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ORIGINAL RESEARCH
published: 11 March 2016
doi: 10.3389/fpsyg.2016.00283
Edited by:
Vivian Zayas,
Cornell University, USA
Reviewed by:
Jiyoung Park,
University of Massachusetts Amherst,
USA
Emre Selcuk,
Middle East Technical University,
Turkey
*Correspondence:
Laura Sels
laura.sels@ppw.kuleuven.be
Specialty section:
This article was submitted to
Personality and Social Psychology,
a section of the journal
Frontiers in Psychology
Received: 03 November 2015
Accepted: 13 February 2016
Published: 11 March 2016
Citation:
Sels L, Ceulemans E, Bulteel K
and Kuppens P (2016) Emotional
Interdependence and Well-Being
in Close Relationships.
Front. Psychol. 7:283.
doi: 10.3389/fpsyg.2016.00283
Emotional Interdependence and
Well-Being in Close Relationships
Laura Sels*, Eva Ceulemans, Kirsten Bulteel and Peter Kuppens
Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
Emotional interdependence—here defined as partners’ emotions being linked to each
other across time—is often considered a key characteristic of healthy romantic
relationships. But is this actually the case? We conducted an experience-sampling
study with 50 couples indicating their feelings 10 times a day for 7 days and
modeled emotional interdependence for each couple separately taking a dyadographic
approach. The majority of couples (64%) did not demonstrate strong signs of emotional
interdependence, and couples that did, showed great inter-dyad differences in their
specific patterns. Individuals from emotionally more interdependent couples reported
higher individual well-being than individuals from more independent couples in terms
of life satisfaction but not depression. Relational well-being was not (relationship
satisfaction) or even negatively (empathic concern) related to the degree of emotional
interdependence. Especially driving the emotions of the partner (i.e., sender effects)
accounted for these associations, opposed to following the emotions of the partner
(i.e., receiver effects). Additionally, assessing emotional interdependence for positive
and negative emotions separately elucidated that primarily emotional interdependence
for positive emotions predicted more self-reported life satisfaction and less empathic
concern. These findings highlight the existence of large inter-dyad differences, explore
relationships between emotional interdependence and key well-being variables, and
demonstrate differential correlates for sending and receiving emotions.
Keywords: emotional interdependence, emotion transmission, well-being, interpersonal relationships,
dyadographic
INTRODUCTION
Emotional interdependence, where the feelings of one person are related to the feelings of another
person, is often seen as a key characteristic of close relationships. Such interdependence transpires
in phenomena like synchrony (concurrent covariation of partners’ emotions; Butler, 2011;Liu et al.,
2013;Papp et al., 2013), being moved by one another (coupling; e.g., Boker and Laurenceau, 2007;
Butner et al., 2007), emotion transmission (e.g., Larson and Almeida, 1999) and so on. Moreover,
such phenomena are often considered defining elements of a healthy romantic relationship. But
is this really the case? Is emotional interdependence a characteristic of healthy couples? While
past research has certainly provided evidence for the existence of emotional interdependence
in romantic couples, the degree and correlates of emotional interdependence remain poorly
understood and documented. Yet, identifying the precise nature and correlates of emotion
interaction patterns between romantic partners can inspire theory about underlying mechanisms
and improve insight in the dynamical interplay of emotions between people. Additionally, it can
help to inform research on relational dysfunction, ultimately contributing to the improvement
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of therapies that focus on emotions, such as emotion focused
couples therapy (Johnson et al., 1999). Here, we apply a
dyadographic approach to study one aspect of emotional
interdependence (one partner’s emotions –the sender- affecting
the other partner’s emotions –the receiver- at a subsequent time
point), examine its occurrence based on data from real life, and
relate this form of emotional interdependence to both individual
and relational indicators of well-being, both in general as well as
separately in terms of sender- and receiver-effects.
For a long time, emotion research has primarily focused
on the intrapersonal aspects of emotions. Emotions mostly
originate from interpersonal contexts, however, and research
is increasingly shifting attention to how emotions emerge in
interactions between people and can become intertwined over
time, giving rise to complex interpersonal emotion dynamics
(Butler, 2011;Boiger and Mesquita, 2012;Butler and Randall,
2013;Kappas, 2013). These interpersonal emotion dynamics can
be observed in several phenomena, one of the most prominent
being that one person’s feelings may influence another person’s
feelings across time (see Butler, 2011 for an overview). Such a
temporally contingent association between people’s emotions has
been studied under various names, such as emotion transmission
(Larson and Almeida, 1999), emotion contagion (Hatfield et al.,
1994;Thompson and Bolger, 1999), susceptibility (Randall and
Schoebi, 2015), cross-lagged covariation (Butler, 2011), and
crossover (Westman, 2001). Summarizing, we will here refer to
it as emotional interdependence.
Emotional interdependence is particularly expected to occur
in the context of close relationships, such as parent-infant dyads
or adult intimate relationships. It is even often considered one
of the defining features of intimate relationships, with partners
continuously influencing each other’s emotions, cognition, and
behavior (Kelley et al., 1983;Berscheid and Ammazzalorso,
2001;Rusbult and Van Lange, 2003). There indeed seems to
be an abundance of evidence for the existence of emotional
interdependence in adult romantic relationships, especially for
negative emotions (see Larson and Almeida, 1999 for an
overview). For instance, stress that one partner experienced at
work can crossover to the other partner at home after they get
together (Westman, 2001).
Moreover, people seem to consider emotional
interdependence as a healthy feature of romantic relationships,
emphasizing the necessity of “being on the same wavelength”.
Literature about emotional similarity matches this idea,
reasoning that tuning emotions to one another has beneficial
interpersonal consequences (e.g., Wallbott, 1995;Keltner and
Kring, 1998;Anderson et al., 2003, 2004). It would help partners
to coordinate their behaviors and thoughts, making them
able to collectively respond to situations that demand action.
Additionally, it would increase mutual understanding and
feeling validated by the partner, promoting social cohesion,
attraction, and sympathy. In sum, tuning emotions to one
another is expected to be related to relational well-being, and
more specifically to relationship satisfaction and empathic
concern.
Furthermore, attachment researchers theorize that adult
partners “coregulate”, referring to a process in which partners
regulate each other’s affect and physiological arousal, resulting
in interwoven oscillating emotional patterns (e.g., Field, 1985;
Sbarra and Hazan, 2009;Butler and Randall, 2013). By
modulating each other’s emotions; partners would help to
maintain each other’s emotional stability, which is known to be
critical for psychological well-being (e.g., Kuppens et al., 2007;
Houben et al., 2015). Hence, based on this literature, we would
expect emotional interdependence not only to be related to
relational well-being, but also to individual well-being.
Although previous work clearly established the occurrence
of emotional interdependence in couples, one may wonder
whether it really is a defining feature of optimal close
relationships. Indeed, studies explicitly investigating intra- and
inter-variability in emotional interdependence suggest that
there are substantial inter-individual differences in couples’
interpersonal emotional patterns (Ferrer and Widaman, 2008;
Madhyastha et al., 2011;Steele et al., 2014). The specific pattern
of emotional interdependence seems to depend on the couple
under investigation, with a lot of couples evidencing emotional
independence. Currently, several factors have been associated
with variation in the amount of emotional interdependence in
couples. On a first, macro- level, emotional interdependence is
moderated by culture. For instance, Schoebi et al. (2010) found
emotional interdependence of anger only in couples that endorse
collectivistic values, and not in couples endorsing individualistic
values. Additionally, couples in arranged marriages show less
emotional interdependence (in the form of synchrony) than
couples in love marriages (Randall et al., 2011). On an
interpersonal level, the degree of emotional interdependence has
been associated with factors such as interpersonal insecurity and
perspective taking (Schoebi, 2008), and cooperation (Randall
et al., 2013). Also, related literature on covariation of concurrent
emotions (called synchrony) suggests that it varies depending
on time spent together of the partners (Papp et al., 2013) and
relationship quality (Saxbe and Repetti, 2010). On an individual
level, emotional interdependence is shown to be moderated
by distress (Randall and Schoebi, 2015) and attachment style
(Randall and Butler, 2013). Finally, on a micro level, there even
seems to be a biological basis for differences in susceptibility for
emotional interdependence, as people with a certain variant of the
serotonin transporter gene are influenced more by their partner’s
affect than others (Schoebi et al., 2012).
Taken together, the aforementioned findings suggest
substantial variability in couples’ emotional interdependence.
Still, existing research on this topic is limited in a number of
important ways. First, emotional interdependence has mostly
been investigated in a very confined manner, by looking at
connections between the same emotions of both partners (one
notable exception is Randall and Schoebi, 2015). Rarely did
research take into account that an emotion felt by one partner
may affect a different emotion in the other partner, such as for
instance emotions of opposite valence. One partner’s negative
emotions may increase the other partner’s negative emotions
(escalation), but also decrease this partner’s positive emotions
(dampening) (Butler, 2011). In other words, a multivariate
approach that looks at emotions across different channels is
needed to grasp the full dynamic interplay of different emotions.
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Second, there is a methodological consideration that existing
research on emotion interdependence and its relation with
well-being often did not take into account. Until now, research
investigating emotional interdependence most frequently
adopted a nomothetic perspective (implicit in for instance
the actor-partner interdependence model, Kenny et al., 2006),
with one model being fitted to data from all couples. However,
it is becoming increasingly clear that such a nomothetic
approach is no longer tenable because each dyad functions
uniquely and nomothetic analyses may fail to map intra-dyad
dynamics accurately (Molenaar, 2004;Ferrer and Widaman,
2008;Ferrer et al., 2012, 2013). As a result, Steele et al. (2014)
have called for a “dyadographic approach”, in which separate
analyses per dyad are taken as starting point and can be used
to elucidate possible nomothetic rules. At the same time, a
dyadographic approach can only be applied when the emotions
of every couple are sampled very intensively; which has become
possible only recently. For a long time, studies investigating
emotional interdependence in daily life have therefore looked
at emotions across days (e.g., Almeida et al., 1999;Thompson
and Bolger, 1999;Butner et al., 2007), but in recent experience
sampling studies emotions have been assessed four or six
times a day for 7 or 10 consecutive days (Schoebi, 2008;
Randall and Schoebi, 2015), which not only more closely
resembles the tiecourse of how emotions unfold (Butler and
Randall, 2013;Butler, 2015), but also allows for a dyadographic
approach.
Finally, it remains unclear if emotional interdependence is
indeed characteristic of more well-being, both on a relational
and individual level. In terms of evidence from emotional
similarity and attachment literature, it has indeed been found
that emotional similarity during conversations can predict
relationship satisfaction in the long term (Anderson et al.,
2003;Gonzaga et al., 2007). Yet, there is also research that
contests whether emotional interdependence is so desirable.
Lab research has repeatedly shown that high interdependence
between negative affect, called negative affect reciprocity, can be
detrimental for a romantic relationship (Levenson and Gottman,
1983;Gottman, 1998). In daily life, increased linkages of partners’
concurrent negative mood states have also been associated with
marital dissatisfaction (Saxbe and Repetti, 2010). However, a
recent experience sampling study examining how emotional
interdependence is related to well-being in the long term reached
different conclusions: Randall and Schoebi (2015) found that
individuals whose negative affect was influenced more by their
partner’s previous positive affect, showed less distress a year
later. The study of Randall and Schoebi (2015) is an important
step in gaining more insight into the relationship between
emotional interdependence and well-being, as it took into
account connections between different emotions, and assessed
emotional interdependence on a time scale of multiple times
(four) per day. Still, being more or less susceptible for one’s
partner (so called receiver effects) are only one angle from
which one can investigate the relation between emotional
interdependence and well-being. People’s emotions can not
only be predicted by the partner’s emotions, but also have
the ability to predict the emotions of the partner themselves,
reflecting so-called sender effects (Hatfield et al., 1994;Larson
and Almeida, 1999). This ability to drive a partner’s emotion
could be equally relevant to well-being, because it may increase
a person’s sense of efficacy and feeling supported by their partner.
Although being a sender has typically been related to individual
differences such as gender (e.g., Bolger et al., 1989;Larson and
Almeida, 1999), and emotional expressiveness (Hatfield et al.,
1994), its relation with well-being remains an untouched area.
Based on this reasoning, we want to investigate emotional
interdependence and well-being in the couple in general (looking
at the combination of receiver and sender effects), as well
as to what extent relations with well-being are a function of
whether one is one the sending or receiving end of emotional
interdependence.
Present Study
With this study, we aim to obtain a more complete picture of
how emotional interdependence takes shape in the daily life
of romantic couples and how it relates to well-being. To this
end, we conducted an experience sampling study, in which
romantic partners simultaneously reported on their emotional
experiences 10 times a day for 7 consecutive days. First, to
fully grasp emotional interdependence, we applied a multivariate
technique to the resulting data of each couple, with both
negative and positive emotions of the partner (influence of
the partner’s emotions) and the actor (influence of own prior
emotions) modeled simultaneously. In this way, we were able
to look at cross-partner influences between both similar and
different emotions. To prevent the problems associated with
a nomothetic approach, we took a dyadographic approach in
which we modeled emotional interdependence for each couple
separately. Next, we examined how individual and relational
well-being indicators relate to being more or less emotionally
interdependent; expecting a positive relation between well-being
and the degree of interdependence. We focused on two indicators
of individual well-being, namely depression and life satisfaction,
and two indicators of relational well-being, empathic concern
and relationship satisfaction. Additionally, we wondered if it
would matter for individuals from a more or less interdependent
couple that they are driving or following their partner’s emotions.
To assess this question, we made a distinction between sender
effects, or the extent to which individuals’ emotions predicted
their partner’s emotions over time, and receiver effects, or the
extent in which individuals’ emotions were predicted by their
partner’s emotions over time, and related these to the well-being
indicators. We also assessed if specific characteristics from the
couple, such as age and relationship duration were related to the
degree of interdependence shown.
MATERIALS AND METHODS
Participants
Couples were recruited through flyers, social media channels, and
advertisements in community and relationship therapy centers.
Inclusion criteria were: (1) in a relationship for at least two
months, (2) heterosexual, (3) over the age of 18, (4) both
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partners were willing to participate. To obtain a representative
sample, we selected couples of varying age, relationship duration,
and cohabitation status. In our final sample of 50 couples
(100 participants), relationship length varied from 2 months to
35 years (M=72.06 months, SD =107.79 months) and age
ranged from 18 to 70 years (M=27.75 years, SD =10.60 years).
Ten of these couples were married, 18 couples were not
married, but lived together, while 22 couples lived separately.
Most participants (94%) had the Belgian nationality and were
highly educated, with 60% holding a university degree. Each
participant received a monetary compensation of 40 euros for
their participation.
Materials
First, participants completed an online background questionnaire
that included demographic questions (e.g., about age and
relationship duration) and questions about well-being. Next,
participants took part in the experience sampling part of the
study, in which they answered questions about their feelings.
Descriptive statistics can be found in Table 1.
Individual Well-Being
Individual well-being was assessed in terms of depression
and life satisfaction. To measure depression, the Center for
Epidemiological Studies Depression Scale (CES-D; Radloff, 1977)
was used. The CES-D consists of 20 items and asks participants to
rate how frequently they have experienced depressive symptoms
over the past week, on a scale from 0 =rarely or none of the time
[less than one day] to 3 =most or all of the time [5–7 days]. The
CES-D is designed for non-clinical samples and has proven to be
a reliable and valid measure (Radloff, 1977).
The Satisfaction with Life Scale (SWLS; Diener et al., 1985;
Pavot and Diener, 1993) is a 5-item scale measuring participants’
global judgments of own life satisfaction. Participants indicated
for each item how much they agreed or disagreed on a 7-point
scale, with 1 =strongly disagree and 7 =strongly agree. This scale
shows good convergent validity with other scales and types of
assessments of subjective well-being (Pavot and Diener, 1993). In
our study, Cronbach’s alphas equaled 0.76 for the SWLS and 0.83
for the CES-D.
Relational Well-Being
Relational well-being was assessed in terms of empathic concern
and relationship satisfaction. Empathic concern was measured
by a subscale of the Davis’ interpersonal reactivity index (Davis,
1983), which consists of seven items that tap “other-oriented”
feelings of sympathy and the tendency to experience feelings of
warmth, and concern toward others. For instance, one such item
asked: “When I see someone being treated unfairly, I sometimes
don’t feel very much pity for them.” These items were answered
on a 5-point Likert scale ranging from Does not describe me well
to Describes me very well. Empathic concern scores are associated
with measures of emotionality and affective empathy (Davis et al.,
1994;De Corte et al., 2007). Cronbach’s alpha equaled 0.79.
The Relationship Assessment Scale (RAS; Hendrick,
1988;Hendrick et al., 1998) is a sound, quick measure of
global relationship satisfaction about one’s current romantic
TABLE 1 | Descriptive statistics and correlations for key variables.
Women Men Women Men
M (SD) M (SD) NE PE D LS RS EC NE PE D LS RS EC
Negative emotions (NE) 10.45 (8.71) 10.12 (8.46) − −
Positive Emotions (PE) 56.66 (10.47) 62.38 (12.07) −0.44∗∗ − −0.33∗−
Depression (D) 1.60 (0.36) 1.50 (0.28) 0.53∗−0.25†−0.34∗−0.27†−
Life Satisfaction (LS) 5.16 (0.89) 5.53 (0.82) −0.42∗∗ 0.28∗−0.47∗∗ − −0.23 0.26†−0.50∗∗ −
Relationship Satisfaction (RS) 4.24 (0.54) 4.36 (0.47) −0.50∗∗ 0.42∗∗ −0.40∗∗ 0.46∗∗ − −0.15 0.05 −0.22 0.32∗−
Empathic Concern (EC) 2.89 (0.54) 2.31 (0.62) 0.05 0.12 0.23 −0.21 0.03 − −0.01 0.07 −0.09 −0.12 0.25†−
†p<0.10; ∗p<0.05; ∗∗ p<0.01.
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relationship, consisting of seven items in which respondents
answer each item by a 5-point scale ranging from 1 =low to
5=high. Example items are “In general, how satisfied are you
with your relationship”, “How well does your partner meet your
needs?” and “How good is your relationship compared to most?”
The RAS has proven to be reliable (Hendrick, 1988;Vaughn
and Baier, 1999;Graham et al., 2011), and shows convergent
validity through high correlations with relationship satisfaction
measures such as the Dyadic Adjustment Scale (Hendrick,
1988;Vaughn and Baier, 1999), the Kansas Marital Satisfaction
Scale (Hendrick et al., 1998) and the Couples Satisfaction Index
(Funk and Rogge, 2007), and predictive validity by successfully
distinguishing between couples that split up and couples that
stay together (Hendrick, 1988;Vaughn and Baier, 1999). In this
study, Cronbach’s alpha was 0.82.
Assessment of Emotions in Daily Life
At each sampling moment, participants indicated how angry,
anxious, depressed, sad, relaxed, satisfied, happy, and cheerful
they felt by the use of a slider scale (from 0 =not at all to
100 =very much). These emotions were selected to represent the
four quadrants of the affective space, defined by the dimensions of
valence and arousal (Russel et al., 1989). We averaged responses
to the angry, anxious, depressed, and sad items to create a
measure for negative emotion (α=0.76, within person centered
across all time points). Responses to the relaxed, satisfied, happy,
and cheerful items were averaged to create a measure for positive
emotion (α=0.83, within person centered across all time points).
Contact Between Partners
At each signal, participants were also asked to indicate whether
they had had any contact with their partner since the last
beep (recoded into 1 =yes; 0 =no). On average, participants
individually reported having had contact with their partner in
73% of the assessment moments. We only considered couples as
having been in contact if the partners agreed about this, which
was the Cases for 88% of the assessment moments (calculated by
the number of beeps for which both partners stated they had been
in contact or both partners stated they had not been in contact,
divided by the number of beeps answered). This resulted in 4317
of the 6465 answered beeps for which couples agreed to have been
in contact with their partner (67%).
Procedure
First, couples received standardized information about the
study, gave their informed consent and completed a battery of
questionnaires, including the questionnaires described above.
Additionally, each partner received a Motorola Defy Plus
smartphone programmed with a custom experience sampling
application, was familiarized with its use and with how to answer
the experience sampling questions.
Subsequently, a signal prompted participants 10 times a day
for 7 consecutive days to answer several questions, including
the questions about their emotions. The time frame for these
signals ranged from 10 AM to 10 PM. The signals followed a
stratified random interval scheme with the time frame being
divided into 10 equal intervals, and one signal programmed
randomly in each interval. On average, signals were separated
by each other with 1 h, 12 min, and 12 s (SD =29 min and
2 s). The smartphones were synchronized to ensure simultaneous
signaling within each couple, but the order of questions was
random within each couple at each beep to avoid cooperation in
answering the questions. Compliance was high, with an overall
compliance rate of 92.03% (M=64.40 signals, SD =7.15
signals). This study was carried out in accordance with the
recommendations of the university’s Social and Societal Ethics
Committee.
RESULTS
Data-Analytic Procedure
To obtain dyadographic results, we applied lag-one vector
autoregressive (VAR) modeling to the data of each dyad
separately (Shumway and Stoffer, 2006). VAR models are
multivariate versions of autoregressive (AR) models, equipped
for modeling time dynamics for multiple variables within a
dyad. In these analyses, each variable is regressed on lag-one
versions of the same variable and of all other variables. In this
way, we can investigate the extent of partner-influence after all
intrapersonal effects of each participant are taken into account.
Specifically, the self-reported positive or negative emotions of
each participant at time tis predicted by the positive and negative
emotions of both partners at time t-1, with time t-1, and time
treferring to two consecutive signals within the same day. For
example, the negative emotion of the female partner of couple
iis regressed on lagged versions of own negative and positive
emotion (actor or intrapersonal effects), and her male partner’s
negative and positive emotions (partner or interpersonal effects).
We first modeled these coefficients and the intercepts as function
of two dummy variables, which allowed estimating the effects
separately for the moments that couples had been interacting
opposed to when they had not. However, five couples did not
have enough time points for which they had not been in contact,
resulting in missing values for these coefficients. Additionally,
in the remaining dyads, there was severe multicollinearity as (1)
shown by variance inflation factors above 100 for 35 couples, and
above 30 for all couples which is way above the common rule
of thumb of ten as cutoff score (Marquaridt, 1970) and (2) the
presence of one or more bivariate correlations between predictors
above 0.80 in all dyads. Therefore, we decided to not include
these dummy variables. As can be seen in Figure 1, this approach
yields 16 slopes per couple (i.e., 4 times 4), corresponding to
the unique direct effects of emotions at time t-1 on emotions
at time t. Eight of these slopes are actor effects, indicating how
much one’s emotion is predicted by own emotions at the previous
time point (represented by dashed arrows in Figure 1). The other
eight slopes are partner effects, and model over-time emotional
interdependence between partners (represented by solid arrows
in Figure 1). We performed a sensitivity analysis to evaluate
how large the unique effects of the predictors had to be, to be
detected by our VAR analysis. Given 48.34 time points (this
is the average amount of time points for each couple after
taking into account missed signals and signals omitted to avoid
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FIGURE 1 | Interpersonal emotion dynamics presented as a network.
The four nodes represent positive (PE) and negative emotions (NE) of both
partners. The arrows represent slopes, and thus the effects of emotions at
time t-1 on emotions at time t. Solid arrows correspond to slopes for
cross-partner connections, and thus partner effects, and dashed arrows to
within-partner connections or actor effects.
FIGURE 2 | Percentages of interdependent couples that evidenced
specific cross-partner connections.
over-night relations), the required effect size (f2) to reach a power
of 0.80 amounts to 0.17. This is considered a medium effect size
(Cohen, 1988). We inspected standardized slopes, as they do not
reflect the variances of the self-reported emotions (Bulteel et al.,
2016).
Emotional Interdependence
To investigate if a couple evidenced substantial interdependence,
we used the significance of the slopes representing partner
effects as a threshold (with p<0.05). More concretely, if
the regression slope for one of the partner effects reached
significance, this couple was considered to be substantially
interdependent. For the model in which all time points were
included, partner effects reached significance in only 18 of the
50 couples (36%). Among these couples, the specific nature
of interdependence differed tremendously. For instance, men
unidirectionally influenced women in 50% of these couples
(n=9), and women unidirectionally influenced men in 39%
of the couples (n=7). Bidirectional influence was only evident
in 11% of the couples (n=2). Additionally, in some couples,
emotions were solely predicted by partners’ similar emotions
(50%) while in other couples emotions were solely predicted
by partners’ emotions with the opposite valence (28%) and in
the remainder, they were predicted by both emotions (22%).
Finally, there were some couples in which both negative and
positive emotions were predicted by partners’ emotions (11%),
and others in which only negative emotions (39%) or positive
emotions (50%) were predicted. The direction of the influence
could be positive or negative depending on the specific couple,
regardless if the emotion was predicted by a similar emotion
or by an emotion with the opposite valence. Figure 2 shows
an overview of the percentage of couples in which particular
emotional interdependence paths were statistically meaningful.
It is noteworthy that women’s positive emotions predicted men’s
positive emotions more than that women’s negative emotions
predicted men’s negative emotions (17% for positive vs. 5.5%
for negative emotions) whereas in men, the opposite pattern
was present: men’s negative emotions predicted women’s negative
emotions more than that men’s positive emotions predicted
women’s positive emotions (22% for positive vs. 28% for negative
emotions). We tested if the occurrence of these patterns differed
by gender on the basis of Fisher’s exact tests, but these revealed no
significant gender differences for sending negative emotions to
negative emotions (p=0.10), nor for sending positive emotions
to positive emotions (p=0.50).
To investigate the potential influence of shared variance
between partner-effects on the magnitude of the unique partner
effects, we reran all analyses for VAR-models with three
predictors: both the emotions of the actor and only one emotion
of the partner. This approach yielded 24 slopes per couple (i.e.,
three times eight). In the VAR-models with three predictors,
partner effects reached significance in 20 of the 50 couples
(40%). Hence, shared variance in partner effects could not fully
account for the oftentimes weak partner-effects. Additionally,
we conducted VAR models in which we only included the
time points for which couples stayed in contact with each
other in between, to see if this mattered for the amount of
interdependence found. On average, this resulted in 31.54 time
points remaining for each couple (again, taking into account
missed signals and signals omitted to avoid over-night relations).
This implies that the required effect size (f2) to reach a power
of 0.80 now equaled 0.26 which is also considered a medium
effect size. Because one dyad only reported to have 15 time points
in which they had contact with each other, leading to missing
values for their estimated regression coefficients, this couple was
excluded from subsequent analyses. For the models in which
only the moments were included in which partners remained
in contact with each other, again only 18 of the 49 couples
showed substantial interdependence (37%), showing tremendous
variation in their specific patterns. We therefore decided to focus
on the models that took into account all time points, as these
models showed higher sensitivity and did not led to the exclusion
of couples.
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Sels et al. Emotional Interdependence in Romantic Couples
Emotional Interdependence and
Well-Being
To get a sense of the degree of interdependence in each couple,
we calculated the density of partner-effects, which stems from the
literature on networks (Newman, 2010). As shown in Figure 1,
over-time emotion dynamics can be represented in a network
where each emotion constitutes a separate node and the arrows
between the nodes indicate the over-time unique effects of the
emotions on one another (Bringmann et al., 2013;Pe et al.,
2015). The overall density of the network is a measure of
the strength of over-time connections between these nodes.
Specifically, this can be calculated by taking the mean absolute
strength of temporal connections between all the emotions. As
we were only interested in the density of partner-influences,
we calculated the average of the absolute values of the eight
slopes that represented partner effects for each dyad. This
measure makes us able to capture emotional interdependence in a
comprehensive way by taking into account interactions between
different emotions and in every direction of both partners.
The density measure is hence a between-dyad variable, being
the same for each partner of the couple but differing across
couples.
Next, we wanted to investigate the relationship between well-
being indicators and this density measure. To accommodate the
dyadic nature of the data, we applied multilevel models in which
the couple was the unit of analysis (Kenny et al., 2006). Each well-
being indicator was regressed on the couple’s density, and gender
was included as a main effect and an interaction effect, in this
way allowing and testing for gender differences. As can be seen
in Table 2, there were, however, no gender differences apparent.
Error variance was permitted to differ for men and women. We
conducted preliminary analyses in which we added the average
amount of contact couples reported to have during the study
as a control variable, but it did not affect any of our findings
(nor evidenced a main effect on the well-being outcomes), so we
dropped it from the final analyses.
Individual Well-Being and Density
Being part of a more or less emotionally interdependent
couple was not related to individuals’ self-reported depression
scores (see Table 2). However, individuals from couples that
were more emotionally interdependent reported higher life
satisfaction than individuals from couples who were more
independent.
Relational Well-Being and Density
The density of partner effects was negatively related to
empathic concern. This means that individuals from couples
that were more interdependent reported less empathic concern
than individuals from less interdependent couples (with no
gender differences in this effect). Being part of an emotionally
interdependent couple was not associated with individuals’ self-
reported relationship satisfaction.
Given that the relation between emotional interdependence
and well-being may vary as a function of the type of emotion
experienced, we examined relations between well-being and
emotional interdependence separately for positive and negative
emotions. In first instance, we calculated separate density scores
corresponding to cross-partner effects on an emotion with
a specific valence. Specifically, the density score for positive
emotions was calculated by averaging the four absolute slopes
representing cross-partner effects in the VAR models with
positive emotions as a dependent variable. The density score
for negative emotions was calculated by averaging the four
absolute slopes representing cross-partner effects in the VAR
models with negative emotions as a dependent variable. Then,
again multilevel models were applied. The density for positive
emotions was positively related to life satisfaction [β=4.44,
t=3.23, p=0.002, 95% CI(1.68,7.20)] and negatively to
empathic concern [β= −2.63, t= −2.52, p=0.02, 95%
CI(−4.72,−0.53)] but not to depression [β= −0.87, t= −1.51,
p=0.14, 95% CI(−2.03,0.29)] or relationship satisfaction
[β= −0.45, t= −0.44, p=0.66, 95% CI(−2.52,1.62)],
TABLE 2 | Density of partner-effects and relations with well-being.
Interactions with gender
B SE T p 95% CI B SE T p 95% CI
Individual well-being
CES-D
Intercept 1.72 0.12 14.61 <0.01 1.48;1.95 0.02 0.10 0.18 0.86 −0.18;0.22
Density −1.22 0.83 −1.48 0.15 −2.90;0.44 0.21 0.71 0.30 0.77 −1.22;1.64
Life satisfaction
Intercept 4.68 0.29 16.00 <0.01 4.09;5.27 0.14 0.28 0.49 0.63 −0.42;0.69
Density 4.91 2.07 2.38 0.02 0.8;9.06 −2.37 1.96 −1.21 0.23 −6.32;1.58
Relational well-being
Empathic concern
Intercept 3.16 0.21 15.05 <0.01 2.74;3.58 0.09 0.17 0.52 0.61 −0.26;0.43
Density −4.12 1.48 −2.78 <0.01 −7.10; − 1.15 1.50 1.21 1.24 0.22 −0.93;3.93
Relationship satisfaction
Intercept 4.54 0.21 21.89 <0.01 4.13;4.96 −0.14 0.13 −1.08 0.29 −0.40;0.12
Density −1.79 1.47 −1.23 0.23 −4.74;1.51 0.59 0.92 0.64 0.52 −1.26;2.45
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whereas the density for negative emotions did not relate to any
of the well-being variables (all p>0.05). Hence, emotional
interdependence only predicted more life satisfaction and less
empathic concern when it were the positive emotions that
were dependent on the partners’ emotions. To shed more
light on the precise processes behind the relation between
emotional interdependence and well-being, we next calculated
a stricter density for positive emotions by aggregating the
absolute slopes of the two cross-partner effects between positive
emotions, representing how much partners’ positive emotions
were influenced by each other’s positive emotions. The density
for negative emotions was calculated in a similar way, by
aggregating the absolute slopes of the two cross-partner effects
between negative emotions. This revealed that the density for
positive emotions was positively related to life satisfaction
[β=2.39, t=2.23, p=0.03, 95% CI(0.23,4.54)], and
marginally negative to empathic concern [β= −1.57, t= −1.99,
p=0.05, 95% CI(−3.16,0.02)], whereas for the density for
negative emotions, no relations with well-being outcomes were
found.
Additionally, we assessed if emotional interdependence varied
with specific aspects of the couple, such as age or relationship
duration (relationship duration was averaged across male and
female partner’s report, as r=0.99), but found no association
with any of these variables [age: β=19.18, t=0.54,
p=0.59, 95% CI (−52.71,91.07)]; relationship duration:
r=0.19 p=0.18). We also investigated if couples who
lived together showed more emotional interdependence than
couples who still lived separately, but found no significant
difference in the magnitude of the density scores, with Mcohabiting
couples =0.14, SDcohabiting couples =0.05, Mnon−cohabiting
couples =0.13, SDnon−cohabiting couples =0.04, t(48) =0.21,
p=0.83.
Sending and Receiving, and their
Relations with Well-Being
We next examined relations with well-being distinguishing
between sender effects that reflected the extent to which an
individuals’ emotions predicted their partner’s emotions over
time, and receiver effects that reflected the extent to which
individuals’ emotions were predicted by their partner’s emotions
over time. To this end, we computed two new density measures:
The density for sending was calculated by taking the mean
absolute values of the four slopes that represented how much each
individual’s negative and positive affect predicted their partner’s
subsequent negative and positive affect. The density for receiving
was calculated by taking the mean absolute values of the four
slopes that represented how much each individual’s negative and
positive affect was predicted by their partner’s previous negative
and positive affect (note that the receiver effects of one partner
equal the sender effects of the other partner, and vice versa).
We applied multilevel models in which each well-being
indicator was regressed on the receiving and the sending
density, including gender as a main effect and an interaction
effect with these densities (resulting in so called actor-partner
interdependence models; Kenny et al., 2006). Error variance was
allowed to differ for men and women. Results can be found in
Table 3, and as can be seen here, again no gender differences
appeared in any of the effects.
Individual Well-Being and Density
Nor the degree of receiving nor the degree of sending was
related to self-reported depression. The density for receiving
was not related to life satisfaction, but the density for sending
was positively related to life satisfaction. Hence, predicting
one’s partner’s emotions more was associated with more life
satisfaction compared to predicting one’s partner’s emotions less.
TABLE 3 | Density sending vs. receiving and relations with well-being.
Interactions with gender
B SE T p 95% CI B SE T p 95% CI
Individual well-being
CES-D
Intercept 1.72 0.12 14.27 <0.01 1.48;1.96 0.03 0.10 0.28 0.78 −0.18;0.24
Density sending −0.81 0.55 −1.47 0.15 −1.91;0.29 −0.01 0.55 −0.01 0.99 −1.09;1.09
Density receiving −0.44 0.54 −0.81 0.42 −1.50;0.63 0.17 0.53 0.32 0.75 −0.89;1.23
Life satisfaction
Intercept 4.69 0.30 15.61 <0.01 4.08;5.29 0.03 0.27 0.11 0.92 −0.52;0.58
Density sending 4.46 1.40 3.19 <0.01 1.68;7.24 −1.07 1.39 −0.77 0.45 −3.84;1.70
Density receiving 0.42 1.37 0.31 0.76 −2.31;3.15 −0.82 1.37 −0.60 0.55 −3.54;1.89
Relational well-being
Empathic concern
Intercept 3.21 0.21 15.24 <0.01 2.79;3.63 0.12 0.17 0.69 0.49 −0.23;0.47
Density sending −2.77 0.93 −2.97 <0.01 −4.62; − 0.91 −0.34 0.92 −0.36 0.72 −2.17;1.50
Density receiving −1.60 0.94 −1.70 0.09 −3.48;0.28 1.70 0.94 1.82 0.07 −0.16;3.56
Relationship satisfaction
Intercept 4.59 0.21 21.83 <0.01 4.16;5.01 −0.13 0.13 −0.98 0.33 −0.40;0.14
Density sending −1.17 0.87 −1.35 0.18 −2.90;0.56 −0.54 0.85 −0.63 0.53 −2.23;1.16
Density receiving −0.82 0.85 −0.96 0.34 −2.51;0.88 1.09 0.84 1.30 0.20 −0.57;2.75
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Relational Well-Being and Density
The density for receiving was not significantly related to an
individual’s empathic concern. Sending density related negatively
to empathic concern, thus individuals that influenced their
partners more reported less empathic concern. Both the density
for sending and the density for receiving were unrelated to
individuals’ relationship satisfaction.
Validation Analysis
Finally, we performed a validation analysis to establish that the
calculated density scores indeed can be considered to represent
emotional interdependence between partners over time. We
hypothesized that if the density score measures emotional
interdependence, breaking emotional interdependence would
decrease the magnitude of the density score. To test this,
we randomly mixed up the time points of the male member
in every couple while keeping the time points of the female
partner in the original chronological order, and calculated new
VAR models. Hence, our couples remained the same, but
density now described cross-partner effects for emotions on
different and random time points, on the basis of which no
emotional interdependence would reasonably be expected. We
expected that this mixing would mainly impact the density scores
in emotionally interdependent couples, and not so much in
independent couples, since there was not much interdependence
in these couples to begin with. Indeed, for the 18 couples
that were considered emotionally interdependent by the original
analyses, their new density scores were significantly lower than
their original scores [t(17) = −5.04, p<0.001, Mnew =0.10,
SDnew =0.04]. For the independent couples, the magnitude of
the new density scores did not differ significantly [t(31) =0.57,
p=0.57, Mnew =0.13, SDnew =0.11]. Additionally, both density
scores were unrelated to each other across couples (r=0.22,
p=0.13), and the new density score did not predict any of our
well-being indicators (all p>0.05).
DISCUSSION
With this study, we aimed to contribute to the understanding
of emotional interdependence in couples’ daily life and its
relation with well-being. A dyadographic approach revealed
that in fact the majority of couples under study did not
show strong evidence of emotional interdependence (64%).
Contact between partners during the day did not account
for this finding because analyses in which only these time
points were included, revealed similar results. This finding
might seem surprising, but in fact echoes other dyadographic
research on variability in emotional interdependence (Ferrer
and Widaman, 2008;Madhyastha et al., 2011;Steele et al.,
2014). Further, we found that when couples evidenced substantial
interdependence, the specific interdependence patterns varied
tremendously with associations between every emotion pair and
in every direction. Unidirectional interdependence, in which
only one partner influences the other partner across time, was
the predominant pattern, and only a few couples evidenced
bidirectional interdependence. Although it is recognized that
emotional interdependence can be unidirectional (e.g., Larson
and Almeida, 1999;Ferrer and Nesselroade, 2003;Gottman,
2013), bidirectional interdependence and reciprocity are often
considered to be the norm (e.g., Gottman, 2013).
Despite this observed heterogeneity, the strength of emotional
interdependence related to one aspect of individual well-
being, life satisfaction, and one aspect of relational well-being,
empathic concern. Individuals that were part of emotionally
more interdependent couples reported higher life satisfaction
and less empathic concern than individuals that were part of
more independent couples. Assessing emotional interdependence
for positive and negative emotions separately elucidated that
primarily emotional interdependence for positive emotions
predicted more self-reported life satisfaction and less empathic
concern. Additionally, analyses in which a subdivision was
made between the degree in which an individual’s emotions
drove (sender effects) or followed (receiver effects) the partner’s
emotions over time, revealed that it consistently was the
magnitude of sender effects that was positively associated
with life satisfaction, and negatively with empathic concern.
For life satisfaction, these findings were consistent with our
expectations, as we proposed that driving one’s partner’s emotion
would increase individuals’ sense of self-efficacy and feeling
supported by their partner, which are known to enhance well-
being (Bandura, 1997;Kuijer and De Ridder, 2003;Maisel and
Gable, 2009). Meanwhile, the findings for empathic concern
were unexpected. Less empathic concern was related to more
emotional interdependence in couples, and primarily on each
other’s positive emotions, and not to emotional influence on each
other’s negative emotion. Based on past research, empathy is
expected to be positively related to emotional interdependence,
and especially in negative emotions (Hatfield et al., 1994).
To explain these results, we think it is important to keep
in mind that empathic concern was only related to shaping
the partner’s emotions, and not to being susceptible for the
partner’s emotions. Although in the past being emotionally
influenced by people’s emotions has been related to empathy
(more specifically cognitive empathy or perspective taking)
(Hatfield et al., 1994;Schoebi, 2008), Hatfield et al. (1994)
did argue that driving people’s emotions would be related
to different characteristics. More concretely, powerful senders
would be people who (1) experience strong emotions, (2) possess
the ability to express emotions, and (3)-most relevant here-
are insensitive to emotions of others who are feeling different
emotions. It also appears that emotions tend to flow from
the person that has the most power to the person with less
power (Larson and Almeida, 1999;Anderson et al., 2003), and
the need for power is inversely related to empathic concern
(Koestner et al., 1990). Additionally, although these findings
were contrary to our expectations, they converge with another
recent counterintuitive finding in which an interpersonal factor
associated with less interpersonal concern, namely attachment
avoidance (Joireman et al., 2002), was associated with more
emotional interdependence in couples (Randall and Butler,
2013).
It is notable that being more or less sensitive for the emotions
of the partner, or so-called partner susceptibility (e.g., Randall
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Sels et al. Emotional Interdependence in Romantic Couples
and Schoebi, 2015), did not relate to well-being in our sample.
In the past, existing research often focused on this aspect
of emotional interdependence (e.g., Levenson and Gottman,
1983;Gottman et al., 1998;Schoebi, 2008;Schoebi et al., 2010,
2012;Randall et al., 2013;Randall and Schoebi, 2015) or on
emotional interdependence for the couple as a whole (by the
form of synchrony; Saxbe and Repetti, 2010;Papp et al., 2013),
while sender effects largely remained untouched. This type
of research has shown both positive (Randall and Schoebi,
2015) and negative relations (Levenson and Gottman, 1983;
Gottman et al., 1998;Saxbe and Repetti, 2010) between emotional
interdependence and well-being. However, these researchers all
investigated emotional interdependence for specific emotion
combinations between partners, such as between hard negative
emotions (Levenson and Gottman, 1983;Saxbe and Repetti,
2010) or between soft negative emotions and positive emotions
(Randall and Schoebi, 2015). In our study, such fine grained
distinctions were not assessed. It is possible that being able to
influence your partner’s emotions (this is, being a sender) is
beneficial in general, whereas the benefits of being susceptible for
partner’s emotions depend more on the specific circumstances or
ways in which this occurs. Of course, this remains speculative,
and needs further investigation.
Our findings have a number of implications for the study
of interpersonal emotion dynamics. First, the results about
the prevalence of emotionally independent couples and the
heterogeneity in emotional interdependence patterns support
the importance of applying a dyadographic approach (Ferrer
and Widaman, 2008;Madhyastha et al., 2011;Steele et al.,
2014). Additionally, they highlight an important direction for
future research; examining under which precise circumstances
interpersonal emotion connections occur, what forms they take
in what couples, and how and when presumed interpersonal
regulation processes play a role (Niven et al., 2009;Zaki and
Williams, 2013). Further, our study showed that emotional
interdependence can be investigated from different angles; by
looking at a couple as a whole or by taking an individual
perspective, and here by investigating the extent to which an
individual is susceptible for the partner’s emotions or the extent
in which he/she drives the partner’s emotion. Depending on the
angle, we showed differential relations with well-being indicators.
This implies the additional value of investigating all three, and of
identifying the specific mechanisms that underlie these.
The results of our study should, however, be considered
in light of several limitations. For one, we assessed emotions
in daily life in which no distinction could be made between
emotions whose precipating sources were within the relationship
versus emotions whose origins lied outside the relationship.
However, emotions directed at the partner can lead to a
totally different emotional interaction with the partner than
emotions directed toward something external to the relationship
(Berscheid and Ammazzalorso, 2001;Parkinson and Simons,
2012;Butler, 2015). Additionally, our findings are correlational
and consequently do not speak to the underlying mechanisms
of emotional interdependence. It is possible that the change
in the receiver’s emotion, following the sender’s emotion,
has nothing to do with the previous emotion of the sender
(see Larson, 2002). Also, our findings should be interpreted
in the context of the timescale on which emotions were
measured. Emotional interdependence can be investigated at
different timescales, ranging from second-to-second to longer
term, and the underlying mechanisms and processes of these
dynamics may not necessarily be the same. The use of time
lags also implies the necessity of choosing a time interval
for these lags. The time span we selected, allows for the
occurrence of various non-partner related events that can
override any immediate emotional influence of the partner.
It should be noted, however, that several studies that find
nomothetic evidence for emotional interdependence have used
similar or even larger time intervals, such as daily reports (e.g.,
Thompson and Bolger, 1999;Butner et al., 2007) or four time
points per day (Saxbe and Repetti, 2010;Randall and Schoebi,
2015). Additionally, we assessed empathy in our sample by
measuring individuals’ empathic concern in general, and not
individuals’ specific empathic concern for their relationship
partner (this is dyadic empathy). General and dyadic empathy
are, however, related to each other (Péloquin and Lafontaine,
2010), and are both associated with relationship satisfaction
(Franzoi et al., 1985;Davis and Oathout, 1987;Long and
Andrews, 1990), and positive relationship behaviors such as
providing social support (Devoldre et al., 2010) and forgiveness
to one’s partner (Paleari et al., 2005;Burnette et al., 2009).
Still, as noted by Péloquin and Lafontaine (2010), they do
not overlap completely, and it is important to see if similar
results would be found for dyadic empathy. Finally, our sample
was relatively small and homogeneous, in that it consisted
mostly of young, highly educated, Western European, unmarried
adults, which limits generalizability. For instance, Schoebi et al.
(2010) have found that emotional interdependence depends on
the endorsement of collectivistic values; with interdependent
couples, such as couples from collectivistic cultures, showing
more emotional interdependence than couples from more
individualistic cultures. This finding may help to explain why
we found so little emotional interdependence in our sample; and
suggests the need for future research to examine if emotional
independence is as prevalent in a more diverse, representative
sample of couples. Additionally, one would expect relationship
duration to be a moderator of emotional interdependence,
with people that are together for a longer time showing more
interdependence, although this did not seem to be the case in our
sample.
CONCLUSION
Examining emotional interdependence and its correlates in a
dyadographic way, we drew attention to several important factors
of interpersonal emotion dynamics such as the heterogeneity
in emotional interdependence, the potential differential relation
between interdependence and individual versus relational
wellbeing, and differences in sender versus receiver effects. In
doing so, the current study forms an important step to an in depth
understanding of interpersonal emotion dynamics and how it
relates to well-being.
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Sels et al. Emotional Interdependence in Romantic Couples
AUTHOR CONTRIBUTIONS
LS, EC, KB, and PK all contributed by – Substantial contributions
to the conception or design of the work; or the acquisition,
analysis, or interpretation of data for the work; AND – Drafting
the work or revising it critically for important intellectual
content; AND – Final approval of the version to be published;
AND – Agreement to be accountable for all aspects of the work
in ensuring that questions related to the accuracy or integrity of
any part of the work are appropriately investigated and resolved.
ACKNOWLEDGMENTS
This research was supported by the Research Fund of the
University of Leuven (Grants GOA/15/003; OT/11/031), by the
Interuniversity Attraction Poles programme financed by the
Belgian government (IAP/P7/06), and by a research grant from
the Fund for Scientific Research-Flanders (FWO, Project No.
G.0582.14 awarded to Eva Ceulemans, Peter Kuppens and Francis
Tuerlinckx). Kirsten Bulteel is a doctoral research fellow with the
Research Foundation-Flanders.
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Conflict of Interest Statement: The authors declare that the research was
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