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Running head: DAILY CONVERSATIONS AND LIFE SATISFACTION: A REPLICA-
TION
In press, Psychological Science
This manuscript may differ slightly from the final published version.
“Eavesdropping on Happiness” Revisited: A Pooled, Multi-Sample Replication of the Asso-
ciation between Life Satisfaction and Observed Daily Conversation Quantity and Quality
Anne Milek1
Emily A. Butler1
Allison M. Tackman1
Deanna M. Kaplan1
Charles L. Raison2
David A. Sbarra1
Simine Vazire3
Matthias R. Mehl1
1University of Arizona
2University of Wisconsin - Madison
3 University of California - Davis
Correspondence concerning this article should be addressed to: Anne Milek,
amilek@email.arizona.edu or Matthias Mehl, mehl@email.arizona.edu.
2
Abstract
The present study aimed to replicate and extend findings by Mehl, Vazire, Holleran and
Clark (2010) that individuals with higher well-being tend to spend less time alone and more
time interacting with others (e.g., greater conversation quantity), and engage in less small talk
and more substantive conversations (e.g., greater conversation quality). To test the robustness
of these effects in a larger and more diverse sample, we used Bayesian integrative data analy-
sis to pool data on subjective life satisfaction and observed daily conversations from three
heterogeneous adult samples, in addition to the original sample (N = 486). We found moder-
ate associations between life satisfaction and amount of alone time, conversation time, and
substantive conversations, but no reliable association with small talk. Personality did not sub-
stantially moderate these associations. The failure to replicate the original small talk effect is
theoretically and practically important as it has garnered considerable scientific and lay inter-
est.
Keywords: Bayesian statistics, happiness, naturalistic observation, well-being, replica-
tion
Words = 150
3
“Eavesdropping on Happiness” Revisited: A Pooled, Multi-Sample Replication of the
Association between Life Satisfaction and Observed Daily Conversation Quantity and
Quality
It is now a well established and consensually acknowledged fact that social relationships
are key to well-being (Argyle, 2001). What is much less clear, however, and what remains a
source of considerable debate, is whether it is primarily the quantity or the quality of our so-
cial encounters that matters. Several gold-standard experience sampling studies show a linear
relationship between how much time people spend interacting with others and how happy
they tend to be (Lucas & Dyrenforth, 2006). However, others argue that the quality of every-
day social encounters may be more important than the frequency with which people engage in
social contact (e.g., Deci & Ryan, 2000; Myers, 1999).
In a recent study, Mehl, Vazire, Holleran and Clark (2010) used the Electronically Acti-
vated Recorder (EAR; Mehl, 2017) to investigate how well-being relates to observational in-
dicators of real-world conversation quantity and quality. Participants wore the EAR for four
days while the device intermittently and unobtrusively recorded snippets of ambient sounds as
they went about their days. Two measures of interaction quantity (time alone and time in con-
versation) and quality (amount of small talk and amount of substantive conversations) were
derived from the sampled ambient sounds and linked to well-being. Further strengthening the
evidence for the association with quantity, happier participants spent objectively less time
alone and more time talking with others. The analyses also provided evidence in favor of
quality: happier participants engaged in more substantive conversations and had a lower ratio
of daily small talk accounting for conversation quantity. Importantly, the effects were robust
in the sense that they held for weekday and weekend conversations and across multiple
measures of well-being.
However, with 79 participants, the sample size, though sizable for labor-intensive natu-
ralistic observation research, was ultimately modest, and by current standards too small to
4
yield effects that can be expected to replicate (Maxwell, Kelley, & Rausch, 2008; Schönbrodt
& Perugini, 2013).
Beyond statistical power, the generalizability of the sample was also limited in consist-
ing only of college students. Although undergraduate samples are common and valuable in
psychology, it is empirically and theoretically important to test whether effects found in a
sample that is relatively homogeneous with respect to important social context variables ex-
tend to other populations (Peterson, 2001). For example, undergraduate students are in an en-
vironment (i.e., college) that maximally affords getting to know new people and in a life stage
where establishing new friendships is normative, relative to middle adulthood, where family
and job demands might render the formation of new relationships more difficult and less im-
portant for social integration (Havighurst, 1981). In addition, later life stages often include
critical life events (e.g., personal illness; trauma or death of a close friend or loved one),
which can affect the habitual equilibrium between conversation quantity and quality.
Hence, the question of whether one’s well-being is first and foremost a function of the
quantity of one’s daily conversations or rather a function of their quality remains unanswered
in important ways. Well-powered replication studies using age- and context-heterogeneous
samples are needed to establish the robustness and generalizability of effect parameters in sin-
gle studies (Asendorpf u. a., 2013). In addition, researchers are increasingly calling for
changes in analytical strategies and recommend estimating posterior model probabilities (i.e.,
a Bayesian approach) in addition to (or instead of) standard frequentist parameters (Carlsson,
Schimmack, Williams, & Bürkner, 2017; Johnson, Payne, Wang, Asher, & Mandal, 2016;
Marsman u. a., 2017). The Bayesian analytical approach seems particularly well suited for
replication studies as it “provides a more flexible modeling framework, allows more appropri-
ate quantification of the uncertainty around effect estimates” (Pitchforth & Mengersen, 2012,
S. 118), and permits the integration of prior information.
5
Finally, Mehl and colleagues (2010) took into consideration that participants’ personali-
ties might explain differences in well-being (cf. DeNeve & Cooper, 1998; Lucas & Fujita,
2000) by controlling for the Big Five personality domains in their analyses. However, as re-
search shows, participants’ personalities exert organizational forces on how individuals act in,
and interact with, their social environments (Mehl, Gosling, & Pennebaker, 2006). For exam-
ple, studies suggests that extraverts might benefit more from social interactions than introverts
(e.g., Harris, English, Harms, Gross, & Jackson, 2017). Hence, personality might also moder-
ate the link between well-being and conversation quantity and quality, which was not tested in
the original study due to limited statistical power.
Therefore, in the present study, we sought to replicate and extend the main findings re-
ported by Mehl and colleagues (2010) and tested whether associations between life satisfac-
tion, as a key component of subjective well-being, and conversation quantity and quality gen-
eralize beyond a student sample. To obtain robust overall effect estimates, we used Bayesian
integrative data analytic methods by pooling life satisfaction and EAR-observed daily conver-
sation information from three large and diverse samples of working adults in addition to the
original data (total N = 486). For daily conversation quantity, we expected a negative associa-
tion between life satisfaction and spending time alone, as well as a positive association with
talking with others. For conversation quality, we expected a negative association between life
satisfaction and having small talk, as well as a positive association with substantive conversa-
tions. Given the known link between personality and life satisfaction, replicating the explora-
tory analyses conducted in Mehl and colleagues (2010), we examined whether the associa-
tions between life satisfaction and the conversation variables hold even after controlling for
personality. In addition, we tested whether the associations between life satisfaction and daily
conversation patterns were moderated by personality but without proposing specific hypothe-
ses (including all Big Five dimensions for the sake of empirical completeness).
Method
6
Participants and procedures
We report how we determined our sample size, all data exclusions, and all available
happiness and conversation measures in the study. Information about the study samples and
measures is summarized in Table 1 and 2.
Study 1. This sample constituted the original sample reported in Mehl and colleagues
(2010). Eighty undergraduate students recruited primarily from psychology introductory
courses at the University of Texas at Austin completed a series of questionnaires and wore the
EAR for four days. One participant failed to provide valid EAR data and was excluded from
all analyses.
Study 2a and b. As part of a larger study, breast cancer patients and their cohabitating
partners were recruited from the Arizona Cancer Center (University of Arizona, Tucson) dur-
ing regular visits to an oncologist. Couples were eligible if the female partner had a primary
diagnosis of Stage I, II, or III breast cancer; they were living together in a marriage-like rela-
tionship, and spoke primarily English in their daily conversations. Fifty-six couples gave their
consent to participate in the study. Fifty patients (Study 2a) and 51 caregiving partners (Study
2b) provided usable data for the current analyses. Both partners completed a series of ques-
tionnaires and wore an EAR device for three consecutive days from Friday afternoon to Mon-
day morning (for more details see Robbins, López, Weihs, & Mehl, 2014).
Study 3. As part of a larger study, 261 medically healthy adults living in Atlanta, GA
were recruited by the Emory University Center for Health and Well-being to participate in a
randomized controlled trial of a meditation intervention (ClinicalTrials Identifier:
NCT01643369). One-hundred-eighty-four participants provided usable data for the current
analyses. They completed a series of questionnaires and wore the EAR for three days (Friday
night through Monday morning) before and after an eight-week meditation intervention (for
more details see Kaplan u. a., 2017).
7
Study 4. As part of a larger study about coping with divorce, 133 adults recently sepa-
rated from their martial partners were recruited from the larger Tucson, Arizona area. One-
hundred-twenty-two participants provided usable data for the current analyses by completing
sets of questionnaires at time 1 (initial time of assessment), at time 2 (3-months follow-up),
and at time 3 (5 months-follow-up). At each time of assessment they also wore the EAR for
three consecutive days (typically, Friday to Monday) (for more details see Hasselmo u. a., in
press).
Measures
Life satisfaction. All participants completed the Satisfaction with Life Scale (SWLS;
Diener, Emmons, Larsen, & Griffin, 1985), a 5-item instrument measuring global evaluations
of life satisfaction (response scale from 1 [strongly disagree] to 7 [strongly agree]). Internal
consistency was high in all studies (α ranged from .84 and .93).
Personality. All participants completed the Big Five Inventory (BFI; John, Donahue, &
Kentle, 1991), a 44-item instrument assessing personality at the global level of the Big Five
personality dimensions (response scale from 1 [strongly disagree] to 7 [strongly agree]). Inter-
nal consistencies for Extraversion, Agreeableness, Conscientiousness, Neuroticism, and
Openness to Experience were satisfactory in all studies (α ranged from .69 and .86).
Observational measures. To obtain an estimate of the objective frequency of partici-
pants’ daily conversations, participants wore the EAR from the time they woke up in the
morning until they went to bed at night. The EAR is a digital audio recorder that unobtru-
sively samples daily behavior by intermittently recording snippets of ambient sounds. It cap-
tures between 5-10% of participants’ waking hours without them knowing exactly when it is
recording. The EAR has been successfully used, with good acceptance and adherence, in vari-
ous samples that are diverse with respect to age, gender, ethnicity, and location (Mehl & Hol-
leran, 2007). Established privacy protection and data confidentiality guidelines (Mehl, 2017;
Robbins, in press) were followed in all studies. Specifically, it was ensured that participants
8
had an opportunity to review their recordings and delete sound files they preferred to remain
private. All participants received financial compensation at the end of the study.
The EAR sampling rates varied across studies (30 or 50 s recordings every 9 or 12.5
min). For each recording, two trained research assistants (except in Study 1) independently
coded whether, in any given sound file, the participant was alone or talking with others (con-
versation quantity), and whether a captured conversation was small talk or a substantive con-
versation (conversation quality). We used the “Conversational Purpose” coding system
(adapted from the original study). Codings were mutually exclusive but non-exhaustive; that
is, for some conversations neither category applied (e.g., conversations could also be coded as
practical conversations, personal/emotional disclosure, or gossip; for more detail see Table S1
in Supplemental Material or the OSF EAR Repository, https://osf.io/74x3c/). Small talk was
defined as an uninvolved, banal conversation, in which only trivial information was ex-
changed (e.g., “I stepped on something”, “What are you up to?”). Substantive conversations
were defined as involved conversations in which meaningful information was exchanged
(e.g., “There are a lot of high stress A-type personalities there”, “They have already raised ten
million dollars for Haiti”). As the data were coded by only one coder in Study 1, inter-coder
reliabilities were computed based on a set of training sound files that all research assistants
coded and ICC [2,k] reliabilities were computed at the sound file level. For all other studies,
intra-class correlations were computed across the two coders on the aggregated, average
sound file level (ICC[1,2]). ICCs were satisfactory for all categories ranging from .67 to .98
(Table 1), except for small talk in Study 4 (ICC[1,2]=.36). For each participant, we converted
the EAR codes into relative frequencies (i.e., percentage of valid waking recordings in which
a category applied). Because Study 3 was an intervention with implications for conversation
behavior, we included only the pre-intervention data; because Study 4 was an observational
study, we averaged across the three assessments to obtain maximally reliable estimates of par-
9
ticipants’ (general) life satisfaction and conversation patterns. To account for individual dif-
ferences in the amount of daily conversations, we also computed the percentage of conversa-
tions that were small talk or substantive (labeled as normalized).
Statistical analysis
Following recommendations for the “new statistics” (Cumming, 2014; Kruschke & Lid-
dell, 2017), we opted against a hypothesis testing approach (e.g., interpreting p-values or
Bayes factors) in favor of an effect size estimation approach. The Bayesian estimation frame-
work appeared particularly suited for a replication analysis, as it allows incorporating prior
information and appropriately quantifies the uncertainty around effect estimates. Data, R-code
and supplementary analyses are available on the Open Science Framework at osf.io/hp2wx.
First, we conducted a series of Bayesian linear regression analyses (5 chains, 10,000 it-
erations, burn-in period of 1000 steps) separately for each study and for each predictor using
the brms R-package (Buerkner, 2017). Visual examination of the chain trajectories suggested
converging results. To facilitate interpretation and to be compatible with the analyses in the
original publication, posterior Bayesian point estimates of the beta weights as well as the cor-
responding credibility intervals were transformed into a correlation metric. The outcome
measure was normally distributed and we therefore used a standard Gaussian regression
model. We ran the models using the parameter estimates from Study 1 as priors for Study 2 to
Study 4. However, the parameter estimates from the Mehl et al. (2010) study may have been
biased due to the small sample size. In the absence of an established literature to guide our
choice of priors, we reran the models using non-informative flat priors (the brms default) for
all estimated parameters. The posterior distributions were relatively robust to changes in the
specification of the prior distributions. We therefore only report the set of analyses with flat
priors (supplementary analyses including Study 1 priors are provided at osf.io/hp2wx).
10
Table 1. Sample Characteristics, Descriptive Statistics and Reliabilities of Study Measures
Sample characteristics
Study 1
Study 2a
Study 2b
Study 3
Study 4
Population Undergraduate
students Cancer
patients Spouses of
cancer patients Healthy working adults
in a meditation trial Recently divorced/
separated adults
N
79
50
51
184 c
122 c
% Female
53.2
100
15.7
66.1
71.3
Age; Mean (SD)
18.70 (1.41)
56.36 (14.06)
59.04 (14.77)
33.48 (8.39)
43.80 (10.50)
Ethnicity
66% White,
20% Asian,
11% Hispanic,
3% other
81% White,
12% Hispanic,
4% African American,
4% other/unknown
82% White,
16% Hispanic,
2% Asian
54% White,
31% African American,
7% Asian, 4% Hispanic,
4% other
63% White,
22% Hispanic,
5% African Americans,
3% Asian, 7% other
EAR sound files per partici-
pant; Mean (SD)
300 (104) 172 (59) 180 (53) 161 (54) 394 (118)
EAR sampling rate
30 s every 12.5 min 50 s every 9 min 50 s every 9 min
50 s every 9 min or
30 s every 12.5 min
30 s every 12.5 min
Subjective measuresa
M
(SD)
α
M
(SD)
α
M
(SD)
α
M
(SD)
α
M
(SD)
α
Life satisfaction
4.52
(1.37)
.93
5.68
(1.19)
.84
5.53
(1.23)
.91
5.04
(1.19)
.84
4.01
(1.51)
.91
Extraversion
4.25
(1.23)
.89
4.92
(1.28)
.86
4.45
(1.02)
.80
4.69
(1.07)
.85
4.78
(1.25)
.86
Agreeableness
4.98
(1.00)
.79
5.79
(0.69)
.69
5.28
(0.80)
.71
5.56
(0.81)
.83
5.71
(0.80)
.77
Concienciousness
4.50
(0.95)
.76
5.62
(0.99)
.83
5.65
(0.89)
.80
5.43
(0.87)
.82
5.44
(0.91)
.79
Neuroticism
3.99
(1.16)
.82
3.31
(1.26)
.87
3.11
(0.93)
.76
3.16
(1.02)
.86
3.64
(1.14)
.81
Openness to Experience
5.14
(1.06)
.85
5.08
(1.00)
.84
5.27
(0.87)
.80
5.33
(0.78)
.77
4.94
(0.90)
.78
Observational measures
M
(SD)
ICCd
M
(SD)
ICCe
M
(SD)
ICCe
M
(SD)
ICCe
M
(SD)
ICCe
Spending time alone
.67
(.15)
.97
.37
(.19)
.82
.39
(.22)
.78
.54
(.24)
.95
.58
(.19)
.92
Talking with others
.32
(.14)
.95
.47
(.15)
.97
.44
(.16)
.95
.40
(.18)
.98
.36
(.13)
.98
Small talk
.06
(.06)
.76
.11
(.08)
.81
.10
(.07)
.76
.08
(.06)
.67
.05
(.04)
.36
Small talk (normalized)b
.18
(.15)
.23
(.15)
.23
(.14)
.19
(.11)
.15
(.11)
Substantive conversations
.12
(.10)
.84
.18
(.11)
.81
.16
(.10)
.83
.13
(.08)
.73
.16
(.09)
.80
Substantive conversations
(normalized)b .36 (.25) .36 (.15) .34 (.14) .32 (.14) .43 (.15)
11
Note. a higher values indicate higher life satisfaction, extraversion, etc. (range: 1 to 7); b normalized variables are computed as number of small talk/ substantive
conversations relative to a person’s total number of recorded conversations to account for individual differences in number of conversations; c N differs for the
Big Five measures due to missing values (Study 3: n = 180; Study 4: n = 120); d data were single coded; inter-coder reliabilities were computed based on a set of
training sound files that all coders coded and ICC [2,k] reliabilities were computed at the sound file level; e data were double coded; inter-coder reliabilities were computed
across the two coders on the aggregated, average sound file level (ICC[1,2]) .
12
Table 2. Pearson Correlations among all Study Variables within Each Study
Study 1 (N = 79)
Study 2a (N = 50)
Study 2b (N = 51)
Study 3 (N = 184)
Study 4 (N = 122)
2
3
4
5
6
7
2
3
4
5
6
7
2
3
4
5
6
7
2
3
4
5
6
7
2
3
4
5
6
7
1 Life satisfaction
-.36
.32
-.03
-.25
.26
.20
.14
.17
.16
-.23
.21
.16
.13
-.06
-.12
-.14
-.05
.06
-.19
.16
.12
.01
.22
.16
-.21
.33
.15
.02
.29
.11
2 Spending time alone
-.91
-.38
.09
-.57
-.17
-.52
-.05
.17
-.30
-.02
-.66
-.27
.11
-.40
-.06
-.83
-.66
-.23
-.54
.06
-.82
-.35
-.02
-.64
-.15
3 Talking with others
.45
-.04
.56
.07
.29
-.12
.69
.20
.35
-.22
.75
.29
.65
.08
.74
.06
.41
-.01
.76
.12
4 Small talk
.78
-.06
-.30
.90
-.14
-.41
.79
-.10
-.36
.73
.31
-.19
.85
-.05
-.47
5 Small talk (normalized)
-.32
-.45
-.44
-.54
-.50
-.56
-.15
-.28
-.35
-.54
6 Substantive conversations
.77
.81
.81
.64
.70
7 Substantive conversations
(normalized)
Note. All correlations in bold are significant at p < .05, without correcting for multiple comparisons.
13
Second, to derive overall estimates for the associations between life satisfaction and the
EAR-derived variables for conversation quantity and quality, we pooled the raw data of all
four samples. Pooling raw data across samples rather than aggregating sample-based sum-
mary statistics has the advantage of increasing variability and, hence, statistical power
(Scheibehenne, Jamil, & Wagenmakers, 2016; see also Curran & Hussong, 2009). However,
when choosing such an approach non-independence needs to be considered as participants are
nested within studies. Failing to account for between-study variability and non-independence
can lead to biased results (e.g., Carlsson u. a., 2017). Conceptually, random-effects models are
the gold standard for this type of data structure. Practically, however, with only four studies1
and in the absence of strong prior knowledge, Bayesian random-effects models failed to relia-
bly converge. Note that our analyses on the pooled data, therefore, assume a fixed effect. To
nevertheless account for between-sample sources of variability, and to formally test whether
associations differed between studies, we included effect codes for study membership and the
corresponding interaction terms (i.e., target predictor by study membership). The Watanabe-
Akaike information criterion (WAIC) and leave-one-out cross validation (LOO; Vehtari, Gel-
man, & Gabry, 2017) were used to compare model fit of different models applied to the same
data on a conventional scale of “deviance” similar to AIC (lower WAICs and LOOs denote
better model fit). Model comparisons revealed that models with and without interaction terms
evidenced in very similar fit, indicating little benefit to allowing effects to vary between stud-
ies. This was true for all six conversation variables. Consequently, we excluded the interac-
tion terms from the final models and only controlled for study differences in average life satis-
faction.
1 As Study 2 consisted of interdependent couple data, we obtained the overall estimate by first including data
only from Study 2a and then replicated all models including data from 2b instead. Results did not differ substan-
tially but for two pooled moderation models; hence, overall estimates from analyses including Study 2a are re-
ported (see Supplemental Material for results including Study 2b instead of Study 2a).
14
These final models then also served as reference to test (a) whether participants’ person-
alities accounted for the associations between life satisfaction and daily conversation quantity
or quality, and (b) whether personality2 moderated these associations. Theoretically, we were
most interested in extraversion as a potential moderator (due to its known association with so-
cial relationships and life satisfaction) but we included all five Big Five dimensions for empir-
ical completeness.
Finally, to complement the Bayesian approach, we also conducted a traditional fre-
quentist random effects meta-analysis (REMA). We separately calculated effect sizes for all
studies and used the Exploratory Software for Confidence Intervals (ESCI; Cumming, 2014)
to obtain the overall maximum likelihood correlation estimates and the corresponding confi-
dence intervals.
Results
Daily conversation quantity
As shown in the upper panel of Figure 1, when considered separately, two out of three
new studies (Figure 1, Study 2 to 4) provided evidence that a negative association between life
satisfaction and spending time alone was among the 95% most credible values. When pooled
across all studies, the overall posterior Bayesian point estimate of the correlation was r = -.19
(CI95 [-.28, -.09]), approximately half the size of the original study. Following the analytic
procedures of the original study, we also tested whether personality differences accounted for
this correlation. When simultaneously controlling for participants’ Big Five scores in the
models, the effect of spending time alone was essentially unaffected (r = -.18, CI95 [-.26, -
.09]). The traditional frequentist random effects meta-analysis produced a mean effect size of
r = -.18, (CI95 [-.34, -.02]).
2 We also considered age and gender as moderators in exploratory analyses (see Supplemental Material), how-
ever, as we did not have specific hypotheses and the associations were not reliably moderated by either of the
two, we did not report the effects in the main manuscript.
15
The sample-specific posterior correlation estimates for well-being and talking with oth-
ers were positive for all but Study 2b (Figure 1, lower panel). When pooled across studies, the
data indicated that life satisfaction was moderately positively associated with talking with oth-
ers, r =.22 (CI95[.13, .31]), less strongly than in the original study. Again, controlling for par-
ticipants’ Big Five scores did not substantially alter the mean posterior correlation estimate (r
= .18, CI95 [.10, .26]) and the frequentist random effects meta-analysis resulted in a similar
mean effect size of r = .24, (CI95 [.14, .34]).
Daily conversation quality
The sample-specific results for the associations of life satisfaction with small talk (Fig-
ure 2) and substantive conversations (Figure 3) demonstrated that posterior Bayesian point es-
timates and credibility intervals varied among the new studies to different degrees for the two
conversation quality variables. Posterior Bayesian correlation estimates for the association be-
tween small talk and life satisfaction ranged from r = -.16 (CI95 [-.44, .12], Study 2a) to r = .15
(CI95 [-.03, .32], Study 4). Similarly, for normalized3 small talk a correlation of zero was
among the 95% most credible correlations for all but the original study. Results for Studies 2
to 4 remained robust even when the posterior distribution from Study 1 was set as a prior for
the regression coefficient. Hence, when collapsed across samples, the most likely posterior
point estimate was r =.05 (CI95 [-.04, .14]) for small talk and r = -.08 (CI95 [-.17, .01]) for nor-
malized small talk. The credibility intervals of the two indicators for small talk contained zero
also after accounting for the Big Five scores (frequency: r =.08, CI95 [-.001, .16]; normalized:
r =-.01, CI95 [-.09, .07]). The traditional random-effects meta-analysis linking life satisfaction
3 Note that the variables small talk and normalized small talk (or substantive conversations and normalized sub-
stantive conversations) are two somewhat different ways of capturing how often a person engages in shallow,
banal conversation (or substantive conversation, respectively). Whereas the small talk variable captures the per-
centage of EAR sound files in which small talk was coded relative to the total number of sound files, the normal-
ized small talk variable represents the proportion of small talk a participant had as a proportion of all conversa-
tions that the EAR captured for this participant.
16
to small talk resulted in a mean correlation estimate r =.06 (CI95 [-.07, .18]) for small talk and
r = -.09 (CI95 [-.23, .05]) for normalized small talk.
In contrast, posterior correlation estimates for substantive conversations were positive
for all but Study 2b. When pooled across studies, the data revealed that life satisfaction was
moderately associated with having more substantive conversations (frequency: r =.23, CI95
[.14, .32]; normalized: r =.15, CI95 [.06, .24]), similar to the original study. The magnitude of
the associations was only slightly diminished when controlling for participants’ Big Five
scores (frequency: r =.18, CI95 [.10, .26]; normalized: r =.10, CI95 [.02, .18]). The frequentist
random-effects meta-analysis linking life satisfaction to substantive conversation yielded ef-
fect sizes numerically similar to the Bayesian results (frequency: r =.25, CI95 [.15, .34]; nor-
malized: r =.15, CI95 [.06, .25]).
Personality as a moderator
Although we refrained from formulating distinct a priori hypotheses about how person-
ality might moderate the observed effects (but, theoretically, were most interested in extraver-
sion as a potential moderator due to its relationship to both participants’ social lives and their
life satisfaction), we tested whether the slope of the relationship between conversation quan-
tity or quality and life satisfaction differed for people who had different personality traits
(e.g., whether introverted people benefitted more or less from small talk than did extraverted
people). The additional moderation analyses revealed that individual differences in personal-
ity did not substantially moderate the strength of the links between life satisfaction and any of
the six conversation variables. Model comparisons using the Watanabe-Akaike information
criterion (WAIC) and the leave-one-out cross validation (LOO; Vehtari u. a., 2017) revealed
that models with interaction terms did not yield better model fit than models without them,
providing no convincing evidence for moderation. The WAICs or LOOs were either a) sub-
stantially smaller in the reference model or b) the estimated difference (ΔLOOIC) of the ex-
pected leave-one-out prediction errors between the moderation model and the reference model
17
included zero in its confidence interval suggesting no difference in model fit between the
models (so the more parsimonious model is to be preferred). In addition, all credibility inter-
vals for the posterior point estimates of the interaction terms contained zero (see Supple-
mental Material, Table S2 and S3). This suggests that, for example, small talk or substantive
conversations seemed not to be appreciably more (or less) associated with life satisfaction for
extraverted than for introverted participants in our sample.
18
PBPE LCI UCI
-.36
-.57
-.15
.14
-.15
.42
.13
-.15
.41
-.19
-.34
-.05
-.21
-.39
-.04
-.19
-.28
-.09
-.18
-.26
-.09
-.18
-.34
-.02
PBPE LCI UCI
.32
.11
.53
.17
-.12
.45
-.06
-.34
.22
.16
.02
.30
.33
.16
.50
.22
.13
.31
.18
.10
.26
.24
.14
.34
Figure 1. Posterior Bayesian Point Estimates for the Associations between Life Satisfaction and Spending
Time Alone and Spending Time Talking with Others.
Note. The posterior Bayesian point estimates (PBPE, in standardized correlation metric) are indicated as dots
or diamonds (size corresponds to sample size) and the 95% credible intervals as horizontal lines (LCI =
lower boundary, UCI = upper boundary). Grand totals include pooled data from Study 1, Study 2a, Study 3
and Study 4 (n = 429). The parallel analysis including data from Study 2b produced similar results. B5resid =
effect controlled for participants’ Big Five personality domain scores. REMA = random effect meta-analy-
sis; *effect represent frequentist maximum likelihood estimate and confidence intervals.
-1.00 -0.50 0.00 0.50 1.00
Posterior effect size (ρ)
Spending time alone
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
-1.00 -0.50 0.00 0.50 1.00
Posterior effect size (ρ)
Talking with others
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
19
PBPE LCI UCI
-.03
-.25
.19
-.16
-.44
.12
-.12
-.41
.16
.12
-.02
.27
.15
-.03
.32
.05
-.04
.14
.08
-.001
.16
.06
-.07
.18
PBPE LCI UCI
-.25
-.47
-.04
-.23
-.51
.05
-.14
-.42
.14
.01
-.14
.15
.02
-.16
.19
-.08
-.17
.01
-.01
-.09
.07
-.09
-.23
.05
Figure 2. Posterior Bayesian Point Estimates for the Association between Life Satisfaction and Small Talk
Note. The posterior Bayesian point estimates (PBPE, in standardized correlation metric) are indicated as dots
or diamonds (size corresponds to sample size) and the 95% credible intervals as horizontal lines (LCI =
lower boundary, UCI = upper boundary). Grand totals include pooled data from Study 1, Study 2a, Study 3
and Study 4 (n = 429). The parallel analysis including data from Study 2b produced similar results. B5resid =
effect controlled for participants’ Big Five personality domain scores. REMA = random effect meta-analy-
sis; *effect represent frequentist maximum likelihood estimate and confidence intervals.
-1.00 -0.50 0.00 0.50 1.00
Posterior effect size (ρ)
Small talk
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
-1.00 -0.50 0.00 0.50 1.00
Posterior effect size (ρ)
Small talk (normalized)
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
20
PBPE LCI UCI
.26
.04
.48
.20
-.07
.48
-.05
-.33
.23
.22
.08
.36
.29
.12
.45
.23
.14
.32
.18
.10
.26
.25
.15
.34
PBPE LCI UCI
.20
-.01
.42
.16
-.12
.44
.06
-.22
.34
.16
.02
.30
.11
-.07
.28
.15
.06
.24
.10
.02
.18
.15
.06
.25
Figure 3. Posterior Bayesian Point Estimates for the Association between Life Satisfaction and Substantive
Conversations
Note. The posterior Bayesian point estimates (PBPE, in standardized correlation metric) are indicated as dots
or diamonds (size corresponds to sample size) and the 95% credible intervals as horizontal lines (LCI =
lower boundary, UCI = upper boundary). Grand totals include pooled data from Study 1, Study 2a, Study 3
and Study 4 (n = 429). The parallel analysis including data from Study 2b produced similar results. B5resid =
effect controlled for participants’ Big Five personality domain scores. REMA = random effect meta-analy-
sis; *effect represent frequentist maximum likelihood estimate and confidence intervals.
-1.00 -0.50 0.00 0.50 1.00
Posterior effect size (ρ)
Substantive conversation
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
-1 -0.5 00.5 1
Posterior effect size (ρ)
Substantive conversation (normalized)
Study 1 (n = 79)
Study 2a (n = 50)
Study 2b (n = 51)
Study 3 (n = 184)
Study 4 (n = 122)
Grand Total - PBPE
Grand Total - PBPE - B5resid
Grand Total - REMA*
21
Discussion
We aimed to replicate and extend previous findings about how conversational properties
of social interactions relate to life satisfaction in a heterogeneous adult sample. For daily con-
versation quantity, we replicated the results reported in Mehl and colleagues (2010) and found
evidence for medium sized associations between life satisfaction and spending time alone
(negative) or talking with others (positive), although the negative association for spending
time alone was smaller than in the original study.
For daily conversation quality, not all effects were replicated. In accordance with the
original findings reported by Mehl and colleagues (2010), we found solid evidence that en-
gaging in substantive conversations was moderately associated with life satisfaction, above
and beyond personality characteristics. Our analyses indicated that participants who reported
higher life satisfaction than one would expect on the basis of their personality had not only
more, but also more substantive, conversations than their less satisfied counterparts with simi-
lar personalities.
In contrast, the medium sized negative association between life satisfaction and the per-
centage of small talk conversations – our second indicator of conversation quality – did not
replicate: Whether people had more or less small talk in daily life was not reliably associated
with life satisfaction. This was true for the overall frequency of small talk as well as the nor-
malized small talk-conversation ratio. The negative correlation suggested by the underpow-
ered original study seems to have been a false positive finding.
The associations between life satisfaction and conversation quantity and substantive
conversation were reliably detected in two out of the three new studies. The findings are par-
ticularly compelling, as there is no method overlap between the observational conversation
codes and the self-report measures of life satisfaction that could explain the links. Mean effect
estimates were somewhat smaller than the ones originally reported. This can be expected even
in well-powered replication studies, and is likely a result of publication bias inflating effect
22
sizes in original studies (Lakens & Etz, 2017). Indeed, given its small sample size and lack of
pre-registered study and analysis plan, the original study might have been capitalizing on
chance, overestimating the true effects. In fact, three out of the four studies – taken individu-
ally – were underpowered. This fact underscores the observation that isolated results from
small studies should be interpreted with caution, and that pooling multiple smaller studies (as-
suming the absence of a selection bias for inclusion) can be an effective way of increasing
power and more reliably estimating population effects (cf. Curran & Hussong, 2009).
The current study further illustrates how the EAR can provide a window into everyday
social experiences. Recently, other research has shown how digital devices (e.g., smartphones,
cameras) can successfully be used to collect data to examine psychological, behavioral, and
health-related phenomena as they naturally occur in everyday life (e.g., Brown, Blake, &
Sherman, 2017; Lathia, Sandstrom, Mascolo, & Rentfrow, 2017). Whereas past research often
focused on between person comparisons, these new devices are particularly suited to disentan-
gling the within- vs. between-person associations between social activity and well-being.
Combining the EAR method with mobile sensing and experience sampling assessments
would enable researchers to compare participants’ happiness on days when they have a lot of
small talk with days when they predominately engage in substantive conversations. For exam-
ple, a recent study found evidence for within-person as well as between-person links between
social interactions and momentary health indicators in daily life (Bernstein, Zawadzki, Juth,
Benfield, & Smyth, 2017).
Interestingly and somewhat surprisingly – and in contrast to our expectations specifi-
cally with regards to extraversion – we did not find evidence of personality moderating the
identified effects. Absence of evidence, however, should not be mistaken for evidence of ab-
sence and future research should follow up on this aspect. Empirically, although our study
was well-powered to detect small moderation effects according to conventional power anal-
yses, our effective power may ultimately have been considerably lower considering that it is
23
becoming clear that the field has historically operated on an optimistic consensus of what con-
stitutes small, medium, and large effects (Aguinis, Beaty, Boik, & Pierce, 2005). Hence, it is
possible that our study was underpowered to detect small or even medium moderation effects.
Theoretically, extraversion might not have emerged as a moderator for the small talk effect
because only the cognitive component of subjective well-being - life satisfaction - was as-
sessed in the current study. Since small talk occurs more often with strangers than with close
others, this type of conversation may be an emotionally unpleasant experience for introverts
who are reserved, but an emotionally pleasant experience for extraverts who are outgoing.
Therefore, moderation might be more apparent for the emotional component of well-being
that assesses positive and negative affect. Future research should integrate the affective com-
ponent of well-being, model personality as a latent rather than an observed variable, and ex-
amine whether effects of naturalistically observed conversation quantity and quality extend
beyond psychological well-being to physical well-being.
The findings reported here should be considered in light of several further limitations.
First, even though our explicit goal was to capitalize on the commonalities of four unique
samples, results might still be limited with respect to generalizability to the general popula-
tion. Second, our data are correlational and therefore causally ambiguous: whether it is the
satisfied person who attracts more substantive conversations or whether having substantive
conversations makes people more satisfied with their lives is still to be clarified in future (ex-
perimental) research. Knowledge about the mechanisms of whether and how substantive con-
versations relate to better well-being could inform health counselors and inspire innovative
interventions. Third, we did not account for the broader social context of the sampled interac-
tions, that is with whom participants were having small talk or substantive conversations (e.g.,
a stranger vs. a friend). Future research should clarify whether small talk and substantive con-
versations may relate differentially with well-being in different social or normative contexts.
For example, Sandstrom and Dunn (2014) showed that more daily weak tie interactions (e.g.,
24
a small chat with a coffee barista, work colleague, yoga classmate) predicted greater average
well-being.
Overall, our findings are consistent with prior intensive longitudinal studies showing
that the quantity as well as quality of social interactions matter for well-being (e.g., Carmi-
chael, Reis, & Duberstein, 2015). However, it might be that one of the “active ingredients” of
having a conversation is, in fact, that the conversation meets a certain level of meaningful-
ness. As Baumeister and Leary (1995) argue, mere social contact does not satisfy the basic
need to belong. Having close relationships and – as our results suggest – meaningful, substan-
tive conversations might be the key ingredient to a satisfied life.
25
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Supplemental Material
Table S1. Conversational Purpose Coding System (here as used in Study 3)
Type of
conversation
Description
Mutually Exclusive - Only mark 1 of the 5 based on big picture of convo.
You can leave blank even if "talk = 1"
Practical
Conversation is about practical everyday things. The information exchanged
serves a pragmatic purpose in the participant’s everyday life. Can include
making plans, discussing what is for dinner, picking up kids, travel arrange-
ments. NOT CCing people on your thoughts.
e.g. "We need parmesan cheese for our dinner tonight"; "I will pick up Sally
on Monday and Wednesdays at 5"
Small Talk
The purpose of this interaction is completely non-instrumental. No (or very
trivial) information is exchanged- everything would be the same if the con-
versation never happened.
e.g. “how’s the weather?” “I stepped on something” “What are you up to?”
Substantive
Conversation
Any conversation that has the purpose to exchange thoughts, information,
values, ideas about a (NON-EMOTIONAL) topic; it could be about news of
the day, about political issues, philosophical topics, theoretical ideas; infor-
mation only.
THE CONVERSATION DOES NOT REALLY HAVE TO BE "DEEP",
ONLY NEEDS TO HAVE "SUBSTANCE"
e.g., “Aren’t Muslims not supposed to drink alcohol?”; “you heard that the
WTC was attacked?”; “I found this book interesting.”; “Guns n’ Roses has a
real rock n’ roll sound to them”
Personal /
EmotionalDisclosure
Participant is sharing his/her own personal feelings or emotions. Can in-
clude talking about their
or a parent’s divorce and their hopes and dreams for
the future. The conversation passes a threshold of being trivial (for the par-
ticipant).
e.g., “I feel so shitty”; “I am scared about my grades in class”; “I have a
crush on x”
NOT accusatory statements, such as “It pisses me off when you talk with
other women at parties.” --> complaining
Gossip
Conversation is about another person who is not present. Spreading rumor /
reputational information about another person in their absence.
IMPORTANT: DOES NOT HAVE TO BE NEGATIVE.
e.g "Xxxx talked back and that’s why he was fired"; "Did you hear about
their break-up?"; “Frank is so silly sometimes”; “Did you hear the lead
signer of Gun n’ Roses is dating Xxxx?”
31
Table S2. Model Fit Comparisons between Reference Model and Models Including the Moderator
Spending time alone
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1344.71
1344.82
(30.36)
+ Extraversion
1346.75
1346.79
(30.34)
1.97
0.89
[0.23,
3.71]
+ Agreeableness
1343.38
1343.42
(30.13)
-1.40
3.82
[-8.89,
6.09]
+ Contientiousness
1345.40
1345.44
(30.34)
0.62
1.98
[-3.26,
4.50]
+ Neuroticsm
1346.98
1347.02
(30.41)
2.20
0.40
[1.42,
2.98]
+ Openness
1346.39
1346.43
(30.35)
1.61
1.12
[-0.59,
3.81]
Talking with others
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1341.91
1341.93
(30.79)
+ Extraversion
1344.10
1344.14
(30.82)
2.21
0.63
[0.98,
3.44]
+ Agreeableness
1343.54
1343.59
(30.83)
1.66
1.55
[-1.38,
4.70]
+ Contientiousness
1342.25
1342.29
(30.58)
0.36
1.99
[-3.54,
4.26]
+ Neuroticsm
1343.92
1343.97
(30.77)
2.04
0.72
[0.63,
3.45]
+ Openness
1340.24
1340.31
(30.30)
-1.62
4.23
[-9.91,
6.67]
Small talk
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1358.45
1358.48
(29.74)
+ Extraversion
1359.94
1359.99
(29.64)
1.51
1.08
[-0.61,
3.63]
+ Agreeableness
1360.02
1360.27
(29.65)
1.79
1.35
[-0.86,
4.44]
+ Contientiousness
1360.09
1360.12
(29.68)
1.64
0.31
[1.03,
2.25]
+ Neuroticsm
1359.47
1359.51
(29.64)
1.03
1.75
[-2.40,
4.46]
+ Openness
1359.96
1360.00
(29.72)
1.52
0.81
[-0.07,
3.11]
Small talk (normalized)
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1362.20
1362.23
(29.85)
+ Extraversion
1364.34
1364.40
(29.87)
2.17
1.10
[0.01,
4.33]
+ Agreeableness
1364.08
1364.17
(29.70)
1.94
1.86
[-1.71,
5.59]
+ Contientiousness
1364.11
1364.15
(29.83)
1.92
0.33
[1.27,
2.57]
+ Neuroticsm
1363.29
1363.33
(29.64)
1.10
1.91
[-2.64,
4.84]
+ Openness
1360.60
1360.64
(29.72)
-1.59
3.41
[-8.27,
5.09]
Substantive conversation
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1342.79
1342.82
(29.69)
+ Extraversion
1344.69
1344.74
(29.53)
1.92
1.04
[-0.12,
3.96]
+ Agreeableness
1341.50
1341.54
(29.64)
-1.28
3.10
[-7.36,
4.80]
+ Contientiousness
1344.10
1344.14
(29.61)
1.32
1.18
[-0.99,
3.63]
+ Neuroticsm
1344.48
1344.52
(29.69)
1.70
0.72
[0.29,
3.11]
+ Openness
1344.26
1344.31
(29.60)
1.49
1.50
[-1.45,
4.43]
Substantive conversation (norm.)
WAIC
LOOIC
(SE)
Δ LOOIC
(SE)
[CI95]
Reference model
1356.03
1355.99
(29.62)
+ Extraversion
1357.90
1357.95
(29.45)
1.96
1.36
[-0.71,
4.63]
+ Agreeableness
1356.95
1356.99
(29.75)
1.00
2.18
[-3.27,
5.27]
+ Contientiousness
1357.76
1357.80
(29.58)
1.81
0.37
[1.08,
2.54]
+ Neuroticsm
1358.10
1358.14
(29.63)
2.15
0.56
[1.05,
3.25]
+ Openness
1357.82
1357.86
(29.58)
1.87
0.24
[1.40,
2.34]
Note. Pooled data from Study 1, Study 2a, Study 3 and Study 4 were used. Models with the moderator in-
cluded (one at a time) were tested against the reference model; lower WAICs and LOOICs denote better
32
model fit; Δ LOOIC = estimated difference of expected leave-one-out prediction errors and its confidence
interval [CI95].
33
Table S3. Posterior Bayesian Point Estimates and Credibility Intervals for Interaction Terms in Correlation
Metric (Moderation Analyses)
Spending time alone
PBPE
[CI95]
+ Extraversion
-0.02
[-0.09,
0.06]
+ Agreeableness
-0.07
[-0.15,
0.01]
+ Contientiousness
-0.04
[-0.12,
0.04]
+ Neuroticsm
-0.01
[-0.08,
0.07]
+ Openness
0.02
[-0.05,
0.10]
Talking with others
PBPE
[CI95]
+ Extraversion
0.01
[-0.07,
0.09]
+ Agreeableness
0.03
[-0.05,
0.10]
+ Contientiousness
0.04
[-0.03,
0.12]
+ Neuroticsm
0.01
[-0.06,
0.09]
+ Openness
-0.08
[-0.15,
0.00]
Small talk
PBPE
[CI95]
+ Extraversion
0.02
[-0.06,
0.11]
+ Agreeableness
0.02
[-0.05,
0.10]
+ Contientiousness
0.00
[-0.07,
0.08]
+ Neuroticsm
-0.04
[-0.12,
0.04]
+ Openness
0.02
[-0.06,
0.10]
Small talk (normalized)
PBPE
[CI95]
+ Extraversion
0.02
[-0.06,
0.10]
+ Agreeableness
0.03
[-0.05,
0.11]
+ Contientiousness
0.00
[-0.08,
0.08]
+ Neuroticsm
-0.04
[-0.12,
0.04]
+ Openness
0.07
[0.00,
0.15]
Substantive conversation
PBPE
[CI95]
+ Extraversion
0.03
[-0.11,
0.17]
+ Agreeableness
0.14
[-0.02,
0.29]
+ Contientiousness
0.05
[-0.10,
0.20]
+ Neuroticsm
-0.03
[-0.18,
0.12]
+ Openness
-0.05
[-0.20,
0.10]
Substantive conversation (normalized)
PBPE
[CI95]
+ Extraversion
0.03
[-0.05,
0.10]
+ Agreeableness
0.04
[-0.03,
0.12]
+ Contientiousness
0.01
[-0.07,
0.08]
+ Neuroticsm
-0.01
[-0.09,
0.07]
+ Openness
0.00
[-0.08,
0.08]
Note. Pooled data from Study 1, Study 2a, Study 3 and Study 4 were used. Models included one moderator at
a time.
