2012. Vol.3, No.12A, 1091-1099
Published Online December 2012 in SciRes (
Copyright © 2012 SciRes. 1091
The Course of Well-Being in Romantic Relationships: Predicting
Positive Affect in Dating Participants
Katherine Jacobs Bao
Psychology Department, University of California, Riverside, USA
Received August 11th, 2012; revised September 8th, 2012; accepted October 11th, 2012
People use different methods to make themselves happier, but their attempts at lasting happiness are often
thwarted by the hedonic adaptation process. We examined changes in well-being over 8 weeks in partici-
pants who were involved in romantic relationships and those who were not. On average, both groups de-
clined in well-being over time, but the relationship group experienced more positive emotions overall.
High positive affect was predicted by higher aspirations, higher passionate love, and being in a same-eth-
nicity relationship. None of the variables we measured significantly predicted changes in positive affect
over time, which may be due to the short duration of the study.
Keywords: Happiness; Well-Being; Hedonic Adaptation; Romantic Relationships
“The greatest degree of inner tranquility comes from the de-
velopment of love and compassion. The more we care for the
happiness of others, the greater is our own sense of well-being.”
—Tenzin Gyatso, the 14th Dalai Lama
What makes people happier? Is it buying the latest gadget?
Trying a new hobby? Starting a new relationship? A variety of
activities allow people to experience increases in happiness, but
it is unclear whether such increases are long-lasting. Consistent
with researchers’ definitions, we conceptualize happiness (also
known as subjective well-being) as a combination of high life
satisfaction, frequent positive affect, and infrequent negative
affect, (e.g., Diener, Suh, Lucas, & Smith, 1999). Both empiri-
cal and anecdotal evidence suggest that most attempts by indi-
viduals to increase their happiness result in a temporary boost
in well-being, followed by a gradual return to their original
“baseline” level. By understanding the processes and mecha-
nisms underlying this return to baseline (aka “adaptation”),
researchers may advance understanding of how people can
learn to slow the process down or arrest it entirely, thereby
maintaining their initial happiness boost.
Hedonic Adaptation
Hedonic adaptation is reflected by a change (gain or loss) in
happiness after the experience of a valenced stimulus or event,
followed by a gradual return to baseline (Frederick & Loewen-
stein, 1999). Frederick and Loewenstein (1999) have argued
that hedonic adaptation is evolutionarily adaptive. When indi-
viduals experience high levels of positive or negative affect,
they cannot help but focus on those intense feelings. This atten-
tion on their affect can make it difficult to function, because
people need to focus on their basic needs in order to survive.
Thus, people hedonically adapt as a means of reducing high
arousal, allowing them to direct their attention to more impor-
tant needs, as well as to novel opportunities and threats. Finally,
if individuals did not adapt to the attainment of their goals, they
would be less likely to aspire to and pursue even loftier and
more significant goals.
To explore the process of adaptation, several researchers
have investigated changes in people’s life satisfaction both
before and after a major life event, using longitudinal designs.
Several large nationally representative panel studies have found
evidence of a failure to adapt to negative events. In these stud-
ies, participants were followed for several years before and
after a negative event, such as a major disability (Lucas, 2007),
unemployment (Lucas, Clark, Georgellis, & Diener, 2004),
divorce (Lucas, 2005), widowhood (Lucas, Clark, Georgellis, &
Diener, 2003; Lucas & Clark, 2006). On average, participants
would begin to approach, but never reach, their baseline levels
of well-being after the negative event. Perhaps people would
eventually fully recover if followed for longer periods of time,
but it is possible that, on average, people never recover after
certain events. At the very least, these studies tell us that adap-
tation to negative life events is a slow process.
Although fewer studies have been conducted on people’s re-
actions to positive events, interestingly, these studies show
different rates of adaptation in response to positive versus nega-
tive experiences. For example, Lucas and colleagues observed
changes in life satisfaction in the years before and after mar-
riage and found that although participants varied in their degree
of adaptation, on average, people tended to return to baseline
levels of life satisfaction after about 2 years (Lucas et al., 2003).
Similarly, in the work domain, researchers have followed peo-
ple starting a new job and found that, on average, people
quickly lost the “boost” in well-being from starting the job and
returned to baseline levels (Boswell, Boudreau, & Tichy, 2005).
The results of these studies support the idea that adaptation to
positive events is faster and more complete than adaptation to
negative events.
In sum, the literature suggests that hedonic adaptation to
positive experiences may be one of the biggest obstacles to
happiness. After all, if people ultimately “get used to” every-
thing positive that happens in their lives, then how can they
ever become happier? The implication is that an individual who
desires to increase his or her happiness would do well to put
effort into thwarting adaptation. Those who experience a posi-
tive life event (such as starting a new relationship) may be able
to use certain strategies to either slow or stop adaptation. The
hedonic adaptation prevention model (Lyubomirsky, 2011;
Sheldon & Lyubomirsky, 2012) posits that adaptation occurs
via two paths—through decreases in positive emotions and
through increases in aspirations. According to the model, when
someone experiences a positive change, such as beginning a
new romantic relationship, that change will generate a stream of
positive events, which, in turn, will trigger increases in positive
emotions. These increases in positive emotions lead to a boost
in well-being. However, over time, the positive life change will
produce fewer and fewer positive events, and thus fewer posi-
tive emotions, so well-being will begin to decline. In this case,
positive events and positive emotions are mediators of the ad-
aptation process. After all, more positive events and positive
emotions are associated with higher well-being. For example,
as a young man begins a new relationship, he experiences new
positive events (e.g., going out on fun dates and meeting new
people) and thus more positive emotions (e.g., excitement, en-
ergy, affection). This leads to a boost in happiness. However,
over time, these positive events and positive emotions become
less frequent, so he experiences fewer and smaller boosts in
happiness, and thus begins to adapt.
Another path that underlies the process of adaptation in-
volves a rise in people’s aspiration levels, which typically oc-
curs after they experience a positive change and the subsequent
increase in positive events. In other words, over time, the posi-
tive events begin to become expected and predictable, and thus
do not lift their well-being as much as they used to. Aspirations
are another mediator of adaptation, such that higher aspirations
are associated with lower well-being. For example, after a col-
lege student in a new relationship starts going out on dates
every Friday night, she starts to expect to go out on those dates
each week, and thus each date leads to less of a gain in well-
being than the previous one. She may even start to develop
higher aspirations (e.g., going out on more fun dates every
week or boosting her partner’s level of commitment), so the
once-a-week date does not provide as much of a boost in well-
being as it once did.
According to the hedonic adaptation prevention model, both
paths underlying the course of hedonic adaptation are also
moderated by several key variables, including surprise, vari-
ability, and appreciation (Lyubomirsky, 2011; Sheldon &
Lyubomirsky, 2012). The more surprising and varied the posi-
tive events, the longer it takes to adapt to them. So a couple that
engages in a different activity each time they have a date will
adapt to dating less quickly than a couple that does the same
activity each date night. For example, Aron and colleagues used
both survey and experimental methods to examine the associa-
tion between shared exciting activities and relationship satisfac-
tion (Aron, Norman, Aron, McKenna, & Heyman, 2000). The
researchers found that couples that participated in novel and
arousing activities had higher relationship satisfaction than
those who participated in more mundane activities. Furthermore,
using an experimental design, they found that induced shared
participation in novel and arousing activities increased rela-
tionship quality, compared to performing a neutral task. In an-
other longitudinal study, researchers also found that self-re-
ported marital boredom predicted significantly lower rela-
tionship satisfaction 9 years later, even after controlling for
relationship satisfaction at the initial assessment (Tsapelas,
Aron, & Orbuch, 2009). Novelty and excitement are related to
variety and surprise, two moderators of the adaptation process.
Thus, couples that participate in more novel and exciting activi-
ties may adapt more slowly over time than those who do not
participate in such activities, because these experiences are
more variable and surprising.
Also, the less one appreciates one’s positive change in cir-
cumstances, whether the change is a new relationship, a new
car, or a new job, the more quickly one adapts. Appreciation
may slow adaptation by guarding against social comparisons
and increasing expectations (Layard, 2005). For example, if an
individual comes to expect a romantic outing each Friday and
begins to take it for granted, then she may not appreciate the
time spent together or the relationship in general as much, and
thus the outing produces less of a boost in well-being. Striving
to increase one’s appreciation of positive changes, as well as
the other moderators discussed, may be critical to learning how
to forestall adaptation.
Romantic Relationships
Relatively little research has been conducted on adaptation to
romantic relationships, but the large existing literature on other
aspects of romantic relationships bears some relevance for how
adaptation may play out in relationships and how it may be
thwarted. Many studies, for example, have found evidence for
decreases in satisfaction across the course of both marital and
dating relationships (Kurdek, 1999, 2002; Rusbult, 1983; see
Karney & Bradbury, 1995, for a review). These patterns of
results are consistent with the phenomenon of hedonic adapta-
tion. However, such studies typically do not follow participants
before and after starting a relationship; they examine partici-
pants who are already in romantic relationships.
In the present study, we charted changes in well-being for
people who were in romantic relationships (i.e., the “relation-
ship group”) and those who were not in romantic relationships
(i.e., the “control group”) using online surveys that were com-
pleted once a week for 8 weeks.
The relationship group will have higher well-being than the
control group. Research suggests that people in relation-
ships tend to be happier than those who are not in relation-
ships (Dush & Amato, 2005; see Lyubomirsky, King, &
Diener, 2005, for a review).
All participants will show a decline in well-being over time.
Previous studies have found that students’ well-being tends
to decrease over the course of an academic quarter (Lyubo-
mirsky, Sheldon et al., 2005).
The length of the relationship will predict well-being, such
that longer relationships will be associated with lower well-
being, and shorter relationships will be associated with a
steeper decline in well-being over time. People in longer-
term relationships (i.e., 2 or more years) will likely have al-
ready adapted, and thus experience lower well-being. Those
in newer relationships will still be experiencing adaptation,
which may be associated with more rapid declines in well-
Individual differences in relationship composition will be
Copyright © 2012 SciRes.
associated with different rates of adaptation. Specifically,
first, participants in same-sex relationships will decline in
well-being more slowly than those in opposite-sex relation-
ships. Second, participants in different-ethnicity relation-
ships will decline in well-being more slowly than those in
same-ethnicity relationships. Participants in same-sex or
different-ethnicity relationships may face disapproval from
their family (Kurdek, 2004) or society (Donovan, Heaphy,
& Weeks, 1999; Murstein, Murigihi, & Malloy, 1989).
These outside threats to the relationship may increase the
elements of variety and surprise experienced on a daily ba-
sis, thus slowing the rate of adaptation (Lyubomirsky,
Higher aspirations will predict a more rapid decline in
well-being over time. As discussed above, Lyubomirsky’s
model of adaptation to positive events (2011) posits that as-
pirations mediate the adaptation process, such that higher
aspirations increase the rate of adaptation.
Higher passionate love scores will be associated with
higher levels of well-being overall and a slower decline in
well-being. Previous research has found passionate love to
be a significant predictor of positive emotions (Kim & Hat-
field, 2004). Participants who are experiencing high levels
of passionate love are relatively more likely to be in the
early stages of their relationship or to have reignited intense
feelings and/or intimacy in their relationship, and therefore
are less likely to have adapted. High levels of passionate
love may also buffer against adaptation, because passion
may be associated with higher levels of surprise and variety
within a relationship.
Three-hundred five undergraduate students (77 men, 210
women) at a Southern California public university participated
in the study. Participants were recruited from introductory
Psychology courses, and received course credit for their par-
ticipation. Students ranged in age from 18 to 34 (M = 19.25),
and the ethnicities of the sample reflected that of the university
(33.8% Asian, 22.6% Latino, 17.7% White, 2.6% African
American, 9.2% biracial, 8.2% other, and 5.9% unknown). The
“control” group comprised 155 students who had not been in a
romantic relationship for at least the last 3 months. In addition,
150 students were recruited for the “relationship” group—that
is, those students who had been in a romantic relationship for a
minimum 1-month duration. Of the participants in the relation-
ship group, 77% had an opposite-sex partner, 17.6% were
males with male partners, and 5.4% were females with female
partners. In the relationship group, 65.5% of participants were
the same ethnicity as their partner, and 34.5% were a different
ethnicity than their partner.
We followed participants over the course of 8 weeks (identi-
fied as Week 1 through Week 8). Students in both the relation-
ship and control groups completed online questionnaires as-
sessing their well-being, as well as other variables, once a week.
The relationship group completed additional measures about
their romantic relationships each week.
During Week 1, we first measured basic demographic in-
formation. Additional measures were administered each week
of the study. Participants were asked about their current well-
being, followed by measures of aspirations about future affect
and relationship satisfaction (or life satisfaction) for the next
week, followed by a measure of emotion. Relationship group
participants also completed a measure of passionate love after
the emotions measure.
Demographics. We collected information about the partici-
pants’ sex, ethnicity, and religious affiliation. Similar informa-
tion was also obtained about the participant’s partner for those
in the relationship group.
Current well-being. We measured participants’ current well-
being each week using an affect measure and a satisfaction
measure, which differed slightly for our two groups. All par-
ticipants used a sliding scale, which ranged from extremely
negative to extremely positive for the affect questions and ex-
tremely dissatisfied to extremely satisfied for the satisfaction
questions. The slider recorded scores from 0 to 600, but the
participants could not see these numbers. For the control par-
ticipants, we measured general affect (“How do you feel right
now?”) and life satisfaction (“How satisfied with your life are
you right now?”). For the relationship participants, we meas-
ured feelings about the relationship (“How do you feel about
your relationship right now?”) and relationship satisfaction
(“How satisfied with your relationship are you right now?”).
Reliability for the control group items was higher (Cronbach’s
α from 0.80 to 0.93) than for the relationship group items
(Cronbach’s α from 0.67 to 0.82).
Current aspirations. We measured participants’ aspirations
each week using an affect measure and a satisfaction measure.
This measure mirrored the well-being measure, above. We
asked about future affect (“How do you expect to feel this
week?/How do you expect to feel this week about your rela-
tionship partner?”) and future satisfaction (“How satisfied with
your life do you expect to feel this week?/How satisfied with
your relationship do you expect to feel this week?”) of the con-
trol and relationship groups, respectively. Similar to the well-
being measure, reliability for the control group questions was
higher than that for the relationship group questions (Cron-
bach’s α from 0.84 to 0.94 and from 0.61 to 0.88, respectively).
Multiple Affect Adjective Check List (MAACL). We measured
emotions using the MAACL (Zuckerman & Lubin, 1965), but
abridged the measure from 70 to 55 items by removing items
that had not shown much variance in previous studies in our
laboratory. The MAACL items are yes/no questions about
whether participants felt a particular emotion (e.g., affectionate,
hostile, joyful, discouraged) this week. We also added ques-
tions about the frequency and intensity of emotions for each
emotion the participant checked. That is, when a participant
checked “yes” for an emotion, the following questions popped
up: “For every feeling you checked above, please rate how
intense it was using the following scale” (1 = not at all intense,
7 = extremely intense) and “For every feeling you checked
above, please rate how frequently you experienced it this week
using the following scale” (1 = very rarely, 7 = all the time).
Cronbach’s α for this scale ranged from 0.89 to 0.91 over the 8
weeks. Cronbach’s α for just the positive emotions ranged from
0.91 to 0.93 over the 8 weeks.
Passionate Love Scale. We assessed passionate love using
Copyright © 2012 SciRes. 1093
Hatfield and Sprecher’s (1986) Passionate Love Scale. This is a
14-item measure using a 9-point Likert-type scale (1 = not at all
true, 9 = definitely true) to assess the amount of passion the
participant feels toward his or her current relationship partner.
Items include “I would feel despair if _____ left me” and
“Sometimes I feel I can’t control my thoughts; they are obses-
sively on _____.” Reliability estimates were high (Cronbach’s
α = 0.92 to 0.97) for the 8 weeks.
Overview of Analyses
Because the design was longitudinal, we used multilevel
modeling to analyze the data (with SAS proc mixed). Multi-
level modeling allowed us to simultaneously estimate between-
person and within-person changes in well-being over time.
Thus, we were able to examine how individuals change over
time (within person), and how individuals differ in their initial
well-being and their changes in well-being.
We ran Ordinary Least Squares (OLS) regression to deter-
mine the average shape of individuals’ trajectories, and the R2
estimates for a cubic model were higher than those of a linear
or a quadratic model. Thus, our baseline multilevel model was
00 102030
01 23
γγ γγ
ζζ ζζε
ijij ij ij
iiij iij iijij
YWeek WeekWeek
Week Week Week
 
 
Our dependent variable (Yij) was the number of positive emo-
tions participants experienced that week. The intercept term (γ00)
represents the average number of positive emotions reported
during Week 2. Week was centered on Week 2 because our
model is cubic, and thus centering on Week 1 would make it
more difficult to interpret the nonlinear parameters. The γ10
parameter, associated with the Week predictor, is the average
rate of change in the number of positive emotions reported. The
γ20 parameter, associated with the Week2 predictor, is the cur-
vature parameter, which represents the rate at which the slope
changes. The γ30 parameter is associated with the Week3 pre-
dictor and represents the rate at which the curvature changes.
The portion of the model in brackets represents the error terms.
Because our intended well-being measure (i.e., “current
well-being”) had low reliability for the relationship group, we
opted to use as our key measure of well-being the number of
positive emotions that participants reported. Overall, as ex-
pected, a significant decline in positive affect over time was
observed for both the relationship and control groups (see Fig-
ure 1, top). Although the groups did not significantly differ in
rate of decline (see Table 1, far right column), they did differ in
the intercept (second to last column). A comparison with the
baseline model suggested that adding “group” as a predictor
marginally improved fit, χ2 (1) = 3.2, p = 0.07. In addition, a
model with the interaction term (Group X Week) to predict the
slope did not show a significantly better fit than a model with
no interaction term, χ2 (1) = 1.7, p = 0.19. Thus, the relationship
group had significantly higher positive affect at Week 2 (the
intercept) than the control group, but the two groups did not
significantly differ in slope. Because we were interested in the
predictors of positive affect for those in romantic relationships,
all further analyses examined just the relationship group.
Figure 1.
Group membership (relationship vs. control) predicting number of
positive emotions over time (top). Ethnicity composition of relationship
predicting number of positive emotions over time (left). Aspirations
predicting number of positive emotions over time (right).
We tested whether the amount of time a person had been da-
ting their partner influenced their positive affect or the change
in positive affect over time. Contrary to our hypotheses, the
amount of time a person had been dating was not a significant
predictor of positive affect at Week 2 (the intercept) or the
change in positive affect over time (see Table 2, left). The
goodness of fit of these models did not significantly differ from
the baseline model, χ2 < 1, p > .05. We did retain the model
with dating duration as a predictor as a comparison model for
subsequent analyses, because we wanted to be able to interact
the dating duration variable with time-varying predictors. Thus,
our comparison model for all subsequent analyses was as fol-
00 10203001
01 23
γγ γγγ
ζζ ζζε
ijij ij ij
ii iji iji ijij
YWeek Week Week TimeDating
Week Week Week
 
 
Copyright © 2012 SciRes.
Copyright © 2012 SciRes. 1095
Table 1.
Group predicting positive affect—all participants.
Effect Param Baseline Group Group × Week
Intercept 00 10.47*** 9.93*** 9.85***
Week 10 –1.96*** –1.96*** –1.89***
Week2 20 0.50*** 0.50*** 0.50***
Week3 30 –0.04*** –0.04*** –0.04***
Group 01 - 1.22 1.42*
Fixed Effects
Grp × Wk 02 - - –0.14
Level 1 2
σ 12.95*** 12.96*** 12.96***
σ 30.01*** 29.63*** 29.65***
σ 2.14*** 2.14*** 2.09***
σ 0.24 0.24 0.24
Random Effects
Level 2
σ 0.002 0.002 0.002
Deviance 10904.5 10901.3 10899.6
AIC 10934.5 10933.3 10933.6
Good- ness of Fit
BIC 10988.7 10991.1 10995.0
Note: *p < 0.05; **p < 0.01; ***p < 0.001.
Table 2.
Time dating, sex composition, and ethnicity composition predicting positive affect—relationship group only.
Time Dating Sex Composition Ethnicity Composition
Effect Param Baseline Time DatingTime Dating*
Week Sex Comp.Sex Comp × Week Ethnicity
Ethnicity Comp ×
Intercept 00 11.14*** 10.66*** 10.63*** 10.35*** 10.60*** 11.86*** 11.84***
Week 10 –2.37*** –2.37*** –2.33*** –2.37*** –2.64*** –2.38*** –2.35***
Week2 20 0.63*** 0.63*** 0.63*** 0.63*** 0.63*** 0.63*** 0.63***
Week3 30 –0.05*** –0.05*** –0.05*** –0.05*** –0.05*** –0.05*** –0.05***
Time Dating 01 - 0.0008 0.0008 0.0008 0.0008 0.001 0.001
Time Dating
× Week 02 - -
(Time Dating ×
(Sex Comp)
(Sex Comp)
(Eth Comp)
(Eth Comp)
03 - - - -
(Sex Comp ×
- –0.04
(Eth Comp × Week)
Level 1 2
σ 14.55*** 14.56*** 14.56*** 14.56*** 14.57*** 14.52*** 14.52***
σ 23.32*** 23.06*** 23.06*** 23.16*** 23.13*** 22.36** 22.35***
σ 2.80** 2.81** 2.82** 2.81** 2.81** 2.87** 2.88*
σ 0.59* 0.59* 0.59* 0.59* 0.59* 0.60* 0.60*
Effects Level 2
σ 0.006 0.006 0.006 0.006 0.006 0.007 0.007
Deviance 5013.5 5012.8 5012.6 5012.6 5009.4 4967 4966.9
AIC 5043.5 5044.8 5046.6 5046.6 5045.4 5001 5002.9
Goodness of
BIC 5085.3 5089.4 5094 5094 5095.6 5048.2 5052.9
Note: *p < 0.05; **p < 0.01; ***p < 0.001.
Contrary to our predictions, whether a participant was in a
same-sex or opposite-sex relationship did not predict change in
positive affect over time. The goodness of fit test for this model
was not significantly different from the comparison model, χ2
(2) = 3.4, p = 0.18. The gender composition of the couple did
not significantly predict the intercept either, χ2 (1) = 0.2, p =
0.65 (see Table 2, middle). Thus, participants in same-sex rela-
tionships did not differ in positive affect at Week 2 and did not
decline in positive affect more slowly than those in opposite-
sex relationships.
We hypothesized that those in different-ethnicity relation-
ships would have a shallower decline in positive affect over
time, but this hypothesis was not supported (see Table 2, right).
No significant difference emerged in goodness of fit between a
model with the Ethnicity Composition X Week interaction term
and our comparison model, χ2 (2) = 0.1, p = 0.75. However, the
intercept was a significant predictor of positive affect, χ2 (1) =
45.8, p < 0.0001, indicating that those who were in differ-
ent-ethnicity relationships had lower positive affect at Week 2
than those who were in same-ethnicity relationships (see Fig-
ure 1, middle).
Aspirations were a significant predictor of the intercept for
positive affect, but not the slope. Aspirations significantly pre-
dicted positive affect at Week 2 (see Table 3, left). The good-
ness of fit index for this model was significantly different from
that of the comparison model, χ2 (6) = 104.7, p < 0.0001. How-
ever, the relationship between aspirations and positive affect
was in the opposite direction than we predicted. Higher aspira-
tions were predictive of higher positive affect, rather than lower
positive affect (see Figure 1, bottom). Contrary to our hypothe-
sis, aspirations did not predict change in positive affect over
time, χ2 (1) = 0.9, p = 0.34).
Passionate Love
Supporting our hypothesis, passionate love was a significant
predictor of positive affect. Higher passionate love was predic-
tive of higher positive affect at Week 2 (see Table 3, right).
The goodness of fit index of this model was significantly dif-
ferent from that of the comparison model, χ2 (6) = 48.6, p <
0.0001. However, passionate love was not a significant pre-
dictor of the change in positive affect over time. The goodness
of fit for a model predicting the slope was not significantly
different from the comparison model, χ2 (1) = 1.3, p = 0.25.
We also tested whether a significant interaction existed be-
tween dating duration and passionate love scores. We did not
find support for this model, χ2 (1) = 2.1, p = 0.15; see Table 3,
right). Thus, the interaction between dating duration and pas-
sionate love scores did not significantly predict positive affect
Table 3.
Aspirations and passionate love predicting positive affectrelationship group only.
Aspirations Passionate Love
Effect Param
Model Asp Asp ×
Model Pass Love Pass Love ×
Pass. Love ×
Time Dating
Intercept 00 10.61*** 11.14*** 11.17*** 10.61*** 10.62*** 10.63*** 10.68***
Week 10 –2.40*** –2.12*** –2.11*** –2.40*** –2.27*** –2.25*** –2.27***
Week2 20 0.65*** 0.47*** 0.46*** 0.65*** 0.59*** 0.59*** 0.60***
Week3 30 –0.06*** –0.04*** –0.04*** –0.06*** –0.05*** –0.05*** –0.05***
Dating 01 0.0004 0.0004 0.0003 0.0004 0.0009 0.0009 0.0009
02 - 0.02***
(Asp) - 1.10***
(Pass Love)
(Pass Love)
(Pass Love)
03a - -
(Asp ×
- -
(Pass Love ×
03a - - - - - -
–0.0006 * (Pass
Love × Time
Level 1 2
σ 14.51*** 12.81*** 12.82*** 14.51*** 13.81*** 13.81*** 13.92***
σ 22.67*** 21.79** 21.93*** 22.67*** 22.82*** 23.20*** 22.27***
σ 2.90** 1.60* 1.58* 2.90** 2.80** 2.75** 2.97**
σ 0.63* 0.11 0.11 0.63* 0.45 0.46 0.45
σ 0.007 0 0 0.007 0.004 0.004 0.003
Effects Level 2
σ - 0.0002** 0.0002** - 0.43 0.43 0.31
Deviance 4977.1 4872.4 4871.5 4977.1 4928.5 4927.2 4926.4
AIC 5009.1 4914.4 4915.5 5009.1 4972.5 4973.2 4972.4
of Fit
BIC 5053.7 4972.9 4976.8 5053.7 5033.8 5037.3 5036.5
Note: *p < 0.05; **p < 0.01; ***p < 0.001. aThe two passionate love interaction terms were not included in the same model.
Copyright © 2012 SciRes.
at the intercept (Week 2).
Our findings showed that all our participants—both those
who were involved in relationships and those who were not—
reported experiencing fewer positive emotions over the 8 weeks
of the study. None of the variables we measured significantly
predicted this change in positive affect, however, but the eth-
nicity composition of the relationship (i.e., different-ethnicity
vs same-ethnicity), self-reported aspirations, and passionate
love significantly predicted the dating participants’ initial posi-
tive affect.
As hypothesized, those in the relationship group reported
more positive emotions than those in the control group, al-
though this difference was only marginally significant. Many
studies have found that married people are happier than unmar-
ried people (see Lyubomirsky, King et al., 2005, for a review).
Although fewer studies have examined non-married couples,
research suggests that people in committed and dating relation-
ships are happier than those who are single (e.g., Dush &
Amato, 2005).
As in previous studies, our undergraduate participants de-
clined in well-being over the course of the study, on average
(Lyubomirsky, Sheldon et al., 2005). This may be because stu-
dents typically experience increased stress and pressing dead-
lines as the academic quarter progresses, and thus experi- ence
a decline in happiness. No difference emerged, however, in the
rate of well-being decline between the relationship and control
Surprisingly, the amount of time participants had been in a
romantic relationship was not a significant predictor of their
positive affect at Week 2 or the rate at which their positive
affect declined. This was contrary to our hypotheses, but may
be explained by the fact that we only followed our participants
for 2 months and that, when they began our study, many of
them had already been dating for a long period of time (M =
633 days, Mdn = 425 days). It is likely that 8 weeks is not long
enough to capture meaningful changes in well-being, especially
in long-dating participants who may have already adapted to
their relationships when they entered our study.
Participants in same-sex relationships did not differ from par-
ticipants in opposite-sex relationships in initial positive affect
or in change in positive affect over time. Although this finding
countered our hypotheses, it was not too surprising. Those in
same-sex relationships may have experienced few obstacles in
the relatively liberal college environment that served as the
setting for our study. Thus, our participants who were involved
in same-sex relationships may not have experienced much dis-
approval or censure.
Participants in different-ethnicity relationships had signifi-
cantly lower positive affect at Week 2 than those in same-
ethnicity relationships, but there was no group difference in the
rate of decline of positive affect. A possible explanation is that
those in different-ethnicity relationships experience frowns or
disapproval from their family members or others, which could
lower their positive affect, especially if they live at home. This
argument is bolstered by the fact that a higher than average
percentage of students from which the college where our par-
ticipants were recruited reside at home. Finally, we hypothe-
sized that outside disapproval would increase positive affect, as
the participants may respond to adversity by becoming closer to
their partner (i.e., an “us against the world” mindset), but we
did not find evidence of such a process.
We also found that higher aspirations predicted higher posi-
tive affect but did not predict a change in positive affect over
time. Although this is the reverse of what we hypothesized,
similar results have been found with the aspirations measure
used in the current study (Boehm, 2010). Our speculation is
that our measure of aspirations did not actually tap aspirations
but, instead, ended up serving as an indicator of well-being. For
example, we asked our control participants, “How do you ex-
pect to feel this week?” and our relationship participants, “How
do you expect to feel this week about your relationship part-
ner?” Participants’ responses may have been strongly influ-
enced by their current moods, thus rendering the measure more
diagnostic of their current well-being than their aspirations for
the next week. In the future, researchers should aim to develop
new measures that show an ability to discriminate between
aspirations and well-being. For instance, it may be that people’s
general aspirations are too closely linked with well-being, but
more specific aspirations (e.g., how many times a person ex-
pects to see her partner next week) may not be. Thus, a measure
that taps more specific aspirations, or even aspirations for spe-
cific life domains (e.g., Kasser & Ryan, 1993), may be predict-
tive of changes in well-being over time.
Finally, passionate love scores were predictive of partici-
pants’ positive affect at Week 2, but not of the change in posi-
tive affect over time. Thus, higher passionate love scores pre-
dicted higher positive affect, which is consistent with previous
research (Kim & Hatfield, 2004). The interaction between pas-
sionate love scores and relationship duration was not a signifi-
cant predictor of positive affect, however. Thus, time dating did
not moderate the relationship between passionate love and pos-
itive affect, suggesting that differences in well-being of those
who reported higher or lower passionate love did not change
depending on the length of the relationship.
The current study had several limitations. First, we did not
measure participants’ well-being before they entered into a
relationship, so we could not study the full course of the adap-
tation process. Well-being needs to be assessed prior to partici-
pants starting a relationship, so that shifts in well-being over
time could be compared to baseline well-being (i.e., how happy
the participants were before receiving the hypothesized “boost”
from beginning a new relationship). This way, it is possible to
determine when adaptation begins and when participants have
returned to their baseline well-being level. Ideally, future stud-
ies would recruit participants who are actively seeking a rela-
tionship, such as those on a dating web site, and measure their
well-being before and after starting a relationship. Such a de-
sign would allow us to model the entire adaptation process
from beginning to end, as similar studies have done with mar-
ried couples (e.g., Lucas et al., 2003).
Another limitation is that our study only followed the par- ti-
cipants for 8 weeks, which is a short period of time compared
to other studies that have examined changes in relationship
satisfaction and well-being (e.g., Lucas et al., 2003). Indeed,
many relationship and marriage studies follow participants for
years, and thus are able to observe significant changes in satis-
faction over time. We may have seen more meaningful changes
in well-being—and been able to capture the role of key mod-
Copyright © 2012 SciRes. 1097
erators and mediators in the adaptation process—if we had
followed our participants for longer than 8 weeks. Furthermore,
the nonlinearity of the data decreased the power, decreasing the
chances of finding significant predictors of changes in positive
emotions over time.
Future Research
Future research should strive to recruit a broader sample—
beyond merely college students—as the pattern of results may
differ for older participants. Young adults, many of whom may
be in their first serious relationships, may be less likely to adapt
rapidly to such novel experiences. They may be more likely to
savor and appreciate the experiences because they are under-
going them for the first time. Young adults also may feel less
rushed in their relationships (e.g., to get married or start a fam-
ily), and thus may have more time to relish their relationship
experiences. Also, by assessing participants on many occasions,
including weeks, months, or years before they start a romance,
we may obtain a better understanding of the processes underly-
ing adaptation to intimate relationships. Such a long period of
assessment would allow us to capture not only those who adapt
relatively quickly (e.g., days or weeks), but also those who may
take years to adapt to their relationships. In addition, we would
be able to observe different trajectories of well-being over time
and possibly connect those changes in well-being with different
life events (e.g., getting engaged, having a child, etc.) or with
individual difference variables (e.g., sex or ethnicity composi-
tion of the relationship or personality).
Furthermore, it is important to use a sample of participants
who are in dating, rather than married, relationships, because
much of the adaptation process occurs before one even starts to
think about getting married. The first few months of a relation-
ship may entail the biggest changes in well-being, and thus it is
important to capture those months using a dating population.
We may even find that a relationship is characterized by multi-
ple adaptation periods. Lucas and colleagues (2003) have found
evidence of adaptation to marriage, but adaptation may also
occur to the beginning of the premarital relationship. It would
be interesting to compare a person’s baseline well-being to her
well-being after adapting to a new relationship and, later, after
adapting to marriage to her relationship partner. It is possible
that people adapt completely to marriage, but they do not adapt
completely to the premarital relationship. These and other ques-
tions about well-being and hedonic adaptation remain. Learning
the answers to these questions will aid in understanding how
people can thwart adaptation to relationships, which will pro-
mote greater relationship satisfaction, higher well-being, and
may even help to lengthen relationships.
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