Journal of Individual
L. Kööts
Differences
et al.:
© Happiness
2012
2012;
Hogrefe
Vol.and
33(1):43–53
Publishing
Sadness
Original Article
Relationship Between Linguistic
Antonyms in Momentary
and Retrospective Ratings
of Happiness and Sadness
Liisi Kööts, Anu Realo, and Jüri Allik
University of Tartu, Department of Psychology, Tartu, Estonia
Abstract. Momentary ratings of affective states with a pair of strict antonyms (“happy” vs. “sad”) were studied with an experiencesampling method in a group of 110 participants during 14 consecutive days at 7 randomly determined occasions per day. Before and
after the experimental session participants also retrospectively rated how happy or sad they had been during the previous 2 weeks.
Multilevel analysis showed that, at the level of single measurement trials, the momentary ratings of happiness and sadness were
moderately negatively correlated (r = –.32, p < .001). A between-subject correlation of the two antonyms, however, was in a positive
direction (r = .13, p = .123). Participants experienced mixed feelings during a considerable number of measurement trials, whereas the
tendency to feel mixed emotions was predicted by all Big Five personality traits except Agreeableness. A configural frequency analysis
(CFA) demonstrated that, although there was no strict bipolarity between momentary ratings of happiness and sadness, they were
nevertheless used in an exclusive manner in many occasions.
Keywords: happiness, sadness, mixed emotions, personality
Native speakers have strong intuitions about which pairs of
words are good examples of antonyms. Probably all English speakers would agree, for example, that happy is the
opposite of sad, very much like cold is the opposite of hot
(Cruse, 1986). Accordingly, “when you are happy, you are
not sad and when you are sad, you are not happy” (Russell
& Carroll, 1999, p. 25). It was therefore a surprising discovery that individuals tend to characterize their momentary or recent affective experience in two relatively independent ways: A person’s degree of happiness does not predict their degree of sadness, even if both judgments are
made practically at the same time (Diener & Emmons,
1985; Watson & Tellegen, 1985). Very often, retrospective
or momentary measures of affect demonstrate only a weak
negative correlation between positive and negative emotions (Watson, Clark, & Tellegen, 1988). In Estonian, for
example, linguistic antonyms such as happy (rõõmus) and
sad (kurb) are not true opposites when they are used retrospectively to rate recent emotional experience (Allik &
Realo, 1997). This means that people report positive and
negative feelings at the same time, and that these two affective states are not polar opposites. Although paradoxical, the separability of positive and negative affect has acquired remarkable popularity among researchers. For ex© 2012 Hogrefe Publishing
ample, a short measure of positive and negative affect –
PANAS (Watson et al., 1988) – has been cited 5,819 times
(Web of Science, May 9, 2011) since its publication 23
years ago.
As expected, such an idea, contradicting as it does linguistic intuition, did not take long to bring about heated
discussion. Green, Goldman, and Salovey (1993) challenged the idea, claiming that a nonrandom measurement
error can mask bipolarity. They argued that, after adjusting
for random and systematic error in positive and negative
affect, a correlation between the two affects that may be
close to 0 becomes closer to –1, indicating that the relationship between the two variables is mutually exclusive. Although systematic error can attenuate the observed correlation between positive and negative affect, it still does not
explain why measures of positive and negative emotions
behave in many situations as if they were relatively independent (Rafaeli & Revelle, 2006; Schimmack, 2001; Tellegen, Watson, & Clark, 1999).
Another challenge to the separability of positive and
negative emotions comes from Russell (2003), who characterizes a prototypical emotional episode by two basic dimensions: feeling good or bad and energized or lethargic.
These states – called core affect – are supposed to have an
Journal of Individual Differences 2012; Vol. 33(1):43–53
DOI: 10.1027/1614-0001/a000061
44
L. Kööts et al.: Happiness and Sadness
effect on reflexes, perception, cognition, and behavior, the
causes of which are both internal and external; yet people
have no direct access to those causal connections. The central point of the core affect theory is that, in terms of evaluation, all feelings are one-dimensional – either good or
bad – and never a mixture of these polar opposites. Yik,
Russell, and Feldman Barrett (1999) argue that the apparent
contradiction between the core affect model and the positive and negative affect model can be resolved by a 45 °
rotation of dimensions, after which the two different representations can be perceived as essentially identical (Yik,
Russell, & Feldman Barrett, 1999). It is claimed that
PANAS scales, for example, do not measure “pure” feelings of good or bad, but rather that these two feelings are
mixed with some degree of activation: energized-good and
energized-bad. In other words, a component is shared by
both scales. This implies, particularly, that if positive and
negative affect were measured by true linguistic antonyms
such as sad and happy, positive and negative affects would
no longer show separability. It is also argued that even correlations as low as about –.40 do not contradict the bipolarity of positive and negative affects if the response format
is adequately introduced (Russell & Carroll, 1999; Segura
& Gonzalez-Roma, 2003).
In this paper, we take another look at the issue of whether
people can feel happy and sad at the same time by examining the relationship between linguistic antonyms of “happy” versus “sad” using both momentary and retrospective
ratings.
Retrospective Versus Momentary Ratings of
Positive and Negative Affect
One possible reason for the separability of positive and
negative affect is retrospection. In most cases, individuals
are asked to evaluate how they have felt during the past few
days, weeks, or months. As the rated time period increases,
the probability that a respondent has experienced both positive and negative affect also increases. Even an instruction
to evaluate the momentary affective state does not exclude
the possibility that respondents look back in time and use
their previous experiences of positive and negative affect
in evaluating their current emotional state. Indeed, Watson
and Clark (1997) showed that the intercorrelation between
positive and negative affect is not systematically influenced by the rated timeframe. Data collected using the experience sampling method has demonstrated that even in
momentary assessments of emotion, positive and negative
emotions can coexist (Scollon, Diener, Oishi, & BiswasDiener, 2005; Vansteelandt, Van Mechelen, & Nezlek,
2005; Zelenski & Larsen, 2000).
Russell and Carroll (1999) claim that
Bipolarity has not been challenged at the level of such specific
items as happy and sad or guilty and innocent but rather at a
more abstract level: factors or scales named positive and negJournal of Individual Differences 2012; Vol. 33(1):43–53
ative. For example, Watson and Clark (1997) acknowledged
that happiness and sadness form a bipolar pair, even as they
proclaimed and defended the independence of “positive and
negative mood” (p. 270) in general (p. 6).
However, there are several indications that even semantic
opposites could not be represented along a single pleasuredispleasure dimension (Schimmack, 2001, 2009). Larsen
and colleagues also showed that it is possible to feel sad
and happy at the same time (Larsen, McGraw, & Cacioppo,
2001; Larsen & McGraw, 2011). For instance, anticipating
the end of a significant experience can give rise to a mixture
of happiness and sadness – a feeling that has been termed
“poignancy” (Ersner-Hershfield, Mikels, Sullivan, & Carstensen, 2008).
Separability Versus Bipolarity of Positive
and Negative Affect
The choice between complete separability of positive and
negative affect and their strict bipolarity is not the only one.
In addition to distinguishing between three basic options –
radical orthogonality (independence), separability, and radical bipolarity of positive and negative affect – it is possible
to make the next step and to ask whether bipolarity-separability is fixed or whether it would be possible to consider
this relationship as a variable. As Cacioppo and Berntson
(1994) put it, the question is not so much about bipolarity
or separability as about the conditions under which positive
and negative emotions are separable – and when they are
mutually exclusive. For example, it is possible that the degree of bipolarity-separability depends on the level of analysis: The correlations resulting from between-subjects and
within-subject analysis may be rather different (Scollon et
al., 2005; Vansteelandt et al., 2005; Zelenski & Larsen,
2000).
It might be also important to differentiate between levels
of analysis. Besides being a psychological phenomenon,
emotions also involve physiological processes. Physical
limitations may constrain behavioral expressions and incline behavioral guides toward bipolar (good/bad; approach/withdraw) dispositions. However, Cacioppo and
Gardner (1999) argued that these constraints do not have
the same force at the level of underlying mechanism, where
a bivalent approach may provide a more comprehensive
account of the affect system. Hence, instances of mixed
emotions are in principle allowed (Cacioppo & Gardner,
1999).
Besides interindividual differences, culture seems to influence the relationship between positive and negative
emotions. Several studies showed that the correlations between positive (or pleasant) and negative (or unpleasant)
emotions are typically less negative in Asian than in Western samples (Bagozzi, Wong, & Yi, 1999; Kitayama, Markus, & Kurokawa, 2000; Schimmack, 2009; Schimmack,
Oishi, & Diener, 2002; Scollon et al., 2005; Yik, 2007).
© 2012 Hogrefe Publishing
L. Kööts et al.: Happiness and Sadness
This could partly be explained by cognitive style, more specifically dialectical thinking, i.e., the tolerance of apparently contradictory or ambivalent beliefs that presumably
characterizes Eastern cultures. Because dialectical thinking
facilitates balanced appraisals of contradictory, positive
and negative information, it is associated with subsequent
mixed emotions (Hui, Fok, & Bond, 2009). Nevertheless,
empirical studies suggest that dialectical thinking is also
prevalent in Western cultures and serves similar psychological functions (Spencer-Rodgers, Peng, Wang, & Hou,
2004).
What makes some people more prone to dialecticism, to
experiencing opposite feelings at the same time? Until now,
individual differences in mixed emotions have not received
adequate attention, although Rafaeli, Rogers, and Revelle
(2007) argued that the tendency to experience overlapping
positive and negative moods is stable over time within persons and varies broadly across individuals (Rafaeli et al.,
2007). An earlier study showed that the structure of affect
can be predicted by personality traits related to affective
differentiation (Terracciano, McCrae, Hagemann, & Costa,
2003). Therefore, if at least some individuals can have
mixed feelings, it is important to consider which personality traits differentiate these people from those who experience happiness and sadness only in a mutually exclusive
way. In addition to broad personality domains, it is relevant
to look at lower-level personality facets that might be more
powerful in predicting and explaining actual behavior
(Paunonen & Ashton, 2001).
Finally, it is also likely that separability depends on the
intensity of emotions. Diener and Iran-Nejad (1986) observed that people do not experience both positive and negative affect on an intense level. However, if one type of
affect is at a low level, the other type of affect can be at any
level of intensity. Last but not least, bipolarity of affect
depends on the affect measure (Egloff, 1998) as well as on
the response format (a strictly unipolar response format vs
a Likert scaled response format, for instance; Schmukle &
Egloff, 2009).
Aim of the Study
This study tests whether people can feel happy and sad at
the same time. Among other analyses we also examine
whether the Big Five personality traits predict the probability of feeling happy and sad in a mixed way.
The idea of polar opposition of positive and negative
affect – or their mutual exclusiveness – was tested with
momentary (experience sampling method) and retrospective ratings with two strict linguistical antonyms: “happy” versus “sad.” The Estonian words rõõmus (“happy”)
and kurb (“sad”) are the most frequent pair of antonyms
among all affective words (Vainik, 2002). They are almost perfectly symmetrical: kurb is the only word named
as the opposite to rõõmus, and the latter is the most frequent word (91%) named in response to kurb. Unlike
© 2012 Hogrefe Publishing
45
English, Estonian “happy” has only one antonym because
the use of the word “unhappy” (mitterõõmus) is very uncommon.
According to the bipolar model of affect (Barrett &
Russell, 1998; Russell, 1979, 2003; Russell & Carroll,
1999), such identifiable linguistic antonyms should also
mark opposite poles of a single dimension when they are
used to rate immediate or recent affective experience. A
standard procedure for testing bipolarity has been to
compute linear correlations between happiness and sadness ratings (Russell & Carroll, 1999). However, the
product-moment correlation is not an appropriate test of
bipolarity because skewness and systematic response bias can attenuate actual correlations (Green et al., 1993).
The actual bipolarity of positive and negative affect
scales cannot be rejected when the correlation between
them is around –.50 or even lower (Green et al., 1993;
Russell & Carroll, 1999). Although the use of polychoric
correlation instead of product-moment correlation (Tellegen et al., 1999) can avoid some of these problems, it
is still not an optimal test of bipolarity. As observed by
Diener and Iran-Nejad (1986) already more than two decades ago, the relationship between positive and negative
affect may be more complex and is not completely captured by correlational or factor analytic approaches. The
assumption that happy and sad mark opposite poles of a
single dimension does not predict a perfectly negative
linear correlation. The idea of mutual exclusion suggests
a nonlinear relationship between these two ratings: happiness is experienced only when sadness is absent, and
sadness is reported only when there is no happiness (Diener & Iran-Nejad, 1986; Schimmack, 2001). In the contingency table of emotion ratings, this relationship leads
to an L-shape pattern (Diener & Iran-Nejad, 1986; Russell & Carroll, 1999; Schimmack, 2001).
Thus, in order to test bipolarity, it is necessary to analyze contingency tables between happiness and sadness
ratings (Diener & Iran-Nejad, 1986). The strict bipolarity
model predicts that all responses fall along the L-shape,
indicating that one of two emotions is excluded when the
other is evaluated on some positive level. In contrast, the
independence of positive and negative emotions assumes
that some of the responses fall outside the L-shape, indicating that individuals may have mixed feelings and report at least some degree of happiness and sadness at the
same time (Schimmack, 2001).
To the best of our knowledge, there is no direct statistical test to evaluate the extent to which the observed pattern of contingencies conforms to the L-shape pattern.
Diener and Iran-Nejad (1986) simply analyzed contingencies observing how negative affect varies as positive
affect increases (see also Russell & Carroll, 1999).
Schimmack (2001) used an index of mixed feelings borrowed from attitude studies (Priester & Petty, 1996). In
the attitude literature it is accepted that the reaction with
the greater number of positive and negative reactions is
referred to as the dominant reaction, and the one with less
Journal of Individual Differences 2012; Vol. 33(1):43–53
46
L. Kööts et al.: Happiness and Sadness
is referred to as conflicting reaction. All models of ambivalence are based on computing either differences or
ratios between dominant and conflicting reactions. For
example, one model that describes conflicting attitudes
fairly well claims that ambivalence is a function of five
times the conflicting reactions minus the dominant reaction when the number of conflicting reactions is below
some minimal value (Priester & Petty, 1996). Even the
introduction of the threshold value cannot account for the
radical nonlinearity presumed by the L-shape pattern. In
addition, none of these ambivalence indices specifically
tests the strict mutual exclusion of the L-shape pattern of
contingencies.
One potential method to detect the L-shape pattern is
configural frequency analysis (CFA; von Eye, 1990). The
aim of this analysis is to detect patterns in the data that
occur significantly more or significantly less often than expected under the null hypothesis of total independence. Using a log-linear procedure, CFA determines whether the
observed cell frequency of a configuration differs significantly from the expected cell frequency. Patterns that occur
more often than expected by chance are called CFA types,
while those that occur less often than expected by chance
are called CFA antitypes. Using CFA, it is possible to detect
specific configurations in the contingency table.
Method
Participants
The sample consisted of 110 participants (70 women and
40 men) with a mean age of 44.8 (SD = 23.9), ranging from
19 to 84 years. Participants received EEK 520 (about 33
EUR) for taking part in the study.
The first group of participants (n = 55; 42 women and
13 men) was recruited from two day centers in Tartu which
provide activities (exercises, dancing, singing, etc.) and
lunches for elderly people. The age of participants in this
group ranged from 61 to 84 years with a mean age of 68.2
(SD = 5.5). The majority (73%) of the respondents were
retired; about one-third (36%) of the elderly respondents
had higher education. The data were collected in autumn
2004.
The second group of participants (n = 55; 28 women and
27 men) consisted of undergraduate students from the University of Tartu and was recruited via advertisements in
university academic buildings and residence halls. Students
came from different faculties of the University of Tartu,
although students majoring in psychology were not eligible
to participate. The mean age of students was 21.3 (SD =
1.0), ranging from 19 to 23 years. The data were collected
in spring 2005. Parts of the collected data have already been
reported elsewhere (see Kööts, Realo, & Allik, 2011).
Journal of Individual Differences 2012; Vol. 33(1):43–53
Procedure
Participants visited the laboratory three times during the
course of the study (see also Kööts et al., 2011). During
the introductory session, participants were assigned a
palm-top computer (Handspring Visor Neo) and received
instructions regarding the experience-sampling part of
the study. The experience-sampling experiment was
programmed and conducted with the software iESP
(http://seattleweb.intel-research.net/projects/ESM/iESP.
html), which was built at Intel Research Seattle Lab from
existing software called ESP (the Experience Sampling
Program), developed by Dr. Lisa Feldman Barrett and
Daniel Barrett (http://www.experience-sampling.org/
esp/). Participants were told that they would be beeped
randomly 7 times per day (from 8:00 am to 8:00 pm) for
a 14-day period. It was further explained that if they did
not respond to a signal within 2 minutes, the trial would
be recorded as missing. They were told to answer the
questions as quickly as possible without compromising
accuracy. A trial automatically ended if a participant did
not respond to a question within 2 minutes. Participants
were also explicitly instructed to miss a report if it would
be a major inconvenience to complete (e.g., driving, while
showering, in rain or snow, etc.).
Participants went through a practice trial of the experiment on a palm-top computer and received a written set
of instructions about the experience-sampling procedure
before leaving the laboratory. In addition, participants
completed the Estonian version (Allik & Realo, 1997) of
the Positive and Negative Affect Schedule (PANAS;
Watson et al., 1988), which asks about the extent to
which they had experienced different emotions during
the previous 2 weeks. In this study we analyze only two
items, happy and sad, contained in the questionnaire.
Finally, they were given a copy of the Estonian version
(Kallasmaa, Allik, Realo, & McCrae, 2000) of the
Revised NEO Personality Inventory (NEO PI-R; Costa
& McCrae, 1992) which they were asked to complete
at home and return at the beginning of the second session.
A week later, the participants visited the laboratory for
the second time. During this short session an experimenter uploaded their data to a host personal computer. The
participants were given immediate feedback regarding
their level of experiment completion (i.e., response rate)
during the first week of the study. They also returned the
completed NEO PI-R questionnaires.
The third and final session took place after the experiment had ended. Participants returned their palm-top
computers to the laboratory where experimenters
explained the purpose of the study. Participants were also
given an opportunity to offer their opinions about the
experiment. Finally, they were asked to complete the
Estonian version of PANAS (Allik & Realo, 1997) for
a second time, as well as a few other short questionnaires.
© 2012 Hogrefe Publishing
L. Kööts et al.: Happiness and Sadness
47
Table 1. Frequency (%) of momentary ratings of “Happiness” and “Sadness” across 8,823 measurement trials
Happy
Sad
Not at all
To a small extent
Moderately
To a large extent
Total
Not at all
1,766 (20.0)
2,547 (28.9)
1,852 (21.0)
368 (4.2)
6,533 (74.0)
To a small extent
,771 (8.7)
,512 (5.8)
,419 (4.7)
69 (0.8)
1,771 (20.1)
Moderately
,223 (2.5)
,138 (1.6)
,45 (0.5)
8 (0.1)
414 (4.7)
,68 (0.8)
,28 (0.3)
,4 (0.1)
5 (0.1)
,105 (1.2)
2,828 (32.0)
3,225 (36.6)
450 (5.1)
8,823 (100)
To a large extent
Totals
2,320 (26.3)
Experience-Sampling Ratings of Experienced
Emotion
At each occasion of measurement, participants were asked
to indicate on a 4-point Likert scale (1 = not at all, 4 = to
a large extent) the extent to which each of 12 emotion-related adjectives (including happy and sad) described their
current emotional state as quickly and accurately as possible by touching appropriate answers on the screen of the
palm-top computer. Affect terms were presented in the
same order at each trial. Both ratings of experience and
latencies to make the ratings were recorded. (The questionnaire included several other questions that are not relevant
to the current study.)
Across all participants there were 10,667 measurement
trials. For various reasons (including technical), the number of measurement trials per participant varied from 49
(the first part of the database was lost due to a palm-top
computer crash on the fifth day of the experiment) to 99
trials, with an average of 97 measurement trials per participant. The majority of participants (81.2%) had 98 trials.
The response rate was within the normal range for such
experience-sampling studies. Across all participants, the
number of reports was 8,835 (82.8%) of 10,667 possible.
The average response rate was very similar for the two
groups of participants (83.0% and 82.7% for elderly people
and students, respectively). The number of usable trials per
participant ranged from 37 (of 49 possible) to 95 (of 98
possible) (M = 80.3, SD = 10.60) (see also Kööts et al.,
2011).
Results
Momentary Ratings of Happiness and
Sadness
Across all participants, 8,823 measurement trials contained
momentary ratings of happiness and sadness. A frequency
count of happy and sad reports entered by all participants
across all measurement trials is shown in Table 1. In over
two-thirds (68%) of the measurement trials, participants reported at least some level of happiness (i.e., to a small extent, moderately, or to a large extent), while the experience
of sadness was reported only in about a quarter (26.0%) of
© 2012 Hogrefe Publishing
Figure 1. The ordered distribution of individual Pearson
product-moment correlations as a function of cumulative
proportion of participants.
measurement trials. In one-fifth (20%) of all measurement
trials, participants reported feeling neither happiness nor
sadness. Contrary to the strict bipolarity hypothesis, participants experienced mixed feelings in a considerable number of measurement trials: In 14% of all measurement trials, participants reported experiencing both happiness and
sadness to a certain extent. If they had experienced at least
one of the two emotions, the probability that they had also
experienced the other one was .174.
The Pearson product moment correlation between momentary ratings of sadness and happiness was r = –.17 (p
< .001). A cumulative distribution of within-person correlations of momentary ratings of happiness and sadness (the
number of measurement trials ranged from 37 to 95, with
a median of 83) is shown in Figure 1. The distribution started with a minimal value of –.73 and ended with a maximal
positive value equal to .35. The mean correlation was r =
–.23 (SD = .209). Although more than 80% of the participants had a negative correlation between happy and sad
ratings, approximately one-fifth still had a positive correlation between their momentary ratings of experiencing
happiness and sadness.
When happiness and sadness ratings across all measurement trials of the same participant were averaged, a summary score of happy and sad ratings resulted. Unlike the
Journal of Individual Differences 2012; Vol. 33(1):43–53
48
L. Kööts et al.: Happiness and Sadness
.51 for ratings of “happy” and “sad,” respectively, p =
.001), these two scores were averaged for subsequent analyses. The averaged frequency counts are shown in Table 2.
Neither polychoric (r = –.07) nor Pearson product moment
correlations (r = –.06) of the retrospective ratings of happiness and sadness were significant. As with averaged momentary ratings, it was impossible to predict the experience of
sadness from the ratings of happiness and vice versa. As expected, the retrospective (PANAS) and averaged momentary
ratings of happiness and sadness were significantly correlated: r = .52 and .60, respectively (p < .001).
Multilevel Analysis
Figure 2. Bivariate distribution of the averaged estimations
of happiness and sadness across all measurement trials for
110 participants.
case of the momentary estimations, the correlation between
the averaged ratings of happiness and sadness across all
measurement trials was positive, though statistically insignificant (r = .13, p = .166). Figure 2 shows a bivariate distribution of averaged ratings of happiness and sadness. A
visual inspection confirms the result of correlational analysis: The average ratings of happiness and sadness across
all measurement trials are independent of one another.
Retrospective Ratings of Happiness and
Sadness
The averaged momentary ratings were quite similar to the
retrospective evaluations (“During the last couple of weeks
I have felt . . .”) of one’s emotional experiences before and
after the experimental session using the PANAS scales. Because the retrospective ratings before and after the experimental session were significantly correlated (r = .54 and
Next, we used multilevel modeling to estimate both between-subject and within-subject correlations of happiness
and sadness ratings, more specifically the hierarchical linear modeling technique (HLM 6.02; Raudenbush & Bryk,
2002). Multilevel analyses confirmed the above reported
results: At the level of single measurement trials (the occasion level), ratings of happy and sad were moderately negatively correlated (r = –.32, p < .001), whereas at the level
of individuals, the negative correlation turns positive (r =
.13, p = .123). The simplest multilevel model (without any
predictors at the Level 1 or at Level 2) revealed that the
variation within individuals dominated over the variation
between individuals – the interindividual variance accounted for 21.3% and 24.3% of the total variance for happy and
sad momentary ratings, respectively. To determine whether
personality traits and their facets predict the relationship
between overlapping happiness and sadness, we conducted
another series of multilevel analyses. To that end we created a binomial (Bernoulli) outcome variable (1 if happy and
sad are mixed; 0 if exclusive): There were no predictors at
Level 1 (the occasion-level); personality traits and facets
were added to the models separately at Level 2 (the person-level) grand mean centered. The simplest Level 1 and
Level 2 models were as follows:
Level 1 model:
Level 2 model:
Prob(Happy-Sad = 1|β) = ϕ,
Log[ϕ/(1 – ϕ)] = η
η = β0
β0 = γ00 + γ01 (Predictor) + r0
Table 2. Averaged frequency (%) of the twice-given retrospective ratings (“During the past couple of weeks I have been
. . .”) of “Happiness” and “Sadness”
Happy
Sad
Not at all
Not at all
2 (0.9)
To a small extent
8 (3.7)
29 (13.4)
11 (5.1)
50 (23.0)
To a small extent
3 (1.4)
17 (7.8)
41 (18.9)
22 (10.1)
83 (38.2)
Moderately
1 (0.5)
18 (8.3)
27 (12.4)
17 (7.8)
63 (29.0)
To a large extent
0 (0.0)
9 (4.1)
Totals
6 (2.8)
52 (24.0)
Note. N = 109 and N = 108 for the first and second ratings, respectively.
Journal of Individual Differences 2012; Vol. 33(1):43–53
Moderately
To a large extent
Totals
8 (3.7)
4 (1.8)
21 (9.7)
105 (48.4)
54 (24.9)
217 (100)
© 2012 Hogrefe Publishing
L. Kööts et al.: Happiness and Sadness
49
Table 3. Theoretical and observed patterns of crossclassification of “Happy” and “Sad” ratings using configural frequency
analysis (CFA)
Happy
(a)
(b)
(c)
Sad
0
1
2
3
0
1
2
3
0
T
T
T
T
A
T
T
T
1
T
A
A
A
T
A
A
2
T
A
A
A
T
A
0
1
2
3
A
3
T
A
A
A
T
A
Notes. (a) Theoretical prediction from bipolarity assumption. (b) The observed momentary ratings. (c) The observed retrospective ratings. T =
CFA Type; A = CFA Antitype. Empty cells do not differ from frequencies expected by chance.
Table 4. The relationship of happiness and sadness predicted by main personality traits and facets: results of
multilevel logistic models
Level 2 predictorsa
Coefficient (β0j)
Odds ratio (95% CI)
**
–1.819
0.162 (0.120, 0.219)
Neuroticism
0.012*
1.012 (1.003, 1.020)
Extraversion
0.015*
1.015 (1.005, 1.024)
Openness to Experience
0.018*
1.018 (1.006, 1.030)
Intercept
Agreeableness
ns
0.991 (0.977, 1.005)
Conscientiousness
–0.013*
0.988 (0.977, 0.997)
Personality facetsb
Intercept
–1.876**
0.153 (0.113, 0.207)
N1: Anxiety
–0.046 (p = .056)
0.955 (0.911, 1.001)
N3: Depression
.066*
1.069 (1.003, 1.139)
N5: Impulsiveness
.067*
1.069 (1.000, 1.144)
E5: Excitement seeking
.059 (p = .059)
1.061 (0.998, 1.129)
O4: Openness to actions
.079*
1.082 (1.006, 1.164)
C3: Dutifulness
–.103*
0.903 (0.840, 0.970)
**
*
Notes. p < .01, p < .05; CI = confidence interval. aLevel 2 predictors
were added grand mean centered. bOnly statistically significant (p <
.05) coefficients of personality facets are presented here.
Table 5. The relationship of happiness and sadness predicted by age, gender and retrospective affect: Results
of multilevel logistic models
Level 2 predictora
Coefficient (β0j)
Odds ratio (95% CI)
Intercept
–1.839**
0.159 (0.113, 0.223)
Age group
Gender
0.734 (p = .081)
ns
2.083 (0.912, 4.756)
0.753 (0.272, 2.081)
Retrospective Emotional
States (PANAS-X)
General NA
0.098**
1.103 (1.062, 1.145)
General PA
0.057*
1.058 (1.011, 1.108)
Sadness
0.171**
1.187 (1.101, 1.280)
1.145 (1.072, 1.222)
Joviality
0.135**
**
*
Notes. p < .001, p < .05; CI = confidence interval. aLevel 2 predictors
were added grand mean centered. NA = Negative Affect; PA = Positive Affect.
© 2012 Hogrefe Publishing
Here, ϕ is the probability of mixed feelings, i.e., feeling
happy and sad at the same time; η is the log of the odds of
mixed feelings. Therefore, β0 is the subjects’ log-odds of
feeling happy and sad at the same time. Next, γ00 is the
average log-odds of mixed happiness-sadness across subjects, while γ01 is the effect of the specific Level-2 predictor
(personality traits and facets, retrospective emotions, age,
and sex) on β0. Finally, r0 represents the error of β0.
The results show that coefficients were statistically significant for all the Big Five traits, except for Agreeableness. The regression coefficient of Conscientiousness was
negative (β = –.013, p < .05), whereas the coefficients of
Neuroticism, Extraversion, and Openness were positive
(.012, .015, and .018, respectively; p < .05). Simply put,
people who score higher in Neuroticism, Extraversion, and
Openness to Experience and lower in Conscientiousness
are more likely to feel happy and sad simultaneously. A
closer look at the facet scales revealed that Depression
(N3), Impulsiveness (N5), Excitement Seeking (E5), and
Openness to Actions (O4) were positively related to feeling
mixed emotions, whereas only Anxiety (N1) and Dutifulness (C3) had a negative relationship (see Table 4).
Sex did not significantly affect the probability of mixed
happiness-sadness feelings, whereas age group (nearly significantly) did. Results show that there is a marginally
greater probability of experiencing happiness and sadness
simultaneously for younger than for the elderly people (β
= 0.734, p = .081). The probability of mixed emotions was
also greater for participants, who reported to have experienced more sadness (β = 0.171, p < .001) during the 2-week
period of the experiment (see Table 5).
CFA
According to the bipolarity assumption – if people experience some level of happiness they cannot experience any
level of sadness – there should be an L-shaped pattern in
Tables 1 and 2. Although participants reported feeling both
happy and sad at the same time in a considerable percentage (14%) of measurement trials, it is still possible that the
co-occurrence of positive and negative affect is smaller
than expected by chance. If happy and sad ratings are used
Journal of Individual Differences 2012; Vol. 33(1):43–53
50
L. Kööts et al.: Happiness and Sadness
in a mutually exclusive way, then we could expect an Lshape configuration as shown in Table 3, part (a). It is expected that the cells in the first row and the first column are
all CFA types, that is, their frequency is higher than expected by chance. The rest of the cells are predicted to belong
to the CFA antitype because their expected frequency in an
ideal case is zero, or at least less than expected by chance.
We used a first-order CFA, which considers all main
effects when estimating the expected cell frequencies
(CFA2000; von Eye, 2001). Table 3 (part b) shows that,
except for one cell, the observed pattern bore a resemblance to the theoretically expected L-shape. Surprisingly, the number of measurement trials in which respondents reported feeling neither happiness nor sadness was
less than that predicted from the assumption of their independence (the relative odd-ratio .843, p < .001). However, there were even fewer cases of happiness absent
when sadness is present (12%) than when sadness is absent (20%).
According to the Lehmacher’s test with Küchenhoff’s
continuity correction (von Eye, 1990, the global χ² =
353.2 (df = 9; p < .001), momentary ratings of happiness
and sadness were clearly not independent. Thus, although
there was no strict bipolarity between happy and sad ratings there was a tendency to use them exclusively.
As opposed to momentary ratings, retrospective ratings of happiness and sadness did not reveal any specific
configuration. The global fit was good, χ² = 9.30 (df = 9;
p = .410), indicating that ratings of sadness were independent of ratings of happiness, and vice versa. Thus, unlike the case of momentary ratings, a person’s degree of
happiness during the past 2 weeks did not predict their
degree of sadness during the same period of time, and
vice versa.
Discussion
Two Estonian words, rõõmus (“happy”) and kurb (“sad”),
are among the most prototypical linguistic antonyms in the
language. In a linguistic task asking to name opposites they
almost invariably provoke each other (Vainik, 2002). Nevertheless, they were not polar opposites when individuals
rated their momentary affective states during their everyday routines. On a considerable number of occasions, participants reported mixed feelings, i.e., they were, at least to
some extent, both happy and sad at the same time. These
mixed feelings of happiness and sadness represented participants’ momentary real life experiences – not their retrospective reports about what happened hours or days before, collected under laboratory conditions. In this respect,
this study clearly goes beyond that of Larsen and colleagues (2001), who showed that a mixture of happy and
sad is unlikely to be observed in normal, everyday conditions, though it can be provoked in more emotionally intricate situations.
Journal of Individual Differences 2012; Vol. 33(1):43–53
The reports of a considerable number of mixed feelings repeat several recent findings that have shown that
even strict linguistic antonyms (i.e., word pairs that are
opposite in meaning) are relatively independent when
they are used to rate affective states (Hunter, Schellenberg, & Schimmack, 2008; Rafaeli & Revelle, 2006;
Schimmack, 2001, 2009; Schimmack & Colcombe,
2007; Tellegen et al., 1999; Vansteelandt et al., 2005). We
found relatively modest correlations between momentary
ratings of happiness and sadness. The average or median
correlation between happy and sad ratings was close to
–.20, which suggested that feeling happy does not necessarily imply the absence of feeling sad at the same time.
Hierarchical multilevel modeling also confirmed that
within-subject correlations between momentary ratings
of happiness and sadness were relatively modest (–.32),
but that they became even slightly positive (.13) in between-subject comparison. In a comparable study of
Flemish students, for example, Vansteelandt et al. (2005)
found that the within-subject correlation between ratings
of sadness and joy dropped from –.32 to –.12 when between-subject correlations were estimated. These low
values do not lend support to the view of happiness and
sadness ratings are separated by 180 ° when they are portrayed in a two-dimensional affective space – even if
skewness and systematic measurement biases are taken
into account. It is more realistic to assume that happiness
and sadness ratings are not bipolar opposites, but rather
separated by 120 ° in a hypothetical two-dimensional affective space (Rafaeli & Revelle, 2006). Together with
several other recent studies (Rafaeli & Revelle, 2006;
Schimmack, 2001; Vansteelandt et al., 2005) the results
of this investigation question the view that, at the level
of such specific items as happy and sad, bipolarity is not
disputed (see Russell & Carroll, 1999). A pair of words
clearly opposite in meaning turn into two – still related
but clearly separable – attributes when they are used to
rate momentary affective state.
In line with the modest negative correlation between
momentary ratings of happiness and sadness, a CFA revealed the tendency to use these ratings in a way that one
impedes the other, at least to a certain extent. In all cells
of the contingency table where one of two emotions was
present and the other absent, the observed frequency was
higher than expected by chance. On the other hand, many
cells reflecting co-occurrence of happy and sad emotions
had a lower frequency than expected by chance. These
results can be explained by the inhibitory model of Diener and Iran-Nejad (1986), which states that happiness
and sadness inhibit each other, and this inhibition increases with the intensities of the two emotions. More
specifically, participants quite rarely experienced simultaneously both great happiness and great sadness (0.1%
of the measurement trials), but low levels of one emotion
was more compatible with any level of the other emotion.
One interesting result was the classification of the zerozero-cell as CFA antitype: In 20% of all cases the partic© 2012 Hogrefe Publishing
L. Kööts et al.: Happiness and Sadness
ipants reported that they felt neither happy nor sad. Because the expected frequency was slightly higher
(23.8%), it indicates a small but systematic tendency to
avoid the simultaneous absence of the two emotions. Albeit small, this tendency seems to indicate that, in everyday life, people do not follow the Stoics’ principle according to which people should strive to be free of all
passions (Nussbaum, 1994). Instead of ataraxia, an untroubled state of mental and emotional calmness, our respondents tended to be more likely to experience either
happiness or sadness than the absence of both.
However, any exclusion of happiness and sadness ratings disappeared when averaged momentary or retrospective ratings were considered. A good agreement between averaged momentary and retrospective ratings indicated that retrospective ratings of emotion contained
accurate information about momentary emotion reports.
This conclusion is in line with previous studies in which
retrospective ratings were compared with momentary
emotion reports (Barrett, 1997). However, agreement between momentary and retrospective reports is rather the
exception than the rule. For example, there is a relatively
poor correspondence between momentary and retrospective reports of coping. Some reports of momentary coping are not reported retrospectively, and some coping reported retrospectively was not reported at the time the
stressor was occurring (Stone et al., 1998). Similarly, momentary and retrospective reports of fatigue capture different information (Banthia et al., 2006). It is possible
that memory processes affect retrospective reports: Participants do not remember the average experience, but
rather the extremes and variability in everyday experiences (Stone, Schwartz, Broderick, & Shiffman, 2005).
Because individuals are not very efficient at remembering their previous emotional states (Levine, 1997; Levine, Prohaska, Burgess, Rice, & Laulhere, 2001), retrospective reports are at least partially reconstructed based
on current appraisals of events. Further, Aaker, Drolet,
and Griffin (2008) showed with two longitudinal studies
that mixed emotion experiences are more difficult to recall accurately compared to unipolar emotion experiences: Over time, people remember mixed-emotion experiences as being less mixed – a memory decay effect that
does not occur with unipolar emotion experiences (Aaker
et al., 2008).
Returning to momentary ratings, although the majority
of participants rated happy and sad in a mutually exclusive way, there was a sizeable group of individuals who
tended to describe their emotional states as both sad and
happy at the same time. The range of individual differences was considerable – the percentages of individuals
reporting mixed feelings ranged from 0% to 81%. Therefore, it would be misleading to conclude that feelings of
happiness and sadness are generally polar opposites of a
single dimension. Some people indeed cannot be happy
when they are sad (13.6% of all participants never reported mixed feelings over the 2 weeks of our experiment),
© 2012 Hogrefe Publishing
51
but there are many others who are more tolerant toward
ambiguity in their emotional state and report being both
moderately happy and sad at the same time (see Rafaeli
et al., 2007).
Can these individual differences be related to personality traits? Indeed, as shown by the results of hierarchical modeling, mixed emotions are likely to be felt by people who score lower in Conscientiousness and higher in
Neuroticism, Extraversion, and Openness to Experience.
In other words, people who report both happy and sad at
the same time are more likely to act on cravings, who
need environmental stimulation, who tend to be open to
new experiences on a practical level, who do not find it
too important to fulfill moral obligations, and have lower
levels of floating anxiety. It is possible that these characteristics help people experience and tolerate ambiguous
feelings. However, personality is not the only source of
interindividual differences – momentary mixed feelings
are also related to younger age and remembered negative
and positive emotions.
In general, the results reported in this study support
the view that the relationship between positive and negative affect is not invariably organized into a bipolar or
independent bivariate structure; rather, the structure of
affective response itself is a variable influenced by the
mode and level of evaluation (Cacioppo & Berntson,
1994) as well as by the basic personality traits. We know
that the relationship between the same variables may
even have different signs at different levels. We also
learned that, while momentary ratings of positive and
negative affect tend to be negatively correlated, retrospective ratings appear to be almost independent or even
slightly positively correlated. No doubt there are many
other conditions controlling the polarity/separability of
positive and negative affect, including different measurement instruments, cognitive style, and crosscultural differences. The most promising research program appears
to be one that determines regularities when affective
states can be considered as polar opposites, when they
are separable, or when they are completely independent.
Acknowledgments
This project was supported by a grant from the Estonian
Science Foundation (5384) to Anu Realo and by grants
from the Estonian Ministry of Education and Science
(SF0182585s03 and SF0180029s08), and from the Research Council of the University of Leuven to Jüri Allik.
The writing of this article was supported by a Primus grant
(3–8.2/60) from the European Social Fund to Anu Realo.
We are grateful to Talvi Kallasmaa, Kätlin Konstabel,
Gerli Laine, and Tiit Mogom for their input during the
early stages of this project. The authors also thank Iven
Van Mechelen and Delaney Michael Skerrett for their
helpful comments on earlier drafts of this article.
Journal of Individual Differences 2012; Vol. 33(1):43–53
52
L. Kööts et al.: Happiness and Sadness
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Accepted for publication: June 14, 2011
Liisi Kööts
Department of Psychology
University of Tartu
Tiigi 78
Tartu 50410
Estonia
Tel. +37 27 375-902
Fax +37 27 376-152
E-mail [email protected]
Journal of Individual Differences 2012; Vol. 33(1):43–53