Passive Facebook Usage Undermines Affective Well-Being:
Experimental and Longitudinal Evidence
Philippe Verduyn
University of Leuven
David Seungjae Lee, Jiyoung Park, Holly Shablack,
Ariana Orvell, Joseph Bayer, Oscar Ybarra,
John Jonides, and Ethan Kross
University of Michigan, Ann Arbor
Prior research indicates that Facebook usage predicts declines in subjective well-being over time. How
does this come about? We examined this issue in 2 studies using experimental and field methods. In
Study 1, cueing people in the laboratory to use Facebook passively (rather than actively) led to declines
in affective well-being over time. Study 2 replicated these findings in the field using experience-sampling
techniques. It also demonstrated how passive Facebook usage leads to declines in affective well-being:
by increasing envy. Critically, the relationship between passive Facebook usage and changes in affective
well-being remained significant when controlling for active Facebook use, non-Facebook online social
network usage, and direct social interactions, highlighting the specificity of this result. These findings
demonstrate that passive Facebook usage undermines affective well-being.
Keywords: Facebook, social support, well-being, envy, online social networks
Just a decade ago people primarily relied on face-to-face inter-
actions, the phone, and e-mail to connect. Today, such connections
often occur instantly via online social networks such as Face-
book—but to what effect on well-being?
Kross et al. (2013) addressed this issue by examining the lon-
gitudinal implications of Facebook use for the two components of
subjective well-being: how people feel moment-to-moment and
how satisfied they are with their lives. They found that the more
people used Facebook during one time period, the worse they
subsequently felt; the more they used Facebook over 2-weeks, the
more their life satisfaction levels declined over time.
Although these findings begin to illuminate the relationship
between Facebook usage and subjective well-being, they raise an
important question: How does Facebook usage lead to these de-
clines? We addressed this question by examining whether the way
people use Facebook, in particular whether they do so actively or
passively, explains how this technology impacts subjective well-
being.
Prior research indicates that Facebook activities can be dichot-
omized into active and passive forms of usage (Burke, Marlow, &
Lento, 2010; Deters & Mehl, 2013; Krasnova, Wenninger, Wi-
djaja, & Buxmann, 2013). Active usage refers to activities that
facilitate direct exchanges with others (e.g., posting status updates,
commenting on posts); passive usage involves consuming infor-
mation without direct exchanges (e.g., scrolling through news
feeds, viewing posts). This distinction is important because cross-
sectional work has linked passive Facebook usage with reduced
levels of subjective well-being (Krasnova et al., 2013).
Why might passive Facebook usage undermine well-being?
On Facebook, people tend to portray themselves in overly
flattering ways (Barash, Ducheneaut, Isaacs, & Bellotti, 2010;
Kross et al., 2013; Mehdizadeh, 2010; Newman, Lauterbach,
Munson, Resnick, & Morris, 2011). They also communicate
positive life developments more frequently than negative ones
(Kross et al., 2013). Theoretically, continually exposing oneself
to positive information about others should elicit envy, an
emotion linked to lower well-being (Salovey & Rodin, 1984;
Smith & Kim, 2007). Although some cross-sectional evidence
supports this idea (Chou & Edge, 2012; Krasnova et al., 2013),
experimental and longitudinal evidence is needed to demon-
strate whether it is true.
In sum, previous research suggests that Facebook use negatively
influences subjective well-being. However, the mechanisms that
underlie this relationship are not well understood. Does the way
that people use Facebook, in particular whether they do so pas-
sively or actively, differentially impact subjective well-being? If
so, what role does envy play in explaining how passive Facebook
usage in particular negatively impacts subjective well-being? The
This article was published Online First February 23, 2015.
Philippe Verduyn, Department of Psychology, University of Leuven;
David Seungjae Lee, Jiyoung Park, Holly Shablack, and Ariana Orvell,
Department of Psychology, University of Michigan, Ann Arbor; Joseph
Bayer, Department of Communication Studies, University of Michigan,
Ann Arbor; Oscar Ybarra, John Jonides, and Ethan Kross, Department of
Psychology, University of Michigan, Ann Arbor.
This research was supported by funds provided by the University of
Michigan to EK and a postdoctoral research fellowship to PV from the
Fund for Scientific Research-Flanders (FWO). Author contributions: Con-
ceived and designed Study 1: PV, DSL, JP, JB, HS, AO, OY, JJ, EK;
conceived and designed Study 2: PV, DSL, JP, HS, JB, OY, JJ, EK;
performed Study 1: DSL, HS, AO; performed Study 2: HS; analyzed the
data: PV, DSL; wrote the article: PV, DSL, AO, EK; discussed the results
and commented on the manuscript: PV, DSL, JP, HS, AO, JB, JJ, EK.
Correspondence concerning this article should be addressed to Philippe
Verduyn, Tiensestraat 102 - box 3713, 3000 Leuven, Belgium, or Ethan Kross,
530 Church Street, Ann Arbor, MI 48109. E-mail: philippe.verduyn@
ppw.kuleuven.be or ekross@umich.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology: General © 2015 American Psychological Association
2015, Vol. 144, No. 2, 480–488 0096-3445/15/$12.00 http://dx.doi.org/10.1037/xge0000057
480
cross-sectional design used to address these questions in prior
work and exclusive focus on the cognitive component of subjec-
tive well-being (e.g., life satisfaction) makes it difficult to answer
these questions definitively.
Here we aimed to fill these knowledge gaps by using a combi-
nation of laboratory and experience sampling methods to illumi-
nate how Facebook use impacts subjective well-being. In Study 1,
we manipulated whether participants used Facebook passively or
actively in the laboratory, and examined the immediate and de-
layed effects of this manipulation on subjective well-being. In
Study 2, we performed a 1-week experience sampling study to
examine how active versus passive Facebook use impacts subjec-
tive well-being over time in daily life.
Study 1
Method
Participants. Eighty-four undergraduate students (M
age
⫽
19.93, SD
age
⫽ 4.20; 52 females; 79% European American, 16%
Asian, 1% Middle Eastern, 2% African American, 2% other) were
randomly assigned to an active (N ⫽ 42) or passive (N ⫽ 42)
Facebook use condition in exchange for course credit. Participants
had to have an active Facebook account to participate. The Uni-
versity of Michigan institutional review board approved this study.
We aimed for at least 35 participants per condition. The research
coordinator was thus told to stop running the study after approx-
imately 80 participants.
Baseline measures. Upon arrival in the laboratory, partici-
pants were seated in front of a computer where they remained for
the entirety of the initial session. The session began with partici-
pants rating their affect (“How do you feel right now?” 0 ⫽ very
negative, 100 ⫽ very positive; M ⫽ 67.77, SD ⫽ 18.52); loneliness
(“How lonely do you feel right now?” 0 ⫽ not at all lonely, 100 ⫽
very lonely; M ⫽ 34.22, SD ⫽ 23.30); and life satisfaction (M ⫽
5.09, SD ⫽ 1.24, ␣⫽.89; Diener, Emmons, Larsen, & Griffin,
1985).
We also assessed participants’ motivation for using Facebook
by asking them to indicate whether they use Facebook to keep in
touch with friends (77% answered yes), to find new friends (13%
answered yes), to share good things with friends (52% answered
yes), to share bad things with friends (7% answered yes), to
obtain new information (68% answered yes), or other: please
explain (20% answered yes). Examples of other reasons in-
cluded keeping in touch with family and organize photos.
Additional measures were administered during this session for
filler and/or exploratory purposes. The measures reported here are
those that were theoretically motivated and directly informed by
our previous research (Kross et al., 2013).
Experimental manipulation. After completing the baseline
measures, participants were randomly assigned to use Facebook
actively or passively for 10 min. The experimenter explained that
active Facebook use involved posting and communicating with
others on Facebook—for example, posting status updates or
sharing links, reacting and commenting on friends’ posts or
private messages; passive Facebook use involved browsing
Facebook—for example, scrolling through news feeds, looking
at friends’ pages and pictures, or a band’s page. Participants in
the active condition were instructed to use Facebook actively
and refrain from using it passively; those in the passive condi-
tion received the opposite instructions (for verbatim instruc-
tions, see Table 1).
Compliance. To ensure that participants followed protocol,
we recorded, unbeknownst to participants, their screen while they
were using Facebook with a software program called TeamViewer.
Nine participants (five in the active condition, four in the passive
condition) did not follow instructions (e.g., using Facebook ac-
tively when instructed to use it passively). They were excluded
from all analyses.
Postmanipulation questionnaire. After the 10-min Facebook
usage period was complete, the experimenter returned to the lab
and instructed the participant to complete another brief online
questionnaire via Qualtrics, which asked participants to rate their
Table 1
Cover Story and Manipulation Instructions for Study 1
Passive Facebook use instructions Active Facebook use instructions
Although there are many factors that contribute to Facebook’s popularity,
some studies suggest that one of the key reasons for Facebook’s
popularity is that it allows people to browse their social world
conveniently. By browsing, we mean scrolling through one’s news
feed, looking at one’s friends’ pages, pictures, and status updates.
Many people report that through browsing, they can easily connect to
and experience their social world.
Although there are many factors that contribute to Facebook’s popularity,
some studies suggest that one of the key reasons for Facebook’s
popularity is that it allows people to have direct communication with
others conveniently—by direct communication, we mean posting
status updates, sharing links, reacting and commenting on friends’
walls, or sending messages. Many people report that through direct
communication, they can easily connect to and experience their social
world.
So for the next 10 min, we ask that you try using Facebook only for
browsing: for example, scrolling through your news feed, looking
at your friends’ pages, pictures, and status updates, or a band’s
page, etc. In addition, we ask that you only use Facebook for
browsing and refrain from other activities, such as posting statuses,
sharing links, reacting and commenting on friends’ walls, or
sending messages.
So for the next 10 min, we ask that you try using Facebook only for
direct communication: for example, posting status updates, sharing
links, reacting and commenting on friends’ walls, or sending
messages. In addition, we ask that you only use Facebook for direct
communication and refrain from other activities, such as browsing,
scrolling through your news feed, looking at your friends’ pages,
pictures, and status updates, or a band’s page, etc.
While you are browsing we ask that you refrain from clicking on any
links that will lead to non-Facebook sites.
While you are browsing we ask that you refrain from clicking on any
links that will lead to non-Facebook sites.
Note. Bolded text reflects aspects of the manipulation that differed between conditions.
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481
FACEBOOK AND WELL-BEING
current mood (T
2
affect, M ⫽ 69.51, SD ⫽ 17.18); how lonely they
felt (T
2
loneliness, M ⫽ 27.89, SD ⫽ 21.30); how connected to
others they felt (T
2
connected, 0 ⫽ not at all connected, 100 ⫽
very connected, M ⫽ 58.74, SD ⫽ 21.56); and how much better
(T
2
better, 0 ⫽ not at all better, 100 ⫽ better, M ⫽ 54.41, SD ⫽
26.54) or worse (T
2
Worse, 0 ⫽ not at all worse, 100 ⫽ worse,
M ⫽ 21.99, SD ⫽ 22.11) they thought their life seemed compared
to others. Because the latter two items both reflected how partic-
ipants viewed their own life compared with others’, we averaged
(reverse-coded T
2
better) them so that higher scores reflected the
tendency to view one’s life as worse off than other people (r ⫽ .48,
M ⫽ 33.76, SD ⫽ 20.86).
Funneled debriefing and end-of-day instructions. Next, a
funneled debriefing was administered to determine whether
participants were aware of why we manipulated their Facebook
usage. Eight participants (three in the active condition, five in
the passive condition) were aware of the study hypotheses and
were thus excluded from all analyses on a priori grounds
leaving 67 participants (34 in the active and 33 in the passive
condition).
Participants were also asked whether they believed they were
watched while using Facebook. Nine participants reported suspi-
cion in this regard. As we had no predictions on the possible effect
of suspicion on our results we did not exclude these participants.
Nevertheless, it is notable that the conclusions remained identical
with or without these participants.
At this point participants were notified that the in-laboratory
session was complete. They were told that they would receive a
follow-up survey in the evening (9 p.m.), which they were asked
to complete upon receipt.
End-of-day questionnaire. Participants received a follow-up
survey at 9 p.m. All but four participants (two from each condi-
tion) completed the survey (95.2%). Another four participants
initially forgot to complete the follow-up survey and completed it
during the days following the experiment instead. Excluding these
participants from the analyses did not substantively influence the
results. The average time elapsed between the in-laboratory ses-
sion and the end-of-the-day questionnaire was 9.04 hr (SD ⫽
11.52). Controlling for the elapsed time did not substantively
influence the results.
The survey included all items from the post manipulation
questionnaire (T
3
affect, M ⫽ 67.05, SD ⫽ 21.06; T
3
loneliness,
M ⫽ 25.77, SD ⫽ 23.30; T
3
connected, M ⫽ 61.84, SD ⫽
20.28; T
3
better, M ⫽ 59.99, SD ⫽ 21.02; T
3
worse, M ⫽ 25.66,
SD ⫽ 22.39). In addition, to assess potential lagged effects of
active versus passive Facebook usage on cognitive well-being,
we administered the Satisfaction with Life Scale again (T
3
SWLS, M ⫽ 5.26, SD ⫽ 1.20, ␣⫽.90).
Finally, for exploratory purposes we also asked participants
how much they used Facebook actively (T
3
active Facebook
use, M ⫽ 22.61, SD ⫽ 25.05) and passively (T
3
passive Face
-
book use, M ⫽ 49.20, SD ⫽ 30.28), and to indicate the degree
to which they used other non-Facebook online social network
sites actively (T
3
non-Facebook active social network use, M ⫽
32.78, SD ⫽ 30.86) and passively (T
3
non-Facebook online
social network use, M ⫽ 39.78, SD ⫽ 33.93) since they left the
lab.
Results
We examined the effect of type of Facebook usage on affective
well-being (“How do you feel right now?”) by performing a 2
(Facebook Use: Active vs. Passive) ⫻ 3 (Time of Assessment:
Baseline vs. Post Manipulation vs. End of Day) repeated measures
Analysis of Variance (ANOVA). This analysis revealed a signif-
icant interaction, F(2, 126) ⫽ 4.04, p ⫽ .02,
2
⫽ .06, uncorrected;
F(1.61, 101.1) ⫽ 4.04, p ⫽ .03,
2
⫽ .06, Greenhouse-Geisser
corrected.
1
As Figure 1 and Table 2 illustrate, neither passive use
nor active use participants displayed changes in affect immediately
following the manipulation. However, passive use participants
displayed a significant drop in affective well-being at the end of
the day relative to both their baseline and postmanipulation affect
levels; active use participants did not. The two groups also differed
significantly on end of the day affect, t(126) ⫽⫺3.07, p ⬍ .01,
95% CI [⫺16.78, ⫺3.66]. Gender did not moderate the effect of
type of Facebook usage on affective well-being across time, F(2,
122 ⫽ .31, p ⫽ .73,
2
⫽ .01 uncorrected; F(1.61, 98.10) ⫽ .31,
p ⫽ .69,
2
⫽ .01, Greenhouse-Geisser corrected.
Although we expected passive Facebook usage would lead
participants to view their own life as worse compared to others, a
2 (Facebook Use) ⫻ 2 (Time of Assessment: Postmanipulation vs.
End of the Day) repeated measures ANOVA on this variable did
not reveal a significant effect of condition or condition by time
interaction (Fs ⬍ 1.32, ps ⬎ .25). The complementary analysis
performed on life satisfaction likewise failed to reveal any signif-
icant effects involving condition (Fs ⬍ 1.76, ps ⬎ .19). We did,
however, observe a significant effect of time on life satisfaction,
F(1, 63) ⫽ 6.96, p ⫽ .01,
2
⫽ .10, 95% CI [.04, .25]; participants
scored higher on this measure at the end of the day compared to
baseline. This increase was not related to any type of social media
usage that we assessed (Fs ⬍ 1.55, ps ⬎ .21).
2
Note that unlike affective well-being (described earlier), both of
these measures were administered twice (not three times) to reduce
the likelihood that participants would discern the goals of the
study, which is also why 2 ⫻ 2 ANOVAs were performed.
Finally, the experimental manipulation did not influence how
much participants reported using Facebook overall, F(1, 63) ⫽ .16,
p ⫽ .69,
2
⫽ .003, 95% CI [⫺15.17, 10.11]; or how actively, F(1,
63) ⬍ .001, p ⫽ .98,
2
⬍ .001, 95% CI [⫺11.71, 11.97]; or
passively, F(1, 63) ⫽ .37, p ⫽ .55,
2
⫽ .006, 95% CI [⫺19.65,
10.53] they used Facebook after they left the lab. It did, however,
influence how much people reported using non-Facebook online
social networks. Specifically, participants in the passive condition
1
Mauchly’s Test of Sphericity indicated that the assumption of sphe-
ricity had been violated,
2
(2) ⫽ 17.54, p ⬍ .0001, therefore degrees of
freedom were corrected using Greenhouse-Geisser estimates of sphericity
(ε ⫽ .80).
2
Although our study focused on how type of Facebook usage influences
the two components of subjective well-being (affect and life satisfaction),
we also measured loneliness and social connection because some research
has linked active Facebook usage with changes in these constructs (Deters
& Mehl, 2013). Neither the main effect of condition nor the Condition ⫻
Time interaction was significant for these variables (Fs ⬍ .51, ps ⬎ .52).
However, the effect of time was significant for loneliness, F(2, 118) ⫽
8.60, p ⬍ .001,
2
⫽ .13); all participants felt less lonely at the second,
t(118) ⫽⫺3.25, p ⬍ .01, 95% CI [⫺12.80, ⫺3.10] and third assessment,
t(118) ⫽⫺3.86, p ⬍ .001, 95% CI [⫺14.29, ⫺4.60]) compared with
baseline.
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482
VERDUYN ET AL.
reported engaging more in active, F(1, 63) ⫽ 5.12, p ⫽ .03,
2
⫽
.08, 95% CI [2.02, 32.69]; and passive, F(1, 63) ⫽ 5.01, p ⫽ .03,
2
⫽ .07, 95% CI [2.04, 35.80] forms of non-Facebook online
social interaction (e.g., Tumblr, Twitter, Blogger, MySpace, Insta-
gram) after they left the lab. Critically, controlling for these other
types of non-Facebook online social network use did not substan-
tively influence any of the aforementioned results.
Study 2
Study 1 demonstrated that passive Facebook usage decreases
affective well-being, but not life satisfaction. Study 2 examined
whether passive Facebook usage predicts similar outcomes
when people engage in this behavior spontaneously in daily life.
It also investigated the psychological mechanism underlying the
link between passive Facebook usage and affect. In Study 1, we
did not observe an effect of type of Facebook usage on people’s
tendency to view their life as worse off than others, which we
thought would exacerbate envy and lead to emotional declines.
One explanation for this null finding is that participants may not
have been aware of whether they compared their life to others
(Brickman & Bulman, 1977; Gilbert, Giesler, & Morris, 1995;
Goethals, 1986; Nisbett & Wilson, 1977; Wood, 1996). Conse-
quently, it is possible that people did engage in this comparison
process, but our measurement strategy may have failed to
capture it. Study 2 circumvented this issue by examining
whether passive Facebook usage influenced envy, a consciously
accessible subjective experience (Ellsworth, 1995; Robinson &
Clore, 2002) that we thought would be a more proximal pre-
dictor of affective well-being.
We examined these issues in Study 2 by text messaging
participants five times a day for 6 days. Each text contained a
link to an online survey, which asked participants to answer
questions that assessed affective well-being, envy, active Face-
book usage, passive Facebook usage, direct social interaction,
and non-Facebook online social network usage (for question
items, see Table 3). We performed lagged analyses on partici-
pants’ responses to these questions and their answers to the
Satisfaction With Life Scale (Diener et al., 1985), which they
completed before and after the experience-sampling phase of
the study, to examine whether type of Facebook usage predicts
changes in affective and cognitive well-being over time.
Method
Participants. Eighty-nine people (M
age
⫽ 20.23, SD
age
⫽
2.10; 61 females; 53% European American, 34% Asian, 8% Af-
rican American, and 5% other) were recruited for a study about
Facebook through flyers posted around Ann Arbor, Michigan. To
qualify for the study participants had to possess a Facebook
account and a touch-screen smartphone. They received up to $40
and were entered into a raffle to receive an iPod Nano for partic-
ipating. The University of Michigan institutional review board
approved this study.
We determined our target sample size by referencing a recent
experience sampling study on Facebook and well-being, which
consisted of 82 participants (Kross et al., 2013). The research
coordinator was told to stop running participants after approx-
imately 80 participants were successfully run through the pro-
tocol.
Phase 1. Participants completed a set of questionnaires, which
included the Satisfaction With Life Scale (M ⫽ 5.14, SD ⫽ 1.08,
␣⫽.85; Diener et al., 1985), the Beck Depression Inventory (M ⫽
.43, SD ⫽ .37, ␣⫽.90; Beck, Ward, Mendelson, Mock, &
Erbaugh, 1961), the Revised UCLA Loneliness Scale (M ⫽ 1.72,
SD ⫽ .51, ␣⫽.92; Russell, Peplau, & Cutrona, 1980), the
Rosenberg Self-Esteem Scale (Rosenberg, 1965; M ⫽ 2.68, SD ⫽
.56, ␣⫽.90), and the Social Provision Scale (M ⫽ 3.48, SD ⫽ .36,
␣⫽.82; Cutrona, 1989), which we modified to assess perceptions
of Facebook support.
We also assessed participants’ motivation for using Facebook
by asking them to indicate whether they use Facebook to keep
in touch with friends (95% answered yes), to find new friends
Table 2
Simple Effects Demonstrating How the Passive and Active Facebook Conditions Affect Levels
Fluctuate Over Time
Condition
Postmanipulation vs.
baseline End of day vs. baseline
End of day vs.
postmanipulation
t 95% CI t 95% CI t 95% CI
Passive Facebook use .29 [⫺5.67, 7.61] 2.74
ⴱⴱ
[2.55, 15.83] 2.45
ⴱ
[1.58, 14.86]
Active Facebook use ⫺1.26 [⫺10.69, 2.39] ⫺1.24 [⫺10.63, 2.45] .02 [⫺6.48, 6.60]
Note. Degrees of freedom for all tests is 126.
ⴱ
p ⬍ .05.
ⴱⴱ
p ⬍ .01.
ⴱⴱⴱ
p ⬍ .0001.
50
55
60
65
70
75
80
Baseline Post Manipulation End of Day
Affective Well-being
Active Passive
Figure 1. Affective well-being over time as a function of passive vs.
active Facebook use. Error bars represent ⫹/⫺ 1 standard error.
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483
FACEBOOK AND WELL-BEING
(19% answered yes), to share good things with friends (75%
answered yes), to share bad things with friends (34% answered
yes), to obtain new information (76% answered yes), or other:
please explain (14% answered yes). Examples of other reasons
included keeping in touch with family and playing games.
Phase 2. Participants were text-messaged five times per day
between 10 a.m. and midnight for 6 consecutive days. Text-
messages occurred at random times within five 168-min window
blocks each day. Controlling for the length of time between any
two text messages did not substantively influence the results. Each
text-message contained a link to an online survey, which asked
participants to answer questions on affective well-being and lone-
liness at the moment of completing the online survey, and degree
of envy, active Facebook usage, passive Facebook usage, direct
interactions, and non-Facebook social network usage since the
previous report. The original affective well-being response scale
ranged from 0 ⫽ very positive to 100 ⫽ very negative but ratings
were reversed prior to analyses to match the response format of
Study 1. Participants always answered the affect question first,
followed by the loneliness question. The rest of the questions were
presented randomly.
Prior to the start of the study, we operationalized active and
passive Facebook usage for participants in the same way it was
defined in Study 1. Subsequently, the experimenter walked partic-
ipants through the protocol for answering each experience-
sampling question to ensure that they understood how to respond
to them.
On average, participants responded to 80% of the text-
messages (range: 3%–100%). Following prior experience sam-
pling research with a similar study duration (Koval, Kuppens,
Allen, & Sheeber, 2012) we pruned the data by excluding all of
the data from nine participants who responded to ⬍ 60% of the
texts, resulting in 2,084 total experience sampling observations
from 80 participants. This threshold was more conservative
than the one used in our prior work on Facebook and well-being
(Kross et al., 2013) as the duration of the present study was
shorter. Using the same cutoff score as we previously used did
not alter the results.
Phase 3. Seventy-seven of the 80 participants that our analy-
ses focused on returned to the laboratory following Phase 2 to
complete another set of questionnaires, which included the Satis-
faction With Life Scale (M ⫽ 5.06, SD ⫽ 1.13, ␣⫽.85; Diener
et al., 1985). We recorded participants’ number of Facebook
friends (M ⫽ 783.17, SD ⫽ 425.19) and obtained a screenshot of
their Facebook wall posts (so that we could record status updates)
that corresponded to the timespan that the experience-sampling
phase of the study took place during a subsequent session by
asking them to log into their accounts in the presence of the
experimenter. We were unable to obtain Facebook friend and/or
wall data for 14 participants either because this information was
hidden from their walls or because they did not return to have this
information recorded.
3
Analyses overview. Following prior work (Kross et al.,
2013) we examined the relationship between active and passive
Facebook usage and affect using multilevel analyses to account
for the nested data structure. Specifically, we examined whether
T
2
affect (i.e., How do you feel right now?) was predicted by
T
1–2
active Facebook usage (i.e., How much have you used
Facebook actively since the last time we asked?), or T
1–2
passive Facebook usage (i.e., How much have you used Face-
book passively since the last time we asked?), controlling for T
1
affect at level-1 of the model. Note that although this analysis
assesses Facebook usage at T
2
, the question refers to usage
between T
1
and T
2
(hence the notationT
1–2
). Thus, this analysis
allowed us to explore whether active or passive Facebook usage
during the time period separating T
1
and T
2
predicted changes
in affect over time. Following prior work (Koval et al., 2012),
we excluded between-day lags from the lagged analysis (i.e.,
participants first ratings in the morning were not predicted by
their last ratings on the previous day).
When noncompliant cases were observed, we used partici-
pants’ responses to the last text message they answered to
maximize power when examining the lagged effect of type of
Facebook usage on well-being. Thus, if we examined whether
T
2–3
active or passive Facebook usage predicted T
3
affect
controlling for T
2
affect, but did not have data on T
2
affect, then
we used T
1
affect instead. Excluding trials in which participants
did not respond to the previous texts (rather than following this
protocol) did not substantively alter any of the results we report.
All Level-1 predictors were group-mean centered, and intercepts
and slopes were allowed to vary randomly across participants.
Unstandardized regression weights are reported. Significance test-
ing of fixed effects was performed using t tests.
Degrees of freedom vary across analyses for the following
reasons. For the nonlagged analysis 2,084 observations were
used whereas for the lagged analysis only 1,609 observations
were entered into the analysis (because between-day lags were
excluded). When comparing the frequency of different types of
communication (i.e., active Facebook, passive Facebook, non-
3
Additional measures were included during Phases 1 and 3 either to
serve as filler questionnaires or for exploratory purposes. The only mea-
sures that were administered during Phase 1 and Phase 3 (in addition to life
satisfaction) were the BDI (M ⫽ .36, SD ⫽ .42, ␣⫽.93) and the Revised
UCLA Loneliness Scale (M ⫽ 1.72, SD ⫽ .46, ␣⫽.90). Neither active nor
passive Facebook use predicted changes on these measures (ps ⬎ .24). The
measures reported in the text are those that were theoretically motivated
and directly informed by our previous research (Kross et al., 2013).
Participants engaged in a pilot study after Phase 3, the outcomes of which
have no bearing on the current results.
Table 3
Text Message Questions
Variable Question
Affective well-being How do you feel right now?
Envy How envious have you been of others since
the last time we asked?
Active Facebook use How much have you actively used Facebook
since the last time we asked?
Passive Facebook use How much have you passively used Facebook
since the last time we asked?
Direct social interaction How much have you interacted with other
people directly since the last time we
asked?
Non Facebook online
social networking use
How much time have you spent on social
network sites other than Facebook since the
last time we asked?
Note. Participants rated their affective well-being on a 0 ⫽ very negative
to 100 ⫽ very positive scale; all remaining questions used a 0 ⫽ not at all
to 100 ⫽ a lot scale.
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