]). See Table 4.
Perceived Partner Negative DC. At the between-country level, perceived partner neg-
ative DC was not associated with relationship quality (b
6
¼1.20, 95%CI ¼[4.83,
2.42]; however, individuals who reported higher perceived partner negative DC relative
to others in their country reported lower relationship quality (b
7
¼5.60, 95%CI ¼
[7.31, 3.89]). Moreover, a significant negative interaction between negative DC and
post-COVID-19 psychological distress indicated that, on average, the negative associ-
ation between post-COVID-19 psychological distress on relationship quality was exa-
cerbated for those who reported higher perceived partner negative DC relative to others
in their country (b
8
¼0.06, 95%CI ¼[0.10, 0.02]).
Country-specific coefficients of this interaction term are depicted in Figure 2, Panel
C. Perceived partner negative DC exacerbated the negative association between post-
COVID-19 psychological distress and relationship quality in only 6 out of 28 countries
Randall et al. 21
Table 4. Parameter estimates for the model with perceived partner positive and negative DC as a moderator (Hypothesis 3).
95% CI
Fixed Effects Estimate SE Df t Lower Upper p
(Intercept) 96.94** 1.22 42.39 79.53 94.55 99.33 < .001
Controls
preC19 Distress 1.37** 0.17 8108.99 8.08 1.70 1.04 < .001
Gender 1.83** 0.30 11641.25 6.11 2.42 1.25 < .001
Stress Comm. 3.12** 0.17 11819.56 18.39 2.79 3.45 < .001
Predictors
postC19 Distress (between) 0.44 0.24 22.80 1.81 0.04 0.91 0.08
postC19 Distress (within) 0.12** 0.02 35.25 5.79 0.16 0.08 < .001
Positive DC (between) 7.98* 3.81 19.66 2.10 0.52 15.43 0.04
Positive DC (within) 10.24** 0.63 22.86 16.36 9.02 11.47 < .001
PDC (within)* postC19 (within) 0.14** 0.02 16.77 6.47 0.09 0.18 < .001
Correlations
Random Effects Var. SD (Intercept) postC19 Distress Positive DC
(Intercept) 25.25 5.03
postC19 Distress (within) 0.01 0.07 0.31
Positive DC (within) 8.50 2.92 0.80 0.71
PDC (within)* postC19 (within) 0.01 0.08 0.09 0.63 0.54
Residual 162.65 12.75
95% CI
Fixed Effects Estimate SE Df t Lower Upper p
(Intercept) 81.60** 1.19 33.90 68.38 79.27 83.94 < .001
Controls
preC19 Distress 0.92** 0.18 10962.08 5.03 1.28 0.56 < .001
(continued)
22
Table 4. (continued)
95% CI
Fixed Effects Estimate SE Df t Lower Upper p
Gender 2.11** 0.32 11929.83 6.56 2.74 1.48 < .001
Stress Comm. 7.21** 0.15 11931.43 46.74 6.91 7.52 < .001
Predictors
postC19 Distress (between) 0.24 0.39 21.43 0.61 1.00 0.53 0.55
postC19 Distress (within) 0.10** 0.02 33.14 4.15 0.14 0.05 < .001
Negative DC (between) 1.20 1.85 20.75 0.65 4.83 2.42 0.52
Negative DC (within) 5.60** 0.87 23.18 6.42 7.31 3.89 < .001
NDC (within)* postC19 (within) 0.06* 0.02 22.41 2.67 0.10 0.02 0.01
Correlations
Random Effects Var. SD (Intercept) postC19 Distress Negative DC
(Intercept) 24.54 4.95
Post-C19 Distress (within) 0.01 0.08 0.26
Negative DC (within) 17.44 4.18 0.13 0.04
NDC (within)* Post-C19 (within) 0.01 0.09 0.03 0.16 0.27
Residual 185.90 13.63
Note. p < 0.05*; p < 0.01**; preC19 Distress ¼symptoms of psychological distress rated prior to each country’s specific COVID-19 restrictions; postC19 Distress ¼symptoms
of psychological distress rated after these restrictions were in place; Stress comm. ¼stress communication; DC ¼dyadic coping; PDC ¼positive dyadic coping; NDC ¼negative
dyadic coping.
23
(i.e., Belgium, Greece, Hungary, India, Ireland, and South Korea). This association was
particularly pronounced in Belgium, Ireland, and South Korea (95%CIs were below
average interaction effect). As shown in Figure 2, Panel D, analysis of the simple slopes
suggests that there was a negative association between post-COVID-19 psychological
distress and relationship quality for participants who reported high perceived partner
negative DC at þ1SD (b¼0.14, 95%CI [0.20, 0.09]) or at their country’s mean
(b¼0.10, 95%CI [0.14, 0.05]). However, when participants reported low per-
ceived partner negative DC at 1SD for their country (b¼0.05, 95%CI [0.11, 0.01],
this association was no longer statistically significant. See Table 4b.
Australia, Portugal, and Romania—Moderating effects of DC. For participants from Australia,
perceived partner positive DC did not significantly moderate the association between
post-COVID-19 psychological distress and relationship quality (b¼0.02, 95%
CI ¼[0.10, 0.14]); however, perceived partner negative DC did moderate this asso-
ciation (b¼0.12, 95%CI ¼[0.23, 0.01]). Specifically, the association between
post-COVID-19 psychological distress and relationship quality was nullified when
participants reported mean-level (b¼0.11, 95%CI ¼[0.28, 0.06]) or low negative
DC (i.e., 1SD; b¼0.01, 95%CI ¼[0.22, 0.19]).
For participants from Portugal, neither perceived partner positive nor negative DC
moderated the association between post-COVID-19 psychological distress and rela-
tionship quality.
For participants from Romania, perceived partner positive DC significantly moder-
ated the association between post-COVID-19 psychological distress and relationship
quality (b¼0.22, 95%CI ¼[0.12, 0.32]). High perceived partner positive DC buffered
the negative association between post-COVID-19 psychological distress and relation-
ship quality (b¼0.02, 95%CI ¼[0.14, 0.17]). Perceived partner negative DC did not
moderate the association between post-COVID-19 psychological distress and relation-
ship quality.
Discussion
Given the global effects of the COVID-19 pandemic, the current study used a large
multinational sample across 27 countries to examine whether perceived partner dyadic
coping moderated the association between COVID-19 psychological distress and rela-
tionship quality during the early phases of the pandemic (March–July, 2020). It was
hypothesized that COVID-19 psychological distress, associated with the country-level
restrictions put in place, would be associated with higher self-reported psychological
distress, compared to self-reports of psychological distress before these restrictions.
Additionally, we examined whether reports of COVID-19 psychological distress would
be negatively associated with relationship quality, and whether perceived partner dyadic
coping moderated this association. Given national responses and community resources in
coping with the pandemic have differed (e.g., Gelfand et al., 2020), along with cultural
ideas and practices around preferred ways of coping with stress (Kim et al., 2008), we
explored cultural variation in the strength of these associations across countries.
24 Journal of Social and Personal Relationships XX(X)
Overall, hypotheses in the study were largely supported. In most (not all) countries,
participants reported more psychological distress after COVID-19 country-level
restrictions were implemented compared to before, and reports of psychological dis-
tress were associated with lower relationship quality. Importantly, and in line with prior
research on dyadic coping (e.g., Falconier et al., 2016), perceived partner positive dyadic
coping buffered the negative association between post-COVID-19 psychological distress
and relationship quality for most participants in our sample. Not surprisingly, perceived
partner negative dyadic coping exacerbated the negative association between post-
COVID-19 psychological distress and relationship quality; however, this association
was only found in a subset of participating countries (i.e., Australia, Belgium, Greece,
Hungary, India, Ireland, and South Korea).
For participants from Bangladesh, Canada, Chile, Ghana, and Spain, perceived
partner positive dyadic coping did not moderate the association between post-
COVID-19 psychological distress and relationship quality. For Bangladesh, post-
COVID-19 psychological distress was not significantly associated with relationship
quality; however, for the remaining countries (i.e., Canada, Chile, Ghana and Spain),
we could not identify a simple unifying factor that could account for these results.
There were no clear commonalities among these countries in terms of economic/
community resources in coping with the pandemic, the government response, the
extent of the pandemic, or larger cultural values that may explain why perceived
partner positive dyadic coping did not moderate the association between post-
COVID-19 psychological distress and relationship quality. It is possible, however,
that systemic differences in baseline distress across different countries (e.g., related to
poverty, population density, access to safe food and water) may explain some of the
differences. Additionally, although efforts were made to align data collection as much
as possible, there were some differences between countries as to when data were
collected, which may also explain some of the country-level differences we found.
Please refer to the supplementary file for the dates of data collection across countries.
However, because participants in each country were asked their perception of their
own psychological distress and examined associations between individuals’ levels of
distress relative to the average levels of distress among individuals in their country,
between- and within-country differences were examined separately. Doing so allowed
us to draw conclusions about individuals’ COVID-19 psychological distress ratings
without overgeneralizing across populations.
Strengths, limitations, and future directions
A cross-sectional design was implemented wherein participants were asked to reflect
on their symptoms of psychological distress prior to their country’s COVID-19
restrictions (i.e., pre-COVID-19 psychological distress), and again following these
restrictions (i.e., post-COVID-19 psychological distress) during the early phases of
the pandemic (March–July, 2020). While the DASS-21 (Lovibond & Lovibond,
1995) is widely used to measure psychological distress, it has not been validated to
examine perceptions of distress pre- and post- a specific time (here COVID-19
restrictions). By implementing the DASS-21 in this way, results demonstrated
Randall et al. 25
perceived differences in participants’ psychological distress from pre- to post-
COVID-19 country-level restrictions. Further, in controlling for pre-COVID-19
psychological distress ratings, although assessed retrospectively, results reflected how
post-COVID-19 psychological distress, above and beyond pre-COVID-19 reports, was
associated with relationship quality, and whether this association was moderated by
perceived partner positive DC.
Based on research conducted with the systemic transactional model of dyadic
coping across cultures (Falconier et al., 2016), the inclusion criteria focused on indi-
viduals who were in a relationship for at least 1 year and living with their partner,
which limits the ability to generalize these results to other couples, especially those
who may have been isolated from their partner and/or experiencing additional stressors
due to their minority status(es) as examples. Additionally, while a valid attempt was
made to adapt the study’s measures to the current COVID-19 context, we acknowledge
the context to which existing psychological phenomena are being applied may affect
the reliability of such measures. For example, the Dyadic Coping Inventory (DCI;
Bodenmann, 2008) asks participants to respond to how they and their partners cope
with stress in the context of their relationship. While the DCI has traditionally been
applied to understanding the presence of common, relatively minor stressors (Falconier
et al., 2016), the current COVID-19 pandemic is undoubtly associated with a multitude
of stressors; therefore, how each participant responded to the scale prompt of “stress”
likely differed.
Importantly, given the cross-sectional nature of the data, temporal associations
between partners’ stress communication and coping responses could not be examined.
For example, it is unclear how the progression of the COVID-19 pandemic, and its
unpredictability from day-to-day, impacted perceptions of stress (or eustress), given the
ongoing changes to individuals’ daily lives—from working remotely, to home schooling
children, to facing continued lockdowns and associated restrictions. Additional research
on the reliability of such measures, especially within a longitudinal design and applied to
the context of a global pandemic, is warranted.
Finally, and perhaps most importantly, future research is encouraged to explore the
cultural variation in these results. While beyond the scope of the current study’s purpose
and available data, it is important to acknowledge how contextual factors such as
available community resources, government responses, or the dynamic of the pandemic
itself may have have impacted participants’ perception of stress and coping. Overall, our
results show that perceived partner positive dyadic coping may be helpful in moderating
the association between COVID-19 psychological distress and relationship quality
across countries. However, it is possible that participants from certain cultural contexts
may benefit from specific types of positive dyadic coping compared to others. For
example, the study of close relationships in Asian contexts found people generally avoid
the disclosure of stressful events or feelings when seeking or providing social support
(Kim et al., 2008). As such, helping partners with tasks (i.e., engaging in delegated
dyadic coping) may be more beneficial than helping one to analyze the problem (i.e.,
problem-focused dyadic coping) or showing empathy (i.e., emotion-focused dyadic
coping) in mitigating symptoms of psychological distress.
26 Journal of Social and Personal Relationships XX(X)
Conclusion
Based on self-report data collected from over 14,000 individuals across the world,
results from this study advance the understanding of how romantic partners experi-
enced and reported coping with stress during the early phases of the COVID-19
pandemic (March–July, 2020). These multination data point to the importance of
partners’ positive dyadic coping behaviors in mitigating the associations between
COVID-19 psychological distress and relationship quality, which further highlights
positive dyadic coping as a generalizable relationship maintenance behavior that may
buffer the damaging effects of stress (Randall & Messerschmitt, 2019), especially
when community coping resources are low (Gelfand et al., 2020). Nonetheless, it is
important to acknowledge that given cultural differences in how people communicate
stress and seek support (Kim et al., 2008), there are likely additional, mediating factors,
that can further explain these associations. These mediating factors include, but are not
limited to, the types of stress that are associated with elevated symptoms of psycho-
logical distress, individuals’ coping responses,andpropensitytocommunicatethe
stress (verbally or nonverbally) to one’s romantic partner. Identifying how romantic
partners experience and respond to stress within their relationship will enable psy-
chologists, mental healthcare providers, and policymakers to identify couples with
enduring vulnerability (e.g., those experiencing low levels of dyadic coping), and tailor
clinical recommendations in coping with major stressors, such as those in the face of
global pandemics.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/
or publication of this article: This research was supported by funding from American Psycholo-
gical Association’s Office of International Affairs (PI: Randall).
ORCID iDs
Ashley K. Randall https://orcid.org/0000-0003-3794-4163
Tam´as Martos https://orcid.org/0000-0001-5946-1299
Michela Baldi https://orcid.org/0000-0002-5110-4331
Lauren Hocker https://orcid.org/0000-0002-6372-8520
Alessio Masturzi https://orcid.org/0000-0002-5640-291X
Susan D. Boon https://orcid.org/0000-0003-4366-1100
Luis Botella https://orcid.org/0000-0003-3794-5967
Alan Carr https://orcid.org/0000-0003-4563-8852
Arobindu Dash https://orcid.org/0000-0003-4642-512X
Sarah Galdiolo https://orcid.org/0000-0002-8912-3116
Susanna Joo https://orcid.org/0000-0003-0304-8459
Selina A. Landolt https://orcid.org/0000-0002-1643-4642
Louise McHugh https://orcid.org/0000-0002-2526-4649
Eddie Murphy https://orcid.org/0000-0002-7276-7628
Pingkan C. B. Rumondor https://orcid.org/0000-0002-0778-929X
Viola Sallay https://orcid.org/0000-0003-1326-1704
Luis Angel Saul https://orcid.org/0000-0002-6351-8283
Laura Sels https://orcid.org/0000-0002-3485-9599
Randall et al. 27
Laura K. Taylor https://orcid.org/0000-0002-2353-2398
Martina Zemp https://orcid.org/0000-0003-0065-5966
Rahel L. van Eickels https://orcid.org/0000-0002-7461-2929
Emmanuel Anongeba Anaba https://orcid.org/0000-0002-8942-0460
Sarah Beauchemin-Roy https://orcid.org/0000-0002-5572-3658
Anna Berry https://orcid.org/0000-0001-7618-6866
Audrey Brassard https://orcid.org/0000-0002-2292-1519
Susan Chesterman https://orcid.org/0000-0002-5128-036X
Gabriela Fonseca https://orcid.org/0000-0001-7210-5491
Justine Gaugue https://orcid.org/0000-0003-4511-0660
Marie Geonet https://orcid.org/0000-0002-2007-7764
Neele Hermesch https://orcid.org/0000-0002-3487-6604
Laura Knox https://orcid.org/0000-0002-4339-2660
Marie-France Lafontaine https://orcid.org/0000-0003-4185-6326
Nicholas Lawless https://orcid.org/0000-0002-3621-1518
Amanda Londero-Santos https://orcid.org/0000-0003-3536-0834
Sofia Major https://orcid.org/0000-0002-4643-2170
Tiago A. Marot https://orcid.org/0000-0002-4491-4993
Pauldy C. J. Otermans https://orcid.org/0000-0001-8495-348X
Ariela F. Pagani https://orcid.org/0000-0002-7149-9350
Miriam Parise https://orcid.org/0000-0003-2150-6636
Mallika De https://orcid.org/0000-0002-5402-3385
B´arbara Rebelo https://orcid.org/0000-0003-3887-5892
Francesca Righetti https://orcid.org/0000-0002-5126-1388
Daniel Romano https://orcid.org/0000-0002-4213-9831
Steven Samrock https://orcid.org/0000-0001-9145-6422
Mary Serea https://orcid.org/0000-0001-9573-1321
Chua Bee Seok https://orcid.org/0000-0002-9394-4638
Luciana Sotero https://orcid.org/0000-0001-8393-2775
Owen Stafford https://orcid.org/0000-0002-4752-6515
Christoforos Thomadakis https://orcid.org/0000-0002-7689-7577
Petra Simon-Z´ambori https://orcid.org/0000-0001-6930-4537
Cornelia Filip https://orcid.org/0000-0002-1829-8453
Hayoung Park https://orcid.org/0000-0002-4840-6910
Claudia Chiarolanza https://orcid.org/0000-0002-8726-4724
Supplemental material
Supplemental material for this article is available online.
Open research statement
As part of IARR’s encouragement of open research practices, the authors have provided the
following information: This research was pre-registered: https://osf.io/s7j52/. The data used in
the research are available. The data can be obtained by emailing the first author at Ashley.K.Ran-
dall@asu.edu. The materials used in the research are available. The materials can be obtained by
emailing the first author at Ashley.K.Randall@asu.edu.
28 Journal of Social and Personal Relationships XX(X)
Note
1. Inclusion criterion were selected based on prior research conducted with the systemic transac-
tional model of stress and coping (STM; Bodenmann et al., 2016). The STM is predicated on
partners’ interdependence, wherein partners living together have greater opportunity for shared
experiences of stress communication and associated coping behaviors.
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