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TYPE Original Research
PUBLISHED 28 March 2024
DOI 10.3389/fnhum.2024.1331859
OPEN ACCESS
EDITED BY
Jared J. McGinley,
Towson University, United States
REVIEWED BY
Marianne Richter,
Université de Fribourg, Switzerland
Eleonora Gentile,
Azienda Sanitaria Localedella Provincia di
Barletta Andri Trani (ASL BT), Italy
*CORRESPONDENCE
Simran K. Johal
skjohal@ucdavis.edu
RECEIVED 01 November 2023
ACCEPTED 09 February 2024
PUBLISHED 28 March 2024
CITATION
Johal SK and Ferrer E (2024) Variation in
emotion dynamics over time is associated
with future relationship outcomes.
Front. Hum. Neurosci. 18:1331859.
doi: 10.3389/fnhum.2024.1331859
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terms.
Variation in emotion dynamics
over time is associated with
future relationship outcomes
Simran K. Johal*and Emilio Ferrer
Department of Psychology, University of California, Davis, Davis, CA, United States
Romantic relationships are defined by emotion dynamics, or how the emotions
of one partner at a single timepoint can aect their own emotions and the
emotions of their partner at the next timepoint. Previous research has shown
that the level of these emotion dynamics plays a role in determining the state and
quality of the relationship. However, this research has not examined whether the
estimated emotion dynamics change over time, and how the change in these
dynamics might relate to relationship outcomes, despite changes in dynamics
being likely to occur. We examined whether the magnitude of variation in
emotion dynamics over time was associated with relationship outcomes in a
sample of 148 couples. Time-varying vector autoregressive models were used to
estimate the emotion dynamics for each couple, and the average and standard
deviation of the dynamics over time was related to relationship quality and
relationship dissolution 1–2 years later. Our results demonstrate that certain
autoregressive and cross-lagged parameters do show significant variation over
time, and that this variation is associated with relationship outcomes. Overall,
this study demonstrates the importance of accounting for change in emotion
dynamics over time, and the relevance of this change to the prediction of future
outcomes.
KEYWORDS
dyadic interactions, emotion dynamics, dynamic modeling, generalized additive
modeling, vector autoregressive model, non-stationarity, romantic relationships
Variation in emotion dynamics over time is
associated with future relationship outcomes
Romantic relationships can be defined by the emotional interdependence of the
two people involved (Kelley et al., 2002;Vallacher et al., 2005). Indeed, relationships
can be viewed as “temporal interpersonal emotions systems,” such that the emotion
state of one partner at one timepoint influences how the other partner feels at the
same or future timepoint (Butler, 2011;Lougheed and Hollenstein, 2018). Although this
interdependence of emotional states has been given different names (e.g., synchrony,
reciprocity, transmission, contagion, coregulation, and coupling), we will use the term
emotion dynamics to refer to the influence that one partner’s emotions have on either
their own emotions or their partner’s across time. Over time, these moment-to-moment
emotion dynamics can reveal important aspects of the couple, such as the quality of their
relationship (Granic, 2005;Lougheed and Hollenstein, 2018).
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Much work has examined how emotion dynamics relate to
important outcomes in romantic relationships (Gottman and
Levenson, 1992;Gottman, 1994;Gottman et al., 1998;Saxbe and
Repetti, 2010;Castro-Schilo and Ferrer, 2013;Sels et al., 2016;
Gonzales et al., 2018). However, little work has been done to
understand how change or variation in these dynamics over time
can also contribute to such outcomes, despite the expectation that
such change could or should occur. The rest of the introduction
focuses on reviewing the relation between emotion dynamics and
relationship outcomes. We then discuss why it is important to
consider variation in emotion dynamics and outline the goals of
the current study.
Association of emotion dynamics with
relationship outcomes
Numerous studies have shown that emotion dynamics exist
within many romantic relationships, and that these dynamics
are associated with the state of the relationship. Emotions and
stresses of one member of a couple can influence the emotions and
stresses of their partner, and the level of this influence can vary
depending on several factors, such as characteristics of the couple,
the context in which the couple is interacting, or the particular
emotions being studied (Bolger et al., 1989;Ferrer and Nesselroade,
2003;Butner et al., 2007;Schoebi, 2008;Saxbe and Repetti, 2010;
Randall and Butler, 2013;Randall and Schoebi, 2015;Sels et al.,
2016).
These emotion dynamics can relate to the state of the
relationship, such as how satisfied the couple feels or whether
they remain together, with some of the most notable research
in this area having been done by Gottman and colleagues. They
found that greater exchange of negative emotions between a marital
couple during a conversation (as evidenced by couples engaging
in conflict, displaying negative behaviors such as being critical of
their partner, or reciprocating their partner’s negative behaviors)
was related to the risk of marital dissolution and marital satisfaction
(Gottman and Levenson, 1992;Gottman, 1994;Gottman et al.,
1998). Along similar lines, couples where the wives’ negative affect
was more strongly related to their husbands’ negative affect were
less likely to be satisfied with the marriage (Saxbe and Repetti,
2010).
The association between emotion dynamics and relationship
outcomes is not limited to just the influence between negative affect
states, however. Ferrer et al. (2012) found that emotion synchrony
of a couple, conceptualized as when the two partners reported
being in similar affective states, predicted whether the couple
remained together 1 or 2 years later. Castro-Schilo and Ferrer
(2013) found that dynamic parameters describing the interactions
between each partner’s positive and negative affect were predictive
of relationship quality, but not relationship dissolution, above and
beyond their level of affect. Gonzales et al. (2018) found that
dynamic parameters relating the female partner’s affect to her
male partner’s affect were predictive of relationship dissolution.
Finally, Sels et al. (2016) found that the wellbeing of a couple’s
relationship is associated with how emotionally interdependent the
couple is.
Variation in emotion dynamics
Almost all work focused on emotion dynamics in romantic
couples has assumed that such dynamics are constant over time.
In other words, the influence that one partner’s emotions have on
their partner’s emotions remain the same over the course of the
relationship. Yet we might expect the emotion interdependence of
a couple to not remain constant, and instead change over time.
For example, emotion convergence states that a couple’s
emotions should become more similar over time. This increasing
similarity, or increasing covariation, of the couple’s emotions is
thought to be beneficial because it helps the couple better respond
to the demands of the environment and feel close to each other
(Anderson et al., 2003;Vallacher et al., 2005;Butler, 2011;Sels et al.,
2018). This increasing emotion similarity might occur due to the
influence of one person’s emotions on those same emotions of their
partner becoming stronger over time, reflecting a change in the
relationship’s emotion dynamics.
The idea that emotion dynamics should change over time is
further supported by empirical research. Thompson and Bolger
(1999) found that emotion dynamics can change due to the
presence of a stressful event: as one partner approached the date
of a stressful exam, the influence of their negative mood on
their partner’s feelings declined. This reduction in interdependence
was thought to be due to the receiving partner making more
allowances for their partner’s negative mood. But changes in
emotion interactions do not need to be marked by an external
event, and can simply occur over the course of time or due to
internal processes (such as ruminating on an experience or the
experience of particular affective states; De Haan-Rietdijk et al.,
2016;Bringmann et al., 2018).
Although it is realistic to expect changes in emotion dynamics
amongst members of a couple, no research has investigated
whether the presence or magnitude of these changes relate
to relationship outcomes in the same way that the (mean)
level of the emotion dynamics do. Previous research focusing
on the emotions of one partner have shown that greater
variation in one partner’s evaluations of the relationship (e.g.,
their level of satisfaction, how committed they are to the
relationship, how committed they perceive their partner to
be) is related to relationship instability or greater displays of
negative behavior toward their partner (Kelley, 1983;Arriaga,
2001;Arriaga et al., 2006;Campbell et al., 2010). In these
situations, greater variability in relationship evaluations is believed
to reflect problems in the relationship, resulting in negative
outcomes for the couple. Yet this research has only focused on
variability in one partner’s feelings toward the relationship without
considering interdependence in emotions at all. And although
there has been work showing the benefits of increased emotion
similarity on relationships (Anderson et al., 2003;Townsend
et al., 2014), these studies did not explicitly measure variation in
emotion dynamics.
To understand why it is important to think about variation in
emotion dynamics when studying relationship outcomes, consider
the following example. Suppose we had measured the positive
and negative emotions of a husband and wife over time. At
the beginning of the study, the effect of the husband’s negative
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mood is negatively related to his wife’s negative mood, such that
she downplays her own negative mood in an attempt to make
allowances for his. Yet, over time, this dynamic wears on her,
and the husband’s negative mood begins to have a stronger effect
such that there is a contagion in negative mood from him to her.
If the escalation continues, the increasingly negative interactions
between the couple result in the end of their relationship, as
Levenson and Gottman (1985) and Gottman et al. (1998) have
shown that negative affect reciprocity is the strongest indicator
of an unhappy marriage and low marital satisfaction. If we
had not measured the change in emotion dynamics, then we
would have estimated the effect of the husband’s negative mood
on his wife’s negative mood as non-existent or weak (due to
the change from a strong negative effect to a strong positive
effect over time). Thus, in this hypothetical example, measuring
emotion dynamics alone was not enough to help us learn why
the couple ended their relationship. Instead, it was necessary to
measure the change in emotion dynamics, as the presence of that
change contained important information related to the state of
the relationship.
The present study
Although understanding how levels of emotion dynamics
relate to relationship outcomes is important, accounting for and
understanding changes in emotion dynamics over time is equally
important. This helps researchers not only accurately characterize
the process under study, but could also reveal useful information
that is not available from standard, time-invariant parameters. If
the dynamics in a relationship change over time due to factors
indicating relationship stress (e.g., one member of a couple
becoming less emotionally receptive, or external events such as
one partner losing their job), then being able to measure such
changes in the parameters could be useful for predicting the
future of the relationship. The aim of the present study is to
examine whether the change in emotion dynamics over time,
as measured by time-varying vector autoregressive models, is
related to relationship outcomes above and beyond the mean
of those dynamics alone. In the following sections, we describe
the data used to answer our questions, and the time-varying
vector autoregressive model used to measure variation in emotion
dynamics over time. We then investigate whether these changes
are predictive of relationship outcomes—in particular, perceived
relationship quality and relationship dissolution—and conclude
with a discussion of the future directions and limitations of
our approach.
Methods
Participants
Data were collected as part of the Dynamics of Dyadic
Interactions Project (DDIP), a longitudinal study examining the
emotion dynamics of couples over time (Ferrer et al., 2012).
The two members of each couple were asked to complete a
daily diary questionnaire for up to 90 days, with questions
pertaining to their general emotional affect and their affect with
respect to their relationship. In order to have enough data to
estimate the TV-VAR models, and to remain consistent with
previous work (Castro-Schilo and Ferrer, 2013), we limited our
analyses to couples who had completed at least 50 days of the
questionnaire. We further limited our analyses to those couples
who had provided follow-up information on their relationship 1
or 2 years later. This process resulted in a total of 148 couples.
Participants in this subsample ranged in age from 17 to 74
years (M=24.22, SD =9.34), and had been in a relationship
from 1 month to 35.1 years (M=2.93 years, SD =5.40). Of
the 148 couples, 28 reported living together while 120 reported
not living together. Additionally, 2 of the couples reported
their relationship status as “dating around,” 99 reported dating
each other exclusively, 8 reported being engaged, and 26 were
married. The remaining 13 couples reported living together but
did not report their relationship status. On average, participants
provided 65.5 days of data (SD =15.7), although each dyad was
missing, on average, ∼3.96% of their total daily diary data (SD
=9.32%).
Measures
Relationship aect
Relationship-specific affect (RSA; Ferrer and Widaman, 2008;
Ferrer et al., 2012) is a questionnaire designed to measure
affect related to one’s relationship. The questionnaire consists
of 18 items intended to capture both positive (nine items) and
negative affect (nine items). The instructions read, “Indicate
to what extent you have felt this way about your relationship
today.” Participants rated all items using a Likert-type scale
ranging from 1 (very slightly or not at all) to 5 (extremely).
Previous work with this scale has demonstrated good psychometric
properties regarding reliability of change within person (Cranford
et al., 2006), indicating the precision of the measurement
of systematic change of persons across days, with reliability
coefficients for positive and negative affect of 0.85 and 0.87
(for females) and 0.82 and 0.85 (for males; Ferrer et al.,
2012).
Relationship outcomes
One and two years after the initial visit, participants returned
for a set of follow-up interviews. As part of these interviews,
participants were asked about their relationship status, and were
recorded as having broken up if they were no longer with
their initial partner at either of the two follow-up interviews.
Participants were also asked about their relationship quality, which
was assessed using six items from the Perceived Relationship
Quality Component Inventory (Fletcher et al., 2000). These
items included questions such as “How satisfied are you with
your relationship?” and “How committed are you with your
relationship?” and were answered on a 7-point Likert scale (1 =
Not at all and 7 =Extremely). The scores for each member of a
couple were then averaged together to form one overall relationship
quality score.
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Statistical models to examine emotion
interactions
Vector autoregressive (VAR) models
One common statistical approach to model multivariate time
series is the vector autoregressive (VAR) model. In the VAR model,
each variable being studied (in our case, the positive and negative
affect of the female partner and the positive and negative affect of
the male partner toward the relationship) is predicted by itself and
all other variables at previous timepoints up to a certain lag (with
the most common being a lag-1 VAR model, denoted as VAR(1), in
which each variable is predicted by itself and all other variables at
the previous timepoint only). In other words,
yt=c+8yt−1+ǫt
In this model, ccontains the intercepts of the model, ǫtcontains
the model residuals, or whatever part of the observed process was
not explained by the variables at the previous timepoints, and 8is a
matrix that contains the autoregressive parameters on the diagonal
and cross-lagged parameters on the off-diagonal (Shumway and
Stoffer, 2011). The autoregressive parameters represent the effect
of a variable’s own value at the previous timepoint on itself
at the current timepoint, controlling for the effect of all other
variables. In the context of the variables studied here, an example
autoregressive effect would be how the female partner’s positive
affect toward the relationship relates to her positive affect toward
the relationship the next day. The autoregressive effect is commonly
interpreted as the stability of the process or inertia—a higher
positive autoregressive effect indicates that the process takes longer
to revert to equilibrium, or that the individual is more “rigid”
in that state. For example, if the female partner was feeling very
favorable to her relationship at one timepoint (high positive affect),
then a positive autoregressive effect indicates that she would
likely continue to feel favorable toward her relationship (have
high positive affect) at the next timepoint. On the other hand, a
negative autoregressive effect indicates a more rapidly fluctuating
or oscillating process, as a high value at one timepoint would
typically be followed by a lower value at the next timepoint. So,
if the female partner in our example felt very favorable about
her relationship at one timepoint, then she would likely feel less
favorable to her relationship (low positive affect) the next day,
which is then followed by feeling very favorable again the day after.
The cross-lagged parameters represent the effect of one variable
under study (e.g., positive affect of the female partner) at the
previous timepoint on a different variable (e.g., negative affect
of the female partner) at the current timepoint, controlling for
all other autoregressive and cross-lagged effects. A positive cross-
lagged effect indicates that a high value on one process at a
particular timepoint would generally lead to a high value on
the other process at the next timepoint, and in the context
of emotion dynamics, could represent emotion amplification or
emotion escalation (Sbarra and Ferrer, 2006;Sels et al., 2016).
A negative cross-lagged effect indicates that a high value on one
process at a particular timepoint generally leads to a low value
on the other process at the next timepoint, and could represent
emotion reversal or emotion dampening (Sbarra and Ferrer, 2006;
Sels et al., 2016). For example, suppose that our cross-lagged
parameter of interest represented the influence the female partner’s
negative affect toward the relationship yesterday had on her male
partner’s positive affect toward the relationship today. A positive
value of this cross-lagged parameter would mean that if the female
partner felt very negative toward the relationship yesterday, then
the male partner is likely to feel more positive to the relationship
today. A negative value, on the other hand, would mean that if the
female partner felt very negative toward the relationship yesterday,
then the male partner is likely to not feel very positive (low positive
affect) toward the relationship today.
The above example also demonstrates that the cross-lagged
parameters contain the effects between different affect states within
the same partner (e.g., female partner’s positive affect to her
own negative affect), the effects between same affect states across
partners (e.g., female partner’s negative affect to her male partner’s
negative affect), and the effects between different affect states
across partners (e.g., female partner’s negative affect to her male
partner’s positive affect). The former parameters (different affect
states, same partner) can be referred to as intra-partner effects,
while the latter sets of parameters (same affect states across partners
and different affect states across partners) can be referred to as
inter-partner effects.
Since the autoregressive and cross-lagged parameters encode
the associations between the variables at the previous timepoint
and the variables at the current timepoint, we will refer to them
as dynamic parameters in the rest of the paper. This is because
these parameters represent the emotion dynamics of the couple by
quantifying the influence emotion states have on each other within
and across partners.
The VAR model specified above assumes that the processes
under study are all stationary, or that the mean, variance,
and covariances of the processes all remain constant over time
(Shumway and Stoffer, 2011). Stationarity further implies that the
model parameters are constant over time, which, as mentioned
above, is unlikely to hold when studying emotion interactions
over any relatively long period. Therefore, it is preferable to use
a model that either does not assume stationarity or is able to
account for departures from this assumption, in order to more
accurately characterize the process under study and not obtain
biased estimates of the dynamic parameters (Ryan et al., 2023).
Time-varying VAR model
One way to account for non-stationarity in psychological
processes is to allow the VAR model parameters to vary over
time. This model, called the time-varying VAR (TV-VAR) model,
allows any combination of the intercepts, autoregressive effects, and
cross-lagged effects to take on different values at each timepoint
(Bringmann et al., 2017,2018):
yt=ct+8tyt−1+ǫt
Although the TV-VAR model relaxes the stationarity
assumption of the VAR model, it still requires that the process is
stationary at any given timepoint, and that the model parameters
change in a gradual, as opposed to abrupt, fashion.
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Multiple methods, such as kernel-smoothing and regression
splines, are available to estimate these time-varying parameters
(Bringmann et al., 2017,2018;Haslbeck et al., 2021). In this paper,
we use the generalized additive modeling (GAM) framework,
which uses penalized thin-plate regression splines to estimate
how the coefficients change over time. One benefit of this
approach is that how the time-varying parameter changes over
time does not need to be specified in advance but is instead
estimated from the data. More specifically, each time-varying
coefficient (here, the intercept terms, autoregressive effects, and
cross-lagged effects) is written as a function of kknown basis
functions R(t):
φijt = ˆα1R1(t)+ ˆα2R2(t)+...+ ˆαkRk(t)
Each added basis function determines how flexible or “wiggly”
the final functional form of the time-varying coefficient is, with
each basis function being “wigglier” than the previous. At the
same time, the estimated regression coefficients ( ˆα1,ˆα2,...,ˆαk)
control how much weight that basis function is given at each
timepoint. To prevent the functional form from being too
flexible, the regression coefficients are estimated using a penalized
likelihood approach, so that the effect of the more flexible
basis functions are downplayed due to a “wiggliness penalty.”
The optimal penalty is estimated using a generalized cross-
validation technique, so that the final form is neither too wiggly
(the penalty is too low) or too smooth (the penalty is too
high). Readers interested in more details about this method
are referred to Bringmann et al. (2018) and Haslbeck et al.
(2021).
Data analysis
We were interested in examining whether incorporating
information on the variation of emotion dynamics over time was
associated with future relationship outcomes, even after controlling
for the mean value of those parameters. The data analysis procedure
to answer this question is displayed in Figure 1 and described in
more detail below.
As mentioned above, each dyad rated to what extent they felt
certain positive or negative emotions toward their relationship
that day, and the ratings were averaged into a positive affect (PA)
and negative affect (NA) score for each member of the couple
(Figure 1A). This resulted in four time series (PA of the male
partner, NA of the male partner, PA of the female partner, and
NA of the female partner), which were then used to estimate a
separate TV-VAR model for each dyad (Figure 1B). These analyses
were conducted in R (R Core Team, 2022, version 4.2.1), using
the package mgcv (Wood, 2017). The number of basis functions
was kept at 10 (the default in mgcv), and inspection of the results
showed that this was sufficient. In other words, the effective degrees
of freedom for all smooth functions were not close to 10, indicating
no more basis functions needed to be added. Finally, mgcv handled
missing data by using listwise deletion, such that days where
at least one variable was missing information was not used in
model estimation.
The output of interest from the TV-VAR model was the
estimate of 8tat each timepoint for each dyad that, as displayed
in Figure 1C, could potentially show substantial variation over
time. To summarize each dyad’s estimated 8tmatrices in a way
that could then be used as predictors of the external outcomes,
we calculated the mean and standard deviation for each dynamic
parameter over time. Thus, the final estimates obtained from fitting
the TV-VAR model to each dyad were 16 means and 16 standard
deviations of the corresponding dynamic parameters.
In the final step, the means and standard deviations of
the dynamic parameters were used as predictors of relationship
quality and relationship dissolution in a linear and a logistic
regression model, respectively. Significant associations were chosen
based on a stepwise regression approach, with both forwards
and backwards selection. Although there are noted drawbacks to
stepwise regression (Steyerberg et al., 1999;Austin and Tu, 2004),
we chose to proceed with this approach due to the relatively high
number of predictors (16 means and 16 standard deviations of
dynamic parameters, 32 total), compared to the number of dyads
(127 dyads).
Results
Descriptive statistics
Of the 148 couples (and thus, the 148 TV-VAR models), 127
successfully converged, with the remaining 21 failing to converge
due to an insufficient number of timepoints with complete data.
Descriptive statistics across these couples for the positive and
negative affect variables (averaged across time), the outcome
variables, and covariates are presented in Table 1, while descriptive
statistics for the means and standard deviations of the dynamic
parameters over time are presented in Table 2. A visualization of the
change in dynamic parameters for each dyad is available at: https://
github.com/skjohal/Dyadic-Affect-Networks. By the 2-year follow-
up, 29 of the 127 couples (or 22.83%) had ended their relationship,
and most couples reported high levels of relationship quality (M=
5.96, SD =0.77).
To determine whether a parameter was time-varying or
time-invariant, we evaluated whether the smooth function was
statistically significant and the effective degrees of freedom were
>2, as these are indications that the smooth function is: (1)
important to the model and (2) non-linear (Bringmann et al.,
2017,2018). Based on these criteria, few dynamic parameters
showed variation over time. On average, two out of the 16
dynamic parameters were significantly time-varying. However, this
varied widely across dyads: the number of dynamic parameters
showing variation ranged from 0 to 10 for any given dyad. The
autoregressive parameters showed more variation over time than
the cross-lagged parameters, although cross-lagged parameters did
occasionally vary over time. In particular, cross-lagged parameters
involving the partner’s negative affect at the previous timepoint,
such as the relation from both partners’ negative affect at the
previous timepoint to the female partner’s positive and negative
affect, and the male partner’s positive and negative affect, showed
significant variation over time.
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FIGURE 1
Description of data analysis procedure. (A) Displays an example time series of positive and negative aect for each member of a couple. (B) Shows
the vector autoregressive model used to analyze this data, such that the autoregressive and cross-lagged eects are allowed to change over time.
(C) Shows how the autoregressive and cross-lagged eects change over time, and these eects are each summarized into a mean and standard
deviation. Finally, in (D), the means and standard deviations of the dynamic parameters are used to predict relationship dissolution and relationship
quality.
Relation to relationship quality
To examine the association between the mean and variation of
the dynamic parameters over time on the relationship quality, we
used a stepwise linear regression. The results of the final model
are shown in Table 3. With regards to the average of the dynamic
parameters over time, only the cross-lagged effect from the female
partner’s positive affect to her own negative affect was a significant
predictor of relationship quality. The greater this cross-lagged
effect, the higher the reported relationship quality (b=1.00, p
=0.01).
Then, looking at the variation of the dynamic parameters over
time, change in the cross-lagged effects from the female partner’s
positive affect to the male partner’s positive affect, and from the
male partner’s negative affect to the female partner’s negative affect,
were both significant predictors. The greater the variation in the
cross-lagged effect from the female partner’s positive affect to her
male partner’s positive affect, the lower the relationship quality (b=
−1.33, p=0.02). However, greater variation in the effect from the
male partner’s negative affect to the female partner’s negative affect
was related to higher relationship quality (b=0.43, p=0.01).
Although the means and standard deviations of some dynamic
parameters predicted relationship quality, the model only explained
9.12% of the total variation. However, the regression model
performed better than an intercept-only model, 1χ2(7)=
2.81, p=0.01, indicating the relative contribution of the predictors
included in the model.
Relation to relationship dissolution
To examine the relation between the dynamic parameters
from the time-series model and relationship dissolution we used
a stepwise logistic regression with both forward and backward
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TABLE 1 Descriptive statistics for raw positive and negative aect, outcome variables, and covariates of interest.
Male
PA
Male
NA
Female
PA
Female
NA
Total
involvement
Initial rel.
quality
Final rel.
quality
Rel.
Dissolution
Mean 3.54 1.35 3.50 1.34 3.09 6.26 5.96 0.23
SD 0.66 0.31 0.68 0.28 5.74 0.61 0.77 -
Maximum 1.53 1.01 1.65 1.00 0.04 3.09 3.58 -
Minimum 4.93 2.70 4.83 2.37 35.08 7.00 7.00 -
Male PAa1
Male NA −0.31 1
Female PA 0.76 −0.31 1
Female NA −0.31 0.61 −0.42 1
Total involvement 0.01 −0.18 0.00 −0.16 1
Initial rel. quality 0.42 −0.24 0.50 −0.36 0.08 1
Final rel. quality 0.43 −0.20 0.46 −0.21 0.02 0.46 1
Rel. dissolutionb−0.29 0.19 −0.29 0.33 −0.63 −0.32 −0.42 1
PA, Positive Affect; NA, Negative Affect; Rel., Relationship.
aPA and NA for each member of a couple were first averaged over time. These descriptive statistics are then across dyads, not across time.
bSince relationship dissolution is a binary variable (1 =the couple has ended their relationship), the mean reflects the percentage of couples who have ended their relationship in our sample,
and there is no SD, minimum, or maximum value. Furthermore, correlations with this variable are point-biserial correlations.
entry, using the means and standard deviations of the dynamic
parameters across time as predictors. Table 4 includes the results
from the final model, which included a total of eight predictors,
with six of them significant.
Looking first at the means of the dynamic parameters over time,
three were significant: the average autoregressive effect of the male
partner’s negative affect, the average cross-lagged effect from the
female partner’s positive affect to her own negative affect, and the
average cross-lagged effect from the male partner’s negative affect
to the female partner’s negative affect. The effects of the stability
of the male partner’s negative affect, and the cross-lagged effect
from the female partner’s positive affect to her negative affect, on
relationship dissolution were both negative (Male NA →Male
NA: b= −2.98, p=0.02, odds =0.05; Female PA →Female
NA: b= −3.97, p=0.01, odds =0.02), indicating that stronger
influence between these affect states decreased the likelihood of
breaking up. On the other hand, the cross-lagged effect from the
male partner’s negative affect to the female partner’s negative affect
was positive (b=3.66, p=0.001, odds =38.86), such that stronger
cross-lagged coefficients greatly increased the chances of the couple
breaking up.
Furthermore, variation over time in the dynamic parameters
involving the male partner’s positive affect were also predictive of
relationship dissolution. Variation in the cross-lagged effect from
the female partner’s negative affect to the male partner’s positive
affect, the cross-lagged effect from the male partner’s negative affect
to his own positive affect, and the cross-lagged effect from the male
partner’s positive affect to the female partner’s negative affect were
significant predictors. The cross-lagged effects between the male
partner’s positive affect and the female partner’s negative affect were
both positive (Female NA →Male PA: b=2.01, p=0.02, odds =
7.43; Male PA →Female NA: b=3.19, p=0.008, odds =24.28),
indicating that greater variation in these effects over time increased
the chances of the couple ending their relationship. However, the
effect from the male partner’s negative affect to his own positive
affect was negative (b= −2.09, p=0.03, odds =0.12), such that
greater variation in this cross-lagged effect over time decreased the
chances of relationship dissolution.
The classification accuracy of the final logistic regression model
was 81.1%, which was an improvement from the 77.2% accuracy
of the intercept-only model. Furthermore, the logistic regression
model showed improved fit relative to the intercept-only model,
with Cox and Snell R2=0.24 and Nagelkerke R2=0.36.
Controlling for time in relationship and
initial relationship quality
Due to the likelihood that the probability of a couple ending
their relationship and their perceived relationship quality are
associated with the length of time the couple has been together and
their initial relationship quality, we conducted separate analyses
controlling for these two variables. In these models, we kept
the same predictors that had been identified as important in
the initial analysis, and additionally controlled for the time the
couple had been together as well as the perceived relationship
quality at their initial visit. The results of these models predicting
relationship quality and relationship dissolution are shown in
Tables 5,6, respectively.
When predicting relationship quality, accounting for initial
relationship quality and length of time in the relationship removed
many of the effects of emotion dynamics. The only predictors
related to emotion dynamics that remained significant were the
average cross-lagged effect from the female partner’s positive affect
to her own negative affect (b=0.83, p=0.01) and the amount
of variation in the cross-lagged effect from the female partner’s
positive affect to the male partner’s positive affect (b= −1.24, p
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TABLE 2 Descriptive statistics for mean and variability of dynamic
parameters over time.
Variables Mean SD Min Max
Mean of dynamic parameters
Female PA →Male PA 0.06 0.22 −0.76 0.73
Female NA →Male PA 0.01 0.46 −3.17 1.24
Male PA →Male PA 0.05 0.25 −0.60 0.75
Male NA →Male PA 0.08 0.50 −1.31 2.61
Female PA →Male NA −0.01 0.24 −0.64 1.09
Female NA →Male NA −0.13 2.05 −22.7 2.20
Male PA →Male NA −0.02 0.20 −1.01 0.58
Male NA →Male NA 0.001 0.27 −0.76 0.65
Female PA →Female PA 0.06 0.30 −0.81 1.53
Female NA →Female PA −0.07 0.51 −3.29 1.72
Male PA →Female PA 0.06 0.28 −0.61 1.48
Male NA →Female PA 0.07 0.54 −1.21 2.81
Female PA →Female NA −0.02 0.21 −0.69 0.92
Female NA →Female
NA
−0.06 0.95 −10.27 0.89
Male PA →Female NA −0.03 0.24 −1.18 0.89
Male NA →Female NA 0.03 0.31 −0.96 1.01
Variability of dynamic parameters
Female PA →Male PA 0.17 0.13 <0.01 0.53
Female NA →Male PA 0.30 0.39 <0.01 3.66
Male PA →Male PA 0.19 0.18 <0.01 1.03
Male NA →Male PA 0.39 0.44 <0.01 2.07
Female PA →Male NA 0.14 0.16 <0.01 0.82
Female NA →Male NA 0.36 1.27 <0.01 13.9
Male PA →Male NA 0.15 0.17 <0.01 1.1
Male NA →Male NA 0.23 0.18 0.02 0.82
Female PA →Female PA 0.18 0.20 0.01 1.70
Female NA →Female PA 0.38 0.43 <0.01 2.91
Male PA →Female PA 0.24 0.23 <0.01 1.53
Male NA →Female PA 0.47 0.78 <0.01 7.93
Female PA →Female NA 0.15 0.13 <0.01 0.56
Female NA →Female
NA
0.25 0.54 <0.01 5.99
Male PA →Female NA 0.19 0.21 <0.01 1.21
Male NA →Female NA 0.34 0.42 <0.01 2.63
PA, Positive Affect; NA, Negative Affect.
Mean of dynamic parameters refers to the mean of each autoregressive and cross–lagged effect
for each dyad over time, whereas variability of dynamic parameters is the standard deviation
of each effect for each dyad over time. The descriptive statistics (mean, SD, min, and max) are
then calculated on these means and SDs across dyads.
TABLE 3 Summary of model predicting relationship quality.
b SE t p
Intercept 5.97 0.13 46.09 <0.001
Means
Male PA →Male PA −0.49 0.30 −1.62 0.11
Female PA →Female NA 1.00 0.35 2.86 0.01
Male NA →Female NA −0.34 0.22 −1.56 0.12
Female PA →Male PA 0.49 0.33 1.51 0.13
Variation
Female PA →Male PA −1.33 0.57 −2.34 0.02
Female PA →Male NA 0.64 0.47 1.38 0.17
Male NA →Female NA 0.43 0.16 2.62 0.01
PA, Positive Affect; NA, Negative Affect.
TABLE 4 Summary of model predicting relationship dissolution.
b SE t p Odds
ratio
Intercept −2.16 0.47 −4.59 <0.001 0.22
Means
Male NA →Male NA −2.98 1.23 −2.42 0.02 0.05
Female PA →Female PA −2.36 1.23 −1.92 0.06 0.09
Female PA →Female NA −3.97 1.56 −2.55 0.01 0.02
Male PA →Female NA −1.77 1.26 −1.40 0.16 0.17
Male NA →Female NA 3.66 1.15 3.18 0.00 39.0
Variation
Female NA →Male PA 2.01 0.87 2.30 0.02 7.43
Male NA →Male PA −2.09 0.96 −2.18 0.03 0.12
Male PA →Female NA 3.19 1.22 2.62 0.01 24.3
PA, Positive Affect; NA, Negative Affect.
=0.03). The effects were in the same direction as before: A greater
average value of the effect from the female partner’s positive affect to
her own negative affect was related to greater relationship quality,
while greater variation in the effect from the female partner’s
positive affect to the male partner’s positive affect was related to
decreased relationship quality, even after controlling for length of
time in the relationship and initial relationship quality. As expected,
initial relationship quality was also a significant predictor of future
relationship quality, such that those with higher initial relationship
quality tended to have higher relationship quality at the follow-up
interviews (b=0.51, p<0.001).
Initial relationship quality was also related to the probability
of ending the relationship, such that couples with higher initial
relationship quality had a lower chance of ending their relationship
(b= −1.31. p=0.01, odds =0.27). Emotion dynamics continued
to be related to relationship dissolution: all emotion dynamics
averaged over time that were significant in the initial model
remained significant, and one parameter related to variation in
emotion dynamics remained significant. Greater average values
of the male partner’s autoregressive effect for negative affect and
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TABLE 5 Summary of model predicting relationship quality, controlling
for time in relationship and initial quality.
b SE t p
Intercept 2.81 0.69 4.07 <0.001
Means
Male PA →Male PA −0.39 0.30 −1.32 0.19
Female PA →Female NA 0.83 0.33 2.51 0.01
Male NA →Female NA −0.20 0.21 −0.97 0.33
Female PA →Male PA 0.45 0.30 1.49 0.14
Variation
Female PA →Male PA −1.24 0.56 −2.21 0.03
Female PA →Male NA 0.72 0.44 1.61 0.11
Male NA →Female NA 0.30 0.15 1.91 0.06
Control variables
Time in relationship −0.00 0.01 −0.03 0.97
Initial relationship quality 0.51 0.11 4.69 <0.001
PA, Positive Affect; NA, Negative Affect.
TABLE 6 Summary of model predicting relationship dissolution,
controlling for time in relationship and initial quality.
b SE t p Odds
ratio
Intercept 6.22 3.07 2.03 0.04 502.59
Means
Male NA →Male NA −2.76 1.33 −2.08 0.04 0.06
Female PA →Female PA −1.63 1.34 −1.22 0.22 0.20
Female PA →Female NA −3.66 1.73 −2.12 0.03 0.03
Male PA →Female NA −2.38 1.41 −1.68 0.09 0.09
Male NA →Female NA 3.44 1.25 2.76 0.01 31.22
Variation
Female NA →Male PA 1.55 0.89 1.74 0.08 4.69
Male NA →Male PA −1.35 0.96 −1.42 0.16 0.26
Male PA →Female NA 3.60 1.42 2.54 0.01 36.51
Control variables
Time in relationship −0.22 0.16 −1.37 0.17 0.81
Initial relationship quality 1.31 0.51 −2.57 0.01 0.27
PA, Positive Affect; NA, Negative Affect.
the cross-lagged effect from female partner’s positive affect to her
own negative affect were related to decreased risk of relationship
dissolution (Male NA →Male NA: b= −2.76, p=0.04, odds
=0.06; Female PA →Female NA: b= −3.66, p=0.03, odds
=0.03). Furthermore, a greater average value in the cross-lagged
effect from the male partner’s negative affect to the female partner’s
negative affect, and greater variation in the cross-lagged effect from
male partner’s positive affect to female partner’s negative affect, was
related to increased risk of ending the relationship (Male NA →
Female NA: b=3.44, p=0.01, odds =31.22; Male PA →Female
NA: b=3.60, p=0.01, odds =36.51).
Overall, initial relationship quality was a significant predictor
of both future relationship quality and relationship dissolution.
However, even after controlling for this and length of time in
the relationship, variation in emotion dynamics continued to play
a role.
Discussion
The aim of our present work was to examine whether changes
in emotion dynamics of a couple over time was associated with
future relationship outcomes, to help us better understand the
role that emotion dynamics can play within a relationship. To
answer this question, we estimated TV-VAR models and calculated
means and standard deviations of the autoregressive and cross-
lagged parameters across time. Our TV-VAR models indicated that
there was variation over time in these dynamic parameters for
each dyad, although this variation tended to be small. Typically,
only two out of the 16 dynamic parameters showed significant
variation over time for each dyad. The parameters that tended to
vary the most were the autoregressive effects, although this may
be due to the general tendency for autoregressive effects to be
stronger and, thus, more likely to be significant than cross-lagged
effects. Since one of our criteria for evaluating whether a parameter
was significantly time-varying was whether its smooth function
was statistically significant (i.e., the parameter plays an important
role in the model), this may have resulted in more autoregressive
parameters being detected as significantly time-varying.
Some findings about which parameters tend to be significantly
time-varying also align with previous research. For example,
negative emotions tend to be more contagious (have more
significant influence) than positive emotions (Larson and Almeida,
1999). We extended this finding by showing that parameters
representing the influence of negative affect on other affect states
are not only more contagious, but they also vary over time
more frequently than parameters representing the influence of
positive affect.
In terms of relations with future outcomes, the results of
our stepwise regressions showed that the average of particular
autoregressive and cross-lagged effects, as well as variation in other
dynamic parameters, was related to both relationship quality and
relationship dissolution. Even after controlling for time spent in
the relationship and initial relationship quality, variation in the
parameters representing emotion dynamics was related to both
these outcomes.
Putting these results in the context of other research on the
same dataset, we find some similarities in our results pertaining to
the average value of the dynamic parameters and those of Castro-
Schilo and Ferrer (2013), despite the differences in modeling
strategies. Both found that the mean cross-lagged effect from
the female partner’s positive affect to her own negative affect
was a significant, positive predictor of relationship quality. That
is, the stronger the influence of the female partner’s positive
affect on her negative affect the next day, the higher the couple
rated the quality of their relationship. However, unlike Castro-
Schilo and Ferrer (2013), who found that dynamic parameters
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did not significantly contribute to the prediction of relationship
dissolution, we found that a handful of autoregressive and cross-
lagged effects were significantly—and typically negatively—related
to relationship dissolution.
It is a little harder to directly compare our results relating
relationship dissolution to the average value of the dynamic
parameters to the results of Gonzales et al. (2018), due to the use of
composites in our analysis and individual items in theirs. However,
there was general agreement in the type of dynamic parameters
that were significantly related to relationship dissolution. In both
our analyses, the female partner’s influence on her male partner’s
affect was a better predictor than the male partner’s influence on his
female partner’s affect, which contrasts with some previous findings
showing that husbands’ affect has a stronger effect on marriage
quality than wives’ affect (Cowan and Cowan, 2000). Furthermore,
Gonzales et al. (2018) restricted their analysis to only inter-partner
effects (from one partner to another), whereas our analysis included
intra-partner effects (from one partner’s affect to their own affect)
and showed the relevance of autoregressive effects to relationship
dissolution. However, whereas the results of Gonzales et al. (2018)
highlighted the potentially protective nature of the female partner’s
positive affect—e.g., cross-lagged effects involving indicators of the
female partner’s positive affect was related to decreased risk of
relationship dissolution—our results showed differently. In other
words, most of the significant cross-lagged effects involved the
female partner’s negative affect, and these cross-lagged effects were
related to a decreased risk of relationship dissolution.
Our findings help us better understand emotion dynamics
between members of a romantic couple, and the role they play
in determining relationship outcomes, by explicitly studying how
changes in emotion dynamics over time can impact relationship
outcomes. Changes in the dynamic parameters over time can be
potentially indicative of important changes in the relationship, such
as increased relationship stress, which then relate (negatively) to
future relationship outcomes. Some of our findings align with this
interpretation, as well as the general findings of Arriaga (2001)
and Arriaga et al. (2006), such that greater variation in the cross-
lagged effect from the female partner’s positive affect to the male
partner’s positive affect and in the bidirectional effect between
the male partner’s positive affect and the female partner’s negative
affect predicted decreased relationship quality and increased risk of
relationship dissolution, respectively.
However, it is important to note that variation in dynamic
parameters was not always related to negative relationship
outcomes. For example, variation over time in the cross-lagged
effect from the male partner’s negative affect to his partner’s
negative affect was linked to increased relationship quality.
Similarly, greater variation in the cross-lagged effect from the male
partner’s negative affect to his own positive affect was linked to
decreased risk of relationship dissolution. Although these effects
are counter-intuitive to an interpretation where greater variation
is linked to greater stress in the relationship, we believe that they
can align with an interpretation where greater variation is caused
by a change in the dynamic parameter from a value that represents
maladjustment in the relationship to a value that represents a
healthier relationship state. This would align, for example, with
research relating emotion convergence and emotion similarity to
positive relationship outcomes (Anderson et al., 2003;Townsend
et al., 2014).
For example, we mentioned in the Introduction work
demonstrating that couples whose negative emotions are coupled
together tend to be less satisfied and more likely to end their
marriage (Gottman and Levenson, 1992;Gottman, 1994;Gottman
et al., 1998). Although greater variation in the influence that,
for example, a female partner’s negative affect has on her male
partner’s affect could mean that this relation goes from being
strongly negative to strongly positive, it could also go from being
strongly positive to being weak or negative. A strongly positive
effect would mean that high values of the female partner’s negative
affect toward the relationship lead to higher values of the male
partner’s negative affect, resulting in reciprocation of negative
emotions. A weak or negative effect, on the other hand, would mean
the female partner having negative feelings toward the relationship
does not affect her partner’s negative feelings, or leads him to have a
decrease in negative feelings to compensate. Therefore, a change
from a positive influence to weak or negative influence would
represent a healthier state of the relationship. Greater variation
in this particular dynamic could be linked to higher relationship
quality, as it moves the couple away from a dynamic that tends to
reflect maladjustment.
Limitations and future directions
Despite our results showing the importance of variation in
emotional interactions for predicting relationship outcomes, it is
important to note that the improvement in model fit compared
to an intercept-only model was relatively low for both models.
For example, the R2value for both models was below 0.40, and
the change in classification accuracy for the logistic regression
model was only 3.9%. However, this could be explained by several
reasons. First, for the logistic regression model, only around 23%
of the couples had ended their relationship by the time of the
follow-up, and this low base rate could hinder the predictive ability
of the model. Secondly, our model treated the dyad as a closed
system, such that there were no external inputs or variables other
than the mean and variation of the dynamic parameters that
related to the outcomes of interest. However, research has shown
that there are a variety of socio-demographic factors that could
contribute to relationship quality and relationship dissolution,
which may not affect the average value or variation of the dynamic
parameters (Conger et al., 1990;Gottman and Levenson, 1992;
Lewin, 2005;Poortman, 2005;Røsand et al., 2014;Hensel and
O’Sulliban, 2022). Finally, and more generally, there was a 1-to-
2-year gap between the assessment of daily affect, and the follow-
up with assessment of relationship outcomes. Therefore, although
change in emotion dynamics could be a potential indicator for
important changes in a relationship, it is possible that the effect
of variation in emotional dynamics (or the underlying causes
of that change) is not as important 1–2 years later as it would
have been if the follow-up assessment had occurred, say, 3
months after the daily diary portion of the study. Despite these
limitations, there was still enough of an association between
the mean dynamic parameters and their variation over time to
be picked up in our analysis, and even after controlling for
relevant covariates such as length of the relationship and initial
perceived quality.
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Furthermore, the relation between variation in dynamic
parameters and any outcome is reliant on there being variation in
the dynamic parameters. Although our results did indicate some
variation in the dynamic parameters, and that this variation was
related to relationship outcomes, very few dynamic parameters
showed significant amounts of variation over time. Although this
could indicate that emotional dynamics in our sample showed
little change over time, it is also possible that we simply did not
have enough data to observe any variation that was present. Dyads
in our sample mostly provided between 50 and 60 days, and
previous work has shown that this number of timepoints might
not always be sufficient for the TV-VAR model to detect changes
in dynamics (Bringmann et al., 2018). Another possibility is that
changes in emotion dynamics are more likely to occur for certain
types of couples than others (e.g., those who have just begun their
relationship, or those who are close to significant events such as
engagement or marriage). Therefore, a future direction of this work
could be to repeat this analysis with a larger dataset that includes
more timepoints or with a group of couples at similar stages in
their relationship, to determine the generalizability of these results
beyond this sample.
Future research could also extend the work presented here to
gain an even deeper understanding of changes in emotion dynamics
and their relation to future outcomes. For example, we mentioned
that greater variation in emotion dynamics could reflect a change
toward a healthier dynamic, or a change toward a worse dynamic.
Yet since we quantified variation using the standard deviation,
our measure—although simple—is not able to characterize how
emotion dynamics change over time, and how the direction of this
change is related to relationship outcomes. Therefore, one potential
future direction is to replicate these analyses with more fine-
grained measures of variation that could give us this information.
Furthermore, we mentioned that external events could spark
changes in emotion dynamics as well as potentially impacting
the relationship directly. Therefore, an interesting future direction
would be to examine whether incorporating information about
external events helps us understand changes in emotion dynamics
[although this might require a model that allows for explicit change
points in the dynamic parameters, e.g., Albers and Bringmann
(2020)], as well as aids our prediction of relationship outcomes.
Conclusion
In conclusion, our paper underscores the importance of
identifying whether and how couples’ exchange of affect varies over
time. Given that emotion dynamics unfold over time, capturing
such dynamics properly requires using models that can estimate
not only the dynamics themselves, but also their variation over
time. By using such a model, we revealed variation in dynamic
parameters that otherwise would have been ignored. In addition,
we showed how such variation was related to future outcomes, such
as relationship status and quality, above and beyond the means of
such parameters.
Data availability statement
The data analyzed in this study is subject to the following
licenses/restrictions: the use of the dataset can be discussed
with EF. Requests to access these datasets should be directed to
EF, eferrer@ucdavis.edu.
Ethics statement
The studies involving humans were approved by University of
California, Davis IRB. The studies were conducted in accordance
with the local legislation and institutional requirements. The
participants provided their written informed consent to participate
in this study.
Author contributions
SJ: Conceptualization, Formal analysis, Writing—original draft.
EF: Data curation, Writing—review & editing.
Funding
The author(s) declare that no financial support was received for
the research, authorship, and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those
of the authors and do not necessarily represent those of
their affiliated organizations, or those of the publisher,
the editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by
its manufacturer, is not guaranteed or endorsed by the
publisher.
References
Albers, C. J., and Bringmann, L. F. (2020). Inspecting gradual and abrupt changes
in emotion dynamics with the time-varying change point autoregressive model. Eur. J.
Psychol. Assess. 36, 492–499. doi: 10.1027/1015-5759/a000589
Anderson, C., Keltner, D., and John, O. P. (2003). Emotional convergence
between people over time. J. Personal. Soc. Psychol. 84, 1054–1068.
doi: 10.1037/0022-3514.84.5.1054
Frontiers in Human Neuroscience 11 frontiersin.org
Johal and Ferrer 10.3389/fnhum.2024.1331859
Arriaga, X. B. (2001). The ups and downs of dating: fluctuations in satisfaction
in newly formed romantic relationships. J. Personal. Soc. Psychol. 80, 754–765.
doi: 10.1037/0022-3514.80.5.754
Arriaga, X. B., Reed, J. T., Goodfriend, W., and Agnew, C. R. (2006).
Relationship perceptions and persistence: do fluctuations in perceived partner
commitment undermine dating relationships? J. Personal. Soc. Psychol. 91, 1045–1065.
doi: 10.1037/0022-3514.91.6.1045
Austin, P. C., and Tu, J. V. (2004). Automated variable selection methods
for logistic regression produced unstable models for predicting acute myocardial
infarction mortality. J. Clin. Epidemiol. 57, 1138–1146. doi: 10.1016/j.jclinepi.2004.
04.003
Bolger, N., DeLongis, A., Kessler, R. C., and Wethington, E. (1989). The contagion
of stress across multiple roles. J. Mar. Fam. 51:175. doi: 10.2307/352378
Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., and Tuerlinckx,
F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-
varying vector-autoregressive model. Multivar. Behav. Res. 53, 293–314.
doi: 10.1080/00273171.2018.1439722
Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., and
Tuerlinckx, F. (2017). Changing dynamics: time-varying autoregressive models using
generalized additive modeling. Psychol. Methods 22, 409–425. doi: 10.1037/met0
000085
Butler, E. A. (2011). Temporal interpersonal emotion systems: the “TIES” that
form relationships. Personal. Soc. Psychol. Rev. 15, 367–393. doi: 10.1177/1088868311
411164
Butner, J., Diamond, L. M., and Hicks, A. M. (2007). Attachment style and two
forms of affect coregulation between romantic partners. Person. Relat. 14, 431–455.
doi: 10.1111/j.1475-6811.2007.00164.x
Campbell, L., Simpson, J. A., Boldry, J. G., and Rubin, H. (2010). Trust, variability in
relationship evaluations, and relationship processes. J. Personal. Soc. Psychol. 99, 14–31.
doi: 10.1037/a0019714
Castro-Schilo, L., and Ferrer, E. (2013). Comparison of nomothetic vs. idiographic-
oriented methods for making predictions about distal outcomes from time series data.
Multivar. Behav. Res. 48, 175–207. doi: 10.1080/00273171.2012.736042
Conger, R. D., Elder Jr, G. H., Lorenz, F. O., Conger, K. J., Simons, R. L., Whitbeck,
L. B., et al. (1990). Linking economic hardship to marital quality and instability. J. Mar.
Fam. 1990, 643–656. doi: 10.2307/352931
Cowan, C. P., and Cowan, P. A. (2000). When Partners Become Parents: The Big Life
Change for Couples. Mahwah, NJ: Erlbaum.
Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., and Bolger, N. (2006).
A procedure for evaluating sensitivity to within-person change: can mood measures
in diary studies detect change reliably? Personal. Soci. Psychol. Bullet. 32, 917–929.
doi: 10.1177/0146167206287721
De Haan-Rietdijk, S., Gottman, J. M., Bergeman, C. S., and Hamaker, E.
L. (2016). Get over it! A multilevel threshold autoregressive model for state-
dependent affect regulation. Psychometrika 81, 217–241. doi: 10.1007/s11336-014-
9417-x
Ferrer, E., and Nesselroade, J. R. (2003). Modeling affective processes
in dyadic relations via dynamic factor analysis. Emotion 3, 344–360.
doi: 10.1037/1528-3542.3.4.344
Ferrer, E., Steele, J. S., and Hsieh, F. (2012). Analyzing the dynamics of affective
dyadic interactions using patterns of intra- and interindividual variability. Multivar.
Behav. Res. 47, 136–171. doi: 10.1080/00273171.2012.640605
Ferrer, E., and Widaman, K. F. (2008). “Dynamic factor analysis of dyadic affective
processes with intergroup differences,” in Modeling Dyadic and Interdependent Data in
the Developmental and Behavioral Sciences, eds. N. A. Card, J. P. Selig, and T. D. Little
(London: Routledge/Taylor & Francis Group), 107–137.
Fletcher, G. J., Simpson, J. A., and Thomas, G. (2000). Ideals, perceptions, and
evaluations in early relationship development. J. Personal. Soc. Psychol. 79, 933–940.
doi: 10.1037//0022-3514.79.6.933
Gonzales, J. E., Shestak, A., and Ferrer, E. (2018). Using model
parameters describing affective dynamics to predict romantic relationship
dissolution. Transl. Iss. Psychol. Sci. 4, 362–379. doi: 10.1037/tps00
00179
Gottman, J. M. (1994). What Predicts Divorce? The Relationship Between Marital
Processes and Marital Outcomes. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Gottman, J. M., Coan, J., Carrere, S., and Swanson, C. (1998). Predicting
marital happiness and stability from newlywed interactions. J. Mar. Fam. 60:5.
doi: 10.2307/353438
Gottman, J. M., and Levenson, R. W. (1992). Marital processes predictive of later
dissolution: behavior, physiology, and health. J. Personal. Soc. Psychol. 63, 221–233.
doi: 10.1037/0022-3514.63.2.221
Granic, I. (2005). Timing is everything: developmental psychopathology from
a dynamic systems perspective. Dev. Rev. 25, 386–407. doi: 10.1016/j.dr.2005.
10.005
Haslbeck, J., Bringmann, L., and Waldorp, L. (2021). A tutorial on estimating
time-varying vector autoregressive models. Multivar. Behav. Res. 56:1743630.
doi: 10.1080/00273171.2020.1743630
Hensel, D. J., and O’Sulliban, L. F. (2022). What makes them last? Predicting time
to relationship dissolution in adolescent women’s intimate relationships with male
partners. J. Soc. Personal Relat. 39, 393–412. doi: 10.1177/02654075211036516
Kelley, H. H. (1983). “Love and commitment,” in Close Relationships, eds. H. H.
Kelley, E. Berscheid, A. Christensen, J. H. Harvey, T. L. Huston, G. Levinger, et al. (New
York, NY: W. H. Freeman and Company), 20–67.
Kelley, H. H., Berscheid, E., Christensen, A., Harvey, J. H., Huston, T. L., Levinger,
G., et al. (2002). “Analyzing close relationships,” in Close Relationships, eds. H. H.
Kelley, E. Berscheid, A. Christensen, J. H. Harvey, T. L. Huston, G. Levinger, et al. (New
York, NY: W. H. Freeman and Company), 20–67.
Larson, R. W., and Almeida, D. M. (1999). Emotional transmission in the daily
lives of families: a new paradigm for studying family process. J. Mar. Fam. 61, 5–20.
doi: 10.2307/353879
Levenson, R. W., and Gottman, J. M. (1985). Physiological and affective
predictors of change in relationship satisfaction. J. Pers. Soc. Psychol. 49, 85–94.
doi: 10.1037//0022-3514.49.1.85
Lewin, A. C. (2005). The effect of economic stability on family stability among
welfare recipients. Eval. Rev. 29, 223–240. doi: 10.1177/0193841X04272558
Lougheed, J. P., and Hollenstein, T. (2018). “Methodological approaches to
studying interpersonal emotion dynamics,” in Inter personal Emotion Dynamics in Close
Relationships, eds. A. K. Randall and D. Schoebi (Cambridge: Cambridge University
Press), 27–46.
Poortman, A.-R. (2005). How work affects divorce: the mediating role of financial
and time pressures. J. Fam. Iss. 26, 168–195. doi: 10.1177/0192513X.04270228
R Core Team (2022). R: A Language and Environment for Statistical Computing.
Vienna: R Foundation for Statistical Computing. Available online at: https://www.R-
project.org/ (accessed October 1, 2023).
Randall, A. K., and Butler, E. A. (2013). Attachment and emotion transmission
within romantic relationships: merging intrapersonal and interpersonal perspectives.
J. Relat. Res. 4:10. doi: 10.1017/jrr.2013.10
Randall, A. K., and Schoebi, D. (2015). Lean on me: susceptibility to partner
affect attenuates psychological distress over a 12-month period. Emotion 15, 201–210.
doi: 10.1037/emo0000043
Røsand, G. M. B., Slinning, K., Røysamb, E., and Tambs, K. (2014). Relationship
dissatisfaction and other risk factors for future relationship dissolution: a population-
based study of 18,523 couples. Soc. Psychiatr. Psychiat. Epidemiol. 49, 109–119.
doi: 10.1007/s00127-013-0681-3
Ryan, O., Haslbeck, J., and Waldorp, L. (2023). Non-stationarity in time-
series analysis: modeling stochastic and deterministic trends. PsyArXiv.
doi: 10.31234/osf.io/z7ja2
Saxbe, D., and Repetti, R. L. (2010). For better or worse? Coregulation of
couples’ cortisol levels and mood states. J. Personal. Soc. Psychol. 98, 92–103.
doi: 10.1037/a0016959
Sbarra, D. A., and Ferrer, E. (2006). The structure and process of emotional
experience following nonmarital relationship dissolution: dynamic factor analyses of
love, anger, and sadness. Emotion 6, 224–238. doi: 10.1037/1528-3542.6.2.224
Schoebi, D. (2008). The coregulation of daily affect in marital relationships. J. Fam.
Psychol. 22, 595–604. doi: 10.1037/0893-3200.22.3.595
Sels, L., Ceulemans, E., Bulteel, K., and Kuppens, P. (2016). Emotional
interdependence and well-being in close relationships. Front. Psychol. 7, 1–13.
doi: 10.3389/fpsyg.2016.00283
Sels, L., Ceulemans, E., and Kuppens, P. (2018). “A general framework for
capturing interpersonal emotion dynamics,” in Interpersonal Emotion Dynamics in
Close Relationships, eds. A. K. Randall and D. Schoebi (Cambridge: Cambridge
University Press), 27–46.
Shumway, R. H., and Stoffer, D. S. (2011). Time Series Analysis and Its Applications:
With R Examples, 3rd Edn. Berlin: Springer.
Steyerberg, E. W., Eijkemans, M. J., and Habbema, J. D. (1999). Stepwise selection
in small data sets: a simulation study of bias in logistic regression analysis. J. Clin.
Epidemiol. 52, 935–942. doi: 10.1016/S0895-4356(99)00103-1
Thompson, A., and Bolger, N. (1999). Emotional transmission in couples under
stress. J. Mar. Fam. 61:38. doi: 10.2307/353881
Townsend, S. S., Kim, H. S., and Mesquita, B. (2014). Are you feeling what I’m
feeling? Emotional similarity buffers stress. Soc. Psychol. Personal. Sci. 5, 526–533.
doi: 10.1177/1948550613511499
Vallacher, R. R., Nowak, A., and Zochowski, M. (2005). Dynamics of social
coordination: the synchronization of internal states in close relationships. Interact.
Stud. 6, 35–52. doi: 10.1075/is.6.1.04val
Wood, S. N. (2017). Generalized Additive Models: An Introduction With R, 2nd Edn.
London: Chapman and Hall/CRC.
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