1327169

research-article2025

PSPXXX10.1177/01461672251327169Personality and Social Psychology BulletinIp and Feldman

Empirical Research Paper

The Complex Misestimation of Others’
Emotions: Underestimation of Emotional
Prevalence Versus Overestimation
of Emotional Intensity and Their
Associations with Well-Being
Ho Ching Ip1

Personality and Social
Psychology Bulletin
﻿1­–19
© 2025 by the Society for Personality
and Social Psychology, Inc
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/01461672251327169
DOI:
10.1177/01461672251327169
journals.sagepub.com/home/pspb

and Gilad Feldman1

Abstract
Jordan et al., 2011, demonstrated that people underestimated the prevalence of others’ negative emotional experiences and
that these were associated with higher well-being. We conducted a preregistered replication of Studies 1b and 3 by Jordan
et al., 2011 (N = 594) with adjustments and added extensions. Building on their methodology, we examined both prevalence
and intensity of emotional experiences, and our findings suggest a much more complex story with surprising effects. We found
an underestimation of the prevalence of negative emotions, but also unexpectedly of an underestimation of the prevalence
of positive emotions, with stronger effects for negative than for positive emotions. However, we found an opposite effect
for emotional intensity; people overestimated the intensity of both positive and negative emotional experiences, again with
stronger effects for negative. Surprisingly, associations between prevalence estimations and well-being were in the opposite
direction to the target article’s. Materials, data, and code: https://osf.io/bwmtr/
Keywords
emotional pluralistic ignorance, positive emotions, negative emotions, affect, preregistered replication, social comparison,
well-being, emotional estimation error
Received August 22, 2023; revision accepted February 17, 2025

Introduction
Background
A growing body of literature has documented people’s
misperceptions of others’ attitudes, beliefs, and behaviors
(Miller & Prentice, 1994). These errors in perceiving others’
psyche and mental states seem at least in part related to purposeful public misrepresentations, as people misrepresent
their private worlds to meet what they perceive social norms
dictate. For example, people tend to underestimate their
peers’ negative emotional experiences, as people tend to hide
their negative emotions (Larson et al., 1982) to adhere to perceived display rules to appear positive (Ekman & Friesen,
1969).
Jordan et al. (2011) demonstrated that people underestimated the prevalence of peers’ negative emotional experiences more than their peers’ positive emotions,1 and that
prevalence estimates were associated with a host of wellbeing measures. Their findings showed that those with lower
prevalence estimates of negative emotions reported greater
loneliness, greater rumination, and less satisfaction with life.

We aimed to revisit and extend Jordan et al. (2011)’s theory and findings. Our first goal was to conduct an independent well-powered preregistered close replication examining
misestimation of emotional prevalence and their associations
with psychological well-being, adopting and improving on
the target article’s methodology. Our second goal was to add
extensions and examine: (a) whether misestimations extend
beyond prevalence to intensity, and (b) whether social comparison orientation moderates associations between prevalence and intensity and well-being.
We begin by introducing the literature on emotional misestimations and the chosen article for replication—Jordan
et al. (2011). We then outline our chosen studies for replication from the target article, the target’s experimental designs,
and our adaptations, improvements, and extensions.
1

University of Hong Kong, Pok Fu Lam, Hong Kong SAR

Corresponding Author:
Gilad Feldman, Department of Psychology, University of Hong Kong, Pok
Fu Lam, Hong Kong SAR.
Email: gfeldman@hku.hk; giladfel@gmail.com

2

The Misestimation of Others’ Emotions
Misestimation of others’ views, behaviors, and experiences
were initially mostly studied in the context of bystander nonintervention during emergencies (Allport, 1924; Darley &
Latane, 1968). It was later developed to study people’s misinterpretations of social norms based on their observations of
public behaviors, which may be misaligned with people’s
true private feelings, especially when those are considered
negative (Miller & Prentice, 1994).
One of the earliest demonstrations of the differences
between public behaviors and private attitudes was in the
1930s, showing a misalignment between what people said in
public compared to what they reported in private settings
(Schanck, 1932). In a review by Sargent and Newman
(2021), they summarized three areas in which this phenomenon has been demonstrated—drug use, alcohol, and sexual
and dating norms. For instance, participants in the study of
Miller and Prentice (1994) expressed in private that they
were uncomfortable with excessive drinking, yet publicly
endorsed pro-drinking policies, presumably due to misperceiving their peers’ behaviors and attitudes and being
pro-drinking.
Jordan et al. (2011) extended the literature by demonstrating people’s misestimation of the prevalence of others’ negative emotions. The misestimation is likely due to people’s
tendency to try to suppress their negative emotions in social
contexts (Larson et al., 1982), given that they are deemed
less socially appropriate and not in line with norms for social
behavior (Ekman & Friesen, 1969).

Choice of Study for Replication: Jordan et al.
(2011)
We aimed to revisit the classic phenomenon to examine the
reproducibility and replicability of the findings with an independent, well-powered, preregistered close replication of
Jordan et al. (2011) with several extensions. We answered
recent calls and a growing recognition of the importance of
reproducibility and replicability in psychological science
(e.g., Nosek et al., 2022; Zwaan et al., 2018).
We chose the article by Jordan et al. (2011) as the target
for replication based on several factors: (a) its academic and
practical impact with no direct replications, (b) the potential
for improvement on their methods and analyses, (c) the
potential in answering new theoretical questions using an
extension in examining misestimation of emotional
intensity.
The article has had an impact on scholarly research in the
area of social psychology, with 306 Google Scholar citations
at the time of writing (August 2024). The article was followed by empirical research demonstrating misestimation
and their impact in social media settings (Tandoc et al.,
2015), job-seeking (Burke & Kraut, 2013), risky sexual
behaviors (Young & Jordan, 2013), and impulsive buying

Personality and Social Psychology Bulletin 00(0)
(Liu et al., 2019). Other research extended the original findings and further investigated the practical implications of the
associations between misestimation and psychological wellbeing outcomes, such as for depressive symptoms (Steers
et al., 2014) and self-esteem (Alfasi, 2019; Wang et al.,
2017). To the best of our knowledge, there are currently no
published direct independent replications of the chosen target article.
When we analyzed the article, we realized that some analyses were in need of revision and improvement. The analyses
conducted for these studies were on an item-level, with six
items for positive, and six items for negative, making it very
challenging to detect any effects, which are related to what
seemed like unreasonably large effects (d = 2.52 in Study
1b, and d = 3.16 in Study 3). It was not clear to us why the
analyses were run only on an item-level rather than also on a
participant-level. Given the target article’s null hypothesis
for positive emotions, we were open to the possibility of also
finding support for meaningful effects for positive emotions
and that with a larger sample of items or analyses on a participant-level, the difference from the null might be more easily
detected. Given our surprising findings we report below, we
also sought an external expert reviewer to examine the
regression analyses conducted in Study 3 of the target article
(shared on https://osf.io/zy5qa/). In his analyses, the external
reviewer raised the possibility of a suppression effect in the
regression analyses between prevalence estimates and wellbeing (Sharpe & Roberts, 1997; Thompson & Levine, 1997).
We, therefore, saw the potential in improving on the conducted analyses by also conducting participant-level analyses and focusing on raw correlations.
We also sought to go beyond examining the misestimation of emotional prevalence to also examine an important
missing piece of the puzzle—the possible misestimation of
emotional intensity. We built on the same methodology to
examine both aspects of emotions together, resulting in a
more comprehensive view of a much broader phenomenon.

Original Hypotheses and Findings in Target
Article: Jordan et al. (2011)
Jordan et al. (2011) empirical work consisted of four studies,
and in the current replication, we focused on Studies 1b and 3.
In Study 1b, they examined whether people would accurately predict the prevalence of others’ emotional experiences. Participants rated their own experiences and
estimated the prevalence of six positive emotional experiences (such as receiving high grades or attending a fun
party) and six negative emotional experiences (such as
thinking about workload or having a fight/argument) in the
2 weeks prior. The researchers hypothesized that (a) people
underestimate negative experiences, yet that (b) these errors
are not present for positive experiences. We note that their
second hypothesis regarding positive emotions was a null
hypothesis, which we believe is better reframed to an

Ip and Feldman

3

Table 1. Jordan et al. (2011) Studies 1b and 3: Summary of Findings.
Study 1b
Experiences
Negative experiences
Had fight/argument
   Thought about distant friends/
family
Thought about enormous
workload
Was rejected by boy/girl
   Received low grade
Thought about bad personal
health habits
Positive experiences
Received high grade
Attended fun party
Participated in athletics
Went out with friends
Talked to distant friends/family
Had great meal

Estimation
errora

Study 3

Average estimation
Estimation Average estimation
error (%)
t-statistics
error
error (%)
t-statistics
−17.2

5.47**

−13.0***
−28.2***

−13.8***
−26.3***

−12.2***

−11.3***

−8.9***
−15.9***
−24.1***

−18.4***
−23.3***
−35.0***
+5.6

1.18

−3.0
+20.9***
+13.7***
+12.6***
−8.3***
−2.3

−21.4

5.99**

+3.8

1.06

−0.3
+13.2***
+7.6***
+11.5***
−9.9***
+0.5

Source. The table was adopted from Jordan et al. (2011, pp. 126, 130).
a
A positive number indicates an overestimation and a negative number indicates an underestimation.
**
p < .01. ***p < .001.

interaction hypothesis: (b-reframed) underestimation errors
are stronger for negative experiences than for positive
experiences. Study 2 showed similar results using a re-analysis of a dataset by Srivastava et al. (2009) with an underestimation of negative emotions and an overestimation of
positive emotions.
In Study 3, the researchers examined the associations
between prevalence estimations and psychological wellbeing. Participants completed scales regarding loneliness,
rumination, depressive symptoms, life satisfaction, subjective happiness, and self-reported their number of friends, in
addition to rating the same emotional events from Study 1b.
They hypothesized that (c) higher prevalence estimates of
others’ negative emotional experiences are associated with
poorer psychological outcomes: greater loneliness, rumination, depressive symptoms, and lower life satisfaction and
subjective happiness. They also hypothesized that (d) having
fewer peers is associated with stronger misestimation.
We summarized the findings of Study 1b and Study 3 in
Table 1 and the hypotheses of the target article in Table 2
(under “Replication: Prevalence estimations”).

Extensions
Intensity of Emotional Experiences. The authors of the target
article acknowledged a limitation in their design, specifically
that they did not differentiate between prevalence and intensity. For instance, participants were asked to rate the prevalence of their peers “receiving a bad grade and felt really bad
about it,” making it difficult to distinguish between their

estimation of the prevalence of receiving a bad grade and
their estimation of the intensity of the emotions associated
with that event. As such, we were unsure whether participants underestimated the prevalence of their peers’ negative
emotions because they believed their peers rarely experienced such events (prevalence), or because they believed
they these events would not elicit strong emotional responses
(intensity).
Therefore, we revised the questions to inquire about both
the prevalence of the events and the intensity of the emotional experiences. By expanding upon the original article,
our aim was to examine the interplay between prevalence
and intensity estimates and gain a clearer understanding of
the source of the misestimation.
Social Comparison Orientation (Exploratory). We aimed to
investigate the role of social comparison orientation, the tendency to compare oneself with others, as a potential predictor of misestimation and well-being measures. Gibbons and
Buunk (1999) found that individuals with a higher social
comparison orientation exhibited greater uncertainty and
instability regarding their self-worth. Consequently, they
tended to evaluate themselves based on how others were faring in public. This has the potential to be even more pronounced in an era in which social media provides ample
opportunities for social comparison.
Accumulating evidence suggests a positive link between
social comparison orientation and threats to psychological wellbeing online, as many social media users tend to selectively
present mostly positive aspects of their lives (Vogel et al., 2014).

4

Personality and Social Psychology Bulletin 00(0)

Table 2. Replication and Extensions: Summary of Hypotheses.
Replication: prevalence estimations
No

Hypothesis

2a
2b

People underestimate the prevalence of others’ negative emotional experiences.
People do not underestimate the prevalence and extent of others’ positive emotional experiences.
[Our reframing of the target’s null hypothesis: Prevalence underestimation errors are stronger for negative
experiences than for positive experiences.]
4a) There is a positive association between the estimation of the prevalence of negative emotional experiences and
well-being.a
4b) There is a negative association between the estimation of the prevalence of positive emotional experiences and
well-being.a
Negative indicators—higher well-being: lower depressive symptoms, lower loneliness, lower rumination
4-1a) There is a negative association between the estimation of the prevalence of negative emotional experiences and
depressive symptoms.
4-1b) There is a positive association between the estimation of the prevalence of positive emotional experiences and
depressive symptoms.
4-2a) There is a negative association between the estimation of the prevalence of negative emotional experiences and
loneliness.
4-2b) There is a positive association between the estimation of the prevalence of positive emotional experiences and
loneliness.
4-3a) There is a negative association between the estimation of the prevalence of negative emotional experiences and
rumination.
4-3b) There is a positive association between the estimation of the prevalence of positive emotional experiences and
rumination.
Positive indicators—higher well-being: higher life-satisfaction, higher subjective happiness.
4-4a) There is a positive association between the estimation of the prevalence of negative emotional experiences and
life satisfaction.
4-4b) There is a negative association between the estimation of the prevalence of negative emotional experiences and
life satisfaction.
4-5a) There is a positive association between the estimation of the prevalence of negative emotional experiences and
subjective happiness.b
4-5b) There is a negative association between the estimation of the prevalence of negative emotional experiences and
subjective happiness.b

4

4-1

4-2

4-3

4-4

4-5

Extensions: intensity estimations
No
2c
2d
5
6
6a
6b
6c
6d
6e

Hypothesis
People underestimate the intensity of others’ negative emotional experiences.c
People overestimate the intensity of others’ positive emotional experiences.c
Social comparison orientation interacts with misestimation in predicting well-being:
The higher the social comparison orientation, the stronger the associations in Hypothesis 4
(Hypotheses 4-1a to 4-5b).
Social comparison orientation is negatively associated with well-being.
Negative indicators—higher well-being: lower depressive symptoms, lower loneliness, lower rumination
Social comparison orientation is positively associated with depressive symptoms.
Social comparison orientation is positively associated with loneliness.
Social comparison orientation is positively associated with rumination.
Positive indicators—higher well-being: higher life-satisfaction, higher subjective happiness.
Social comparison orientation is negatively associated with life satisfaction.
Social comparison orientation is negatively associated with subjective happiness.

a
The target article sometimes shifts from referring to misestimation in its predictions to tests regarding prevalence estimations. We aligned the
hypotheses with the tests conducted and the findings reported, rather than the versions of theory and hypothesis referring to misestimation. In addition,
we specified separate hypotheses for estimations of negative emotions and estimations of positive emotions, to make things clearer.
b
The hypothesis was not supported in the original target article, yet is included due the possibility of lacking power given the item-level analysis and small
number of items.
c
We had no specific predictions for the associations between intensity estimations of positive and negative emotional experience, and these should
therefore be treated as exploratory.

Ip and Feldman
For example, Vogel et al. (2015) found that people with higher
social comparison orientation were more susceptible to deleterious consequences such as lower self-esteem and poorer affect
balance when using social media. Similarly, Wang et al. (2017)
found that social comparison orientation was associated with
increased usage of social networks, which in turn predicted
lower life satisfaction and higher levels of jealousy and envy.
We therefore considered the possibility that this trait
encompasses aspects related to social misestimation, as a
greater inclination to compare oneself against others may also
make individuals more vulnerable to potential discrepancies
between their own experiences and those of others. Therefore,
our aim was to examine the association between social comparison orientation, misestimation of others’ emotional experiences, and psychological well-being. Furthermore, we sought
to explore the interaction between social comparison orientation and misestimation of emotional experiences in relation to
well-being measures, with the exploratory prediction that the
higher the social comparison orientation, the stronger the negative link between misestimation of emotional experiences
and well-being measures.
Replication and Extension: Overview. We summarized the
hypotheses for our adjusted replication and extensions in
Table 2. In hypotheses 2c and 2d, we built on the target article’s hypotheses 2a and 2b to separately assess the prevalence and emotional intensity of emotional events. We also
introduced social comparison orientation as a new measure
to examine its association with the misperception of peers’
emotional experiences and psychological well-being in
Hypotheses 5 and 6.

5
which does not seem to matter for the recruitment of participants, and with the small number of items requiring a very
large effect size on the item-level to be able to detect any
effects. This is related to other issues with some of the effects
reported in the target (e.g., d = 2.52 in Study 1b and d = 3.16
in Study 3) being much larger than typical effects in the field
(see Jané et al., 2024).
Therefore, instead of using the target article’s effects, we
aimed for a sample size that would allow for participantlevel detection of paired-sample t-test d(z) = 0.2 (N = 272),
and correlations of r = .15 (N = 472). These effect sizes are
far smaller than any of the effects reported in the target article and are considered weak to medium effects in social psychology (Jané et al., 2024). This approach also aligns with
the conservative estimate of 460 according to the rule of
thumb recommendations by Simonsohn (2015) for a sample
size 2.5 times larger than the combined samples in the original article (80 in the original Study 1b and 104 in the original
Study 3), though we note that these recommendations were
intended for participant-level between subject-designs, and
not for item-level analyses with fixed number of items. To
account for possible exclusions in case of a failed replication
(see data analysis strategy section) and multiple analyses on
the same sample, we exceeded the planned sample by aiming
for a sample of 600.

Participants

Data Availability, Preregistration, and Open-Science Disclosures. We provided all materials, data, and code on the Open
Science Framework (OSF): https://osf.io/bwmtr/. We first
preregistered the experiment on the OSF (https://osf.io/
qda7j/) and data collection was launched later that week. All
measures, manipulations, exclusions are reported, and data
collection was completed before analyses. The preregistration and manuscript were written based on a template by
Feldman (2023).

We recruited a total of 594 U.S. American college students
on Prolific (Palan & Schitter, 2018; 290 males, 285 females,
19 other/did not disclose; using the Prolific internal qualifier for students). We used Prolific’s filters, by restricting
the location to the United States using “standard sample,”
we set it to “Nationality: United States,” “Country of birth:
United States,” “Student status: Yes,” “Minimum Approval
Rate: 95, Maximum Approval Rate: 100,” “Minimum
Submissions: 50, Maximum Submissions: 100,000,” “Total
times a participant can complete your study: Once.” We
summarized a comparison of the target article sample and
the replication sample in Table 3. We outlined the details of
the pay and survey procedures in the Supplemental Material.

Method

Design and Procedure

Power Analysis and Sensitivity Test

We summarized the experimental design and all measures in
Table 4 (more details are available in the Supplemental
Materials).
Participants rated their own emotional experiences and others’ emotional experiences in random order. Misestimations
were measured as participants’ estimates of others’ emotional
experiences minus the average of all participants’ own emotional experiences. In the target article’s Study 1b, the prevalence and intensity were confounded, which we adjusted to
measure prevalence and intensity separately (see “Measures”
subsection).

We first calculated the effect sizes of the findings reported
in the target article with the help of a guide by Jané et al.
(2024). We then conducted an a priori power analysis
(power = 0.95, alpha = .05), with an upward adjustment.
The calculated effect sizes are summarized in the
Supplemental Materials.
There were several issues with using the target article’s
effects as a basis for power analyses. The target conducted
their analyses on an item-level with an analysis on 12 items,

6

Personality and Social Psychology Bulletin 00(0)

Table 3. Comparison of Target Article Versus Replication: Differences and Similarities.
Study

Jordan et al. (2011) Study 1b

Jordan et al. (2011) Study 3

Replication

Medium (location)
Compensation

80
U.S. American students
35 males, 45 females, 0 other/did
not disclose
Paper-and-pen
N/A

104
U.S. American students
51 males, 54 females, 0 other/did
not disclose
Computer (online)
Nominal payment

Year

2011 or earlier

2011 or earlier

594
U.S. American students on prolific
290 males, 285 females, 19 other/did not
disclose
Computer (online)
Nominal payment (2 British pounds for an
estimated 12 min completion time)
2023

Sample size
Geographic origin
Gender

We conducted the study using Qualtrics. All participants
first indicated their consent. They then rated the prevalence
of six positive and six negative emotional experiences for
themselves and for other U.S. student participants taking this
survey, in counterbalanced order.
Prior to each rating task, we added comprehension checks
to ensure that participants were paying attention to the type of
rating (prevalence or intensity) and who they are rating (self
vs. other). Participants had to answer these checks correctly in
order to proceed to the rating task. We note that this is a deviation from the target article’s procedure to ensure that participants were attentive and knew who and what they were
rating.
For exploratory purposes, we included three questions
that asked participants to provide short sentences regarding
their perceptions of the test items they had previously rated,
to help us better understand participants’ mindsets when
answering some of the items from the target article, to
address the possibility of puzzling or surprising findings
using these items.
Participants then completed measures assessing their loneliness, rumination tendency, depressive symptoms, life satisfaction, subjective happiness, and social comparison
orientation. We randomized the order of the six well-being and
trait measures. Finally, participants answered a funneling section, provided demographic information, and were debriefed.

Measures
Prevalence and Intensity of Own Emotional Experiences (Replication and Extension). In the target article, participants only
rated whether they had experienced an emotional event or
not. We adjusted and extended the measures to also include
intensity:
For each of the following emotional experiences, please indicate
whether you have experienced those sometime in the past
2 weeks and, if you have, the intensity of the emotion.
0 means: You have NOT experienced this emotion in the past
two weeks.
1–100 means: You have experienced this emotion at least once
in the past two weeks.

1 = lowest emotional intensity, and 100 = highest emotional
intensity.

In this way, we measured both prevalence and intensity in
the same question: a value of 0 indicated that the participant
has not experienced this event, and a value of 1 to 100 indicated that the participant has experienced it with a rating of
the intensity of the experience.
Prevalence of Others’ Emotional Experiences (Replication). We
closely followed the target’s measure of participants’ estimates of others’ emotional experiences with: “Please estimate the percentage of other U.S. American student
participants on Prolific taking the survey like you who had
had, sometime in the past 2 weeks, each of the following
emotional experiences. (0%–100%)”
Intensity of Others’ Emotional Experiences (Extension). We
extended the target’s measure of prevalence (above) with a
measure of intensity: “Please try and estimate the emotional
intensity for other U.S. American student participants on
Prolific taking the survey like you who have experienced this
emotion (1 = Lowest emotional intensity, and 100 = Highest
emotional intensity).”
Misestimations of Prevalence and Intensity. We first calculated
the actual prevalence of emotional experiences by counting
for each experience the number of participants who indicated
having that experience, and then converted that into a percentage. We then calculated for each participant, and for each
emotional experience, the misestimation of the prevalence of
that emotional experience as the estimation of prevalence
minus the actual prevalence of that emotional experience.
We calculated the actual intensity of emotional experience by calculating the average of all self-reported intensity
for all those who reported having that experience. We then
calculated for each participant and for each emotional experience, the misestimation of the intensity as the participant’s
estimation of the intensity of that emotional experience
minus the actual intensity of that emotional experience.
This means that for both prevalence and intensity, a misestimation score higher than zero indicates an overestimation,
and a score lower than zero indicates an underestimation.

Ip and Feldman

7

Table 4. Replication and Extension: Experimental Design and Measures.
IV1: positive vs. negative
emotions (within; all
participants rated both)
IV2: self vs. others rating (within; all
participants rated both)

Self ratings
Participants were asked to estimate
based on their own emotional
experiences.
Others ratings
Participants were asked to make
estimations of U.S. American student
participants on Prolific taking the
survey.

Exploratory open-ended

Well-being measures and traits

IV1: estimation of negative emotional events
IV1: estimation of positive emotional events
“Felt sad because they. . .”
“Felt happy because they. . .”
1. Had a fight or argument
1. Received high grades
2. Thought about distant friends or family
2. Attended fun party
3. Thought about enormous workload
3. Participated in athletics
4. Were rejected by someone
4. They went out with friends
5. Received a low grade
5. They talked to distant friends or family
6. Thought about bad personal health habits
6. Had great meal
DV1: prevalence and intensity of own emotional experiences [replication + extension]
“For each of the following emotional experiences, please indicate whether you have experienced those
sometime in the past 2 weeks and if you have—the intensity of the emotion.
0 means: You have NOT experienced this emotion in the past two weeks.
1–100 means: You have experienced this emotion at least once in the past two weeks”
(0 = Not experienced; 1 = Lowest emotional intensity; 100 = Highest emotional intensity.)
DV1a: prevalence of others’ emotional experiences [replication]
“Please estimate the percentage of other U.S. American student participants on Prolific taking the survey like
you who had had, sometime in the past 2 weeks, each of the following emotional experiences.
(0% to 100%)
DV1b: intensity of others’ emotional experiences [extension]
“Please try and estimate the emotional intensity for other U.S. American student participants on Prolific
taking the survey like you who have experienced this emotion
(1 = Lowest emotional intensity; 100 = Highest emotional intensity)”
Perception of vague test items:
Participants briefly wrote about their understanding of vague test items.
“What is a “bad” grade? How do you classify a grade as being a bad grade? Can you give a quick example?
(1–2 sentences)”
“What is an ‘enormous’ workload? How do you classify workload as being an enormous workload? Can you
give a quick example? (1–2 sentences)”
“What is a ‘bad’ personal health habit? How do you classify personal health habit as being bad? Can you give
a quick example? (1–2 sentences)”
(presented in random order)
Loneliness [replication]
UCLA Loneliness Scale (Hays & DiMatteo, 1987, p. 4), 8-item short-form version.
Sample items: “There is no one I can turn to.” and “I can find companionship when I want it.”
(1 = Never to 4 = Often)
Rumination [replication]
Brooding subscale (5-item) of the Ruminative Responses to Depression Questionnaire (Nolen-Hoeksema,
1991; Treynor et al., 2003). Sample items: “Think What am I doing to deserve this?” and “Think about a
recent situation, wishing it had gone better.” etc.
(1 = Almost never to 4 = Almost always)
Depressive symptoms [replication]
Center for Epidemiologic Studies Depression Scale 10-item short-form version (Cole et al., 2004; Radloff,
1977). Sample items: “I was bothered by things that usually don’t bother me.” and “I had trouble keeping
my mind on what I was doing.” etc.
(1 = Rarely or none of the time (less than 1 day) to 4 = All of the time (5–7 days))
Life satisfaction [replication]
Participants were asked to complete the 5-item SWLS (Diener et al., 1985). Sample items: “In most ways my
life is close to my ideal.” and “If I could live my life over, I would change almost nothing.” etc.
(1 = Strongly disagree to 7 = Strongly agree)
Subjective happiness [replication]
SHS 4-item (Lyubomirsky & Lepper, 1999). Sample items:
“In general, I consider myself. . .” and “Compared to most of my peers, I consider myself. . .” etc.
(1 = Not at all/Less happy; 7 = A very happy person/More happy/A great deal)
Social comparison orientation [extension]
The INCOM (Gibbons & Buunk, 1999)
Sample items: “I often compare myself with others with respect to what I have accomplished in life” and “If I
want to learn more about something I try to find out what others think about it”
(1 = Strongly disagree to 5 = Strongly agree)

Note. All materials are provided in the Qualtrics survey export provided in the OSF folder. INCOM = Iowa-Netherlands comparison orientation
measure; SWLS = satisfaction with life scale; SHS = subjective happiness scale.

Depressive Symptoms. We measured depressive symptoms
using the 10-item short-form version of the Center for Epidemiologic Studies Depression Scale (Radloff, 1977). Respondents indicated the frequency of events in the week prior on

items such as “My sleep was restless,” and “I had trouble
keeping my mind on what I was doing” (0 = “Rarely or none
of the time (less than 1 day)”; 3 = “All of the time (5–7 days)”;
Cole et al., 2004; α = .88).

8
Brooding/Rumination. We measured Brooding/Rumination
using the Brooding subscale (5-item) of the Ruminative
Responses to Depression Questionnaire (Nolen-Hoeksema,
1991; Treynor et al., 2003). Participants rated items such as
“Why can’t I handle things better,” and “What am I doing to
deserve this” (1 = almost never; 4 = almost always), scoring
from 5 to 20 (α = .87).
Loneliness. We measured participants’ loneliness with the 8-item
short-form version of the UCLA Loneliness Scale (Hays &
DiMatteo, 1987; Russell et al., 1980). Participants rated their
agreement with statements such as “I feel left out” and “I lack
companionship” (1 = Never; 4 = Always; α = .88).
Life Satisfaction. We measured participants’ life satisfaction
with the 5-item Satisfaction with Life Scale (SWLS; Diener
et al., 1985). Participants rated five statements (1 = Strongly
disagree; 7 = Strongly agree), such as “So far I have gotten
the important things I want in life,” and “The conditions of
my life are excellent.” (α = .92).
Subjective Happiness. We measured participants’ overall happiness using the 4-item Subjective Happiness Scale (SHS).
Participants rated four statements on a 7-point Likert scale,
with items such as “In general, I consider myself. . .” ranging from “not a very happy person” to “a very happy person”
(Lyubomirsky & Lepper, 1999) (α = .88).
Number of Friends. Participants indicated a rough number of
friends with whom they feel comfortable talking to concerning their personal emotional experiences.
Social Comparison Orientation (Extension). We measured
social comparison orientation using the 11-item Iowa-Netherlands Comparison Orientation Measure (INCOM) (Gibbons & Buunk, 1999), with items such as “I always like to
know what others in a similar situation would do” and “I
often like to talk with others about mutual opinions and
experiences” (5-point scale; 1 = Strongly disagree; 5 =
Strongly agree; α = .73).
Deviations and Replication Closeness Evaluation. We made several adjustments to the target article’s study design. We summarized our deviations with a comparison of study design of
the target article with our replication using the LeBel et al.
(2018) replication closeness evaluation criteria in Table 5.
We categorized the replication as a close to far replication
(see “replication closeness evaluation” in the Supplemental
Material).

Data Analysis Strategy
Replication Measures
Prevalence Estimation Error: Replication Item-Level Analyses. We followed the target’s analysis and used this as our
criteria for a successful replication.

Personality and Social Psychology Bulletin 00(0)
Prevalence Estimation Error: Item-Level One Sample t-Test for
Each Item. In the target article, the authors first aggregated
means of the self-rating of emotional experiences for each of
the six negative and six positive items. They then calculated
an estimation error comparing participants’ estimation of
others’ emotional experiences against the aggregated mean
of all participants’ self-ratings. They then conducted a series
of 12 one-sample t-tests to examine if participants overestimated by comparing estimation error to 0.
Prevalence Estimation Error: Item-Level One-Sample t-Test for
Positive and Negative. The authors also conducted two itemlevel one-sample t-tests, one on the item-level aggregate of
the negative emotional experiences, and another on the itemlevel aggregate of the positive emotional experiences.
Prevalence Estimation Error: Item-Level Independent
Sample t-Test Comparing Positive and Negative. They ran
an item-level independent sample t-test of the average
estimation error for the negative versus the positive
items.
Prevalence Estimation Error: Extension Participant-Level
Analyses. We felt that the replication analysis above could
be improved and therefore supplemented the analysis with
a participant-level analysis, where we first computed the
average for the negative events for each participant, and then
the average for the positive events. We then conducted two
participant-level one-sample t-tests, one on the mean of the
negative emotional experiences, and another on the mean
of the positive emotional experiences. Finally, we ran a participant-level paired sample t-test of the average estimation
error for the negative versus the positive items.
Associations Between Prevalence Estimates of Others and
Well-Being: Replication Analyses. We followed the target’s
analyses by computing each participant’s average peer-prevalence estimates for negative and positive emotional experiences. Thereafter, we conducted a linear regression analysis
to examine the associations between emotional estimation
error and the well-being indicators such as depressive symptoms, rumination, life satisfaction, loneliness, subjective
happiness, and the number of confidants.
Extension Measures
Intensity Estimation Error. We added intensity measures
and conducted all the analyses for prevalence above also for
intensity, for both item-level and participant-level. We also
conducted similar analyses for associations with well-being
measures.
Social Comparison Orientation. We added the social comparison orientation (SCO) measure to all the correlational
and regression analyses detailed above for both prevalence
and intensity, with all the other well-being measures. We
also conducted regression interaction analyses to examine

Ip and Feldman

9

Table 5. Classification of the Replication Based on LeBel et al. (2018).
Design facet

Replication

IV construct

Same, with an
extension
Similar, with an added
extension

DV construct

IV operationalization
DV operationalization

Similar
Similar

Population

Similar

Procedural details

Different

Physical settings

Similar to Study 3

Contextual variables

Similar

Statistical analyses

Similar with additional
analyses that were
more suitable and
tested robustness

Replication
classification

Close to far
replication

Details of deviation

We separated prevalence from intensity, by adjusting
the self-report measure and adding a separate
measure estimating emotional intensity.
We added a social comparison orientation individual
differences measure.

Original: Study 1b conflated emotional prevalence
and intensity. Study 3 stripped intensity.
Replication: Emotional prevalence and emotional
intensity in self were combined into the same
question. For others’ they were rated as two
separate measures—frequency and intensity.
Original: “rejected by a boy or girl” includes gender.
Replication: adjusted to “rejected by someone” for
inclusiveness.
We added measures evaluating views on test items
that were vague, such as “received a bad grade.”
The population in our studies was also students, but
with a larger and more diverse sample from across
the United States.
Original: N = 80 (Study 1b); N = 104 (Study 3)
Replication: N = 594
We added comprehension checks before the rating
task to ensure participants understand (a) target
(self or others), and (b) dimension (prevalence or
intensity).
We randomized the self-other rating order and the
order of scale measures.
We added three clarification questions that inquired
about how they perceived subjective test items.
Original: Study 1b: In person; Study 3: Online.
Replication: Online.
Original studies: Participants were recruited from a
medium-sized West Coast university in the United
States.
Current replication: Our replication was conducted
in 2023 broadly targeting college students across
the United States.
We added participant-level analyses to supplement
the item-level analyses in the original article.
We also conducted moderation analyses.

Reasons for change

We proposed that social
comparison orientation
may interact with the
misperception and
contribute to differential
well-being outcomes.

Ensuring participants read and
understood the instructions
and to rate accordingly.
Address order effects
Address possibility of diverging
perceptions of our test
items.
Target’s Studies 1b and 3 had
similar designs that can be
combined and extended.

Misestimations were
determined based on
estimate-actual comparisons.
To examine the effect of social
comparison orientation.

Mostly followed the target, yet the self-rating
adjustment to measuring both prevalence
and intensity warranted a more conservative
categorization.

whether SCO interacts with estimation error in predicting
well-being measures.
Outliers and Exclusions. We followed the preregistered plan to
only include responses from participants who completed the

entire questionnaire with no further exclusions. We also preregistered that in case we failed to find support for the estimation hypotheses in our replication of the target article, we
would supplement our analyses by rerunning the analyses
with exclusion and stricter alpha to account for multiple

10
analyses (alpha = .005). We found support for misestimation
effects (see below) using the full sample (N = 594) and
therefore did not proceed to conduct or report the analyses
with exclusions.

Results
Prevalence Estimate Errors (Replication)
We summarized descriptives in Tables 6 (prevalence) and 7
(intensity), and correlations in Table 8.
Consistent with Hypothesis 2a and the original findings,
we found support for an underestimation of others’ negative
emotional experiences. Mirroring the analyses conducted in
the original article, we computed the estimation error for
each item of positive and negative experiences by comparing
the self-rating with ratings for their peers. We conducted an
item-level one-sample t-test on the estimation errors to
examine if they differed from 0. We found support for the
expected underestimation of negative (t(5) = −4.30, p =
.008, d = −1.76, 95% CI [−3.06, −0.41]), yet also support for
an unexpected underestimation of positive experiences (t(5)
= −3.67, p = .014, d = −1.50, [−2.67, −0.26]).
We further extended our analyses to a series of participant-level one-sample t-tests on the estimation error of emotional experiences, summarized in Table 6. Overall, we found
support for participants’ underestimation of all six negative
emotional experiences and of five of the six positive emotional experiences. The participant-level analyses mirrored
that of the item-level analyses, with support for the expected
underestimation of negative emotional experiences (M =
23.56; t(593) = −35.02, p < .001, d = −1.44, 95% CI [−1.55,
−1.32]), yet again with the unexpected underestimation of
positive emotional experiences (M = 15.97; t(593) = −24.17,
p < .001, d = −0.99, [−1.09, −0.89]).
Partially consistent with Hypothesis 2b, the underestimation of negative experiences was stronger than positive experiences, yet only for the participant-level analysis. Specifically,
for the item-level analyses, we ran an independent samples
t-test comparing negative and positive emotional experiences
and did not find support for Hypothesis 2b in estimation error
(t(10) = 0.31, p = .31, d = −0.62, 95% CI [−1.79, 0.60]). We
anticipated this in advance, given that the item-level analysis
had too few items and therefore power to detect such differences, which is why we planned and preregistered to also run
participant-level analyses, which we felt were more appropriate and accurate. The participant-level paired-samples t-test
analysis allowed us to find support for a larger estimation error
for negative compared to positive experiences (t(593) =
−11.90, p < .001, d = −0.49, [−0.57, −0.40]).

Intensity Estimate Errors (Extension)
To test whether underestimation of emotional experiences
also extends to emotional intensity, we conducted similar

Personality and Social Psychology Bulletin 00(0)
analyses on the emotional intensity measures. We began with
item-level one-sample t-tests and found support for
Hypothesis 2d with an overestimation of positive experiences (t(5) = 2.97, p = .031, d = 1.21, 95% CI [0.099,
2.26]), yet contrary to our expectations in Hypothesis 2c, we
also found an overestimation for negative experiences (t(5)
= 3.51, p = .017, d = 1.43, [0.23, 2.58]).
The participant-level analyses showed a similar trend, as
we found support for the expected overestimation of positive
emotional experiences (M = 5.60; t(593) = 8.45, p < .001, d
= 0.35, 95% CI [0.26, 0.43]), and again the unexpected
overestimation of negative emotional experiences (M =
12.54; t(593) = 19.42, p < .001, d = 0.80, [0.70, 0.89]). We
ran one-sample t-tests for each of the experiences and found
that participants overestimated five out of the six negative
emotional experiences and five out of the six positive emotional experiences.
Mirroring the target’s analyses for item-level prevalence
comparing positive and negative, we ran the same analysis
for intensity estimates and found no signal for difference in
overestimation error between negative and positive emotional experiences, though large effect size (t(10) = 1.72, p
= .117, d = 0.99, 95% CI [−0.24, 2.18]). Again, this is most
likely due to the small number of items a power analysis of
which shows an unreasonably large effect in order to be
detectable with null hypothesis significance testing. We supplemented the item-level analysis by conducting a participant-level paired sample t-test, which was far better powered,
and indeed found support for stronger overestimation for
negative experiences than for positive experiences (t(593) =
10.63, p < .001, d = 0.44, [0.35, 0.52]).

Prevalence Estimates Associations with WellBeing (Replication)
We summarized the Pearson’s correlations in Table 8, and
the regression model findings predicting well-being from
negative and positive prevalence estimates in Table 9 (comparing with the target’s).
Inconsistent with and opposite to Hypothesis 4, prevalence estimations of negative emotional experiences were
negatively associated with well-being, as indicated by a negative association with life satisfaction (β = −.23, t(591) =
−4.87, p < .001), and subjective happiness (β = −.26, t(591)
= −5.59, p < .001), and a positive association with loneliness (β = .33, t(591) = 7.04, p < .001), brooding (β = .37,
t(591) = 8.08, p < .001), depressive symptoms (β = .41,
t(591) = 9.01, p < .001). On the other hand, positive prevalence estimates were negatively associated with loneliness (β
= −.25, t(591) = −5.40, p < .001), and depressive symptoms
(β = −.22, t(591) = −4.69, p < .001), and positively associated with life satisfaction (β = .25, t(591) = 5.35, p < .001),
subjective happiness (β = .32, t(591) = 6.73, p < .001), and
the number of confidants (β = .19, t(591) = 3.98, p < .001).

11

46.08

69.64

−23.56

−7.57
−42.85

−10.09

53.68
63.31

72.90

94.44

70.85

Overall positive

70.24

50.48

−19.77

3.8

−15.97

−31.22

−19.22

−11.66

1.01
−14.59

14.27

8.7

16.10

22.00

22.47

19.63

24.04
22.64

24.35

8.7

16.40

23.60
23.02

22.11

22.29

23.54
22.61

Error
SD

−33.75

—

−24.17

−34.49

−20.84

−14.48

1.03
−15.70

−20.27

—

−35.02

−7.82
−45.36

−11.12

−26.49

−24.47
−35.60

t-stat

−0.46 [−0.54, −0.37]
−0.32 [−0.40, 0.24]
−1.86 [−1.99, −1.73]

−1.44 [−1.55, −1.32]

<.001
<.001
<.001
<.001

−0.86 [−0.95, 0.76]
−1.42 [−1.53, −1.30]
−0.99 [−1.09, −0.89]

<.001
<.001
<.001

<.001

−1.38 [−1.50, −1.27]

—

−0.59 [−0.68, −0.51]

<.001

—

0.04 [−0.04, 0.12]
−0.64 [−0.73, 0.56]

−0.83 [0.92, 0.74]

.305
<.001

<.001

—

−1.09 [−1.19, −0.99]

<.001

—

−1.00 [−1.10, −0.90]
−1.46 [−1.58, −1.34]

Cohen’s d and CI

<.001
<.001

p

—

Unexpected signal; same
direction
Unexpected no signal;
Signal; opposite
direction
Signal; opposite
direction
Signal; opposite
direction
Unexpected signal; same
direction
Unexpected signal

—

Signal; same direction

Signal; same direction
Signal; same direction

Signal; same direction

Signal; same direction

Signal; same direction
Signal; same direction

Interpretation

−5.61

1.06

−3.67

/

/

/

/
/

/

−5.99

−4.30

/
/

/

/

/
/

t-stat

/

/

/

/
/

/

11

5

5

5

5

/
/

/

/

/
/

df

<.001

n.s

.014

/

/

/

/
/

−1.62 [−2.48, −.73]

0.48 [−0.48, 1.38]

−1.50 [−2.67, −0.26]

/

/

/

/
/

/

−2.68 [−4.65, −0.68]

<.01
/

−1.76 [−3.06, −0.41]

/
/

/

/

/
/

Cohen’s d and CI

.008

/
/

/

/

/
/

p

Item-level analysis

Note. One-sample t-tests, N = 594, participant level df = 593, 95% CI (confidence intervals). The interpretation of the outcome is based on LeBel et al. (2019). We summarized whether we found
a signal (p < alpha), whether the signal or lack of was expected, and whether it was in the same direction as that of the target’s per the singular item. We did not summarize effect size consistency
(target’s effect within replication confidence intervals) given that we conducted analyses on participant level whereas the target conducted analysis on item-level.

Overall positive and
negative combined

Overall positive: target article (Study 3)

65.61

77.27

54.87

52.36
39.45

51.35
54.04

  Attended fun party
  Participated in
athletics
  Went out with
friends
Talked to distant
friends/ family
   Had great meal

75.08

−20.35

42.60
42.51

50.17
85.35

54.84

36.04

46.13

−24.22

Positive experiences
  Received high grade

62.14

86.36

−23.63
−33.02

Error
mean

−21.4

47.41
45.76

71.04
78.79

Prevalence
estimate
mean

Overall negative: target article (Study 3)

Negative experiences
   Had fight/argument
   Thought about
distant friends/
family
   Thought about
enormous
workload
   Was rejected by
boy/girl
   Received low grade
   Thought about bad
personal health
habits
Overall negative

Experiences

Actual
prevalence
average

Participant-level analyses

Table 6. Prevalence of Emotional Experiences (Replication and Extension): One-Sample t-Tests of Estimation Error.

12
59.64
48.48
60.44
58.16
53.65
45.07
54.24
60.56
62.65
49.35
67.65
57.96
57.03
59.20
56.72

42.63
42.65
50.81
33.00
37.05
44.07
41.70
56.07
50.63
44.95
59.11
52.03
58.80
53.60
47.65

Intensity
estimate
mean

4.50
12.03
4.40
8.54
5.93
−1.77
5.60
9.07

12.54

17.01
5.84
9.63
25.17
16.60
1.01

Error
mean

22.39
21.35
22.16
19.47
20.29
23.40
16.16
13.83

15.73

22.27
21.48
22.13
23.94
22.46
21.93

Error
SD

Note. One-sample t-tests, N = 594, participant level df = 593, 95% CI (confidence intervals).

Negative experiences
Had fight/argument
Thought about distant friends/ family
Thought about enormous workload
Was rejected by boy/girl
Received low grade
   Thought about bad personal health
habits
Overall negative
Positive experiences
   Received high grade
   Attended fun party
   Participated in athletics
   Went out with friends
   Talked to distant friends/family
   Had great meal
Overall positive
Overall: positive and negative combined

Experiences

Actual
intensity
average

4.90
13.73
4.84
10.69
7.12
−1.85
8.45
15.99

19.42

18.62
6.62
10.60
25.62
18.01
1.12

t-stat

Participant-level analyses

Table 7. Intensity of Emotional Experiences [Extension]: One-Sample t-Tests of Estimation Error.

0.76 [0.67, 0.86]
0.27 [0.19, 0.35]
0.43 [0.35, 0.52]
1.05 [0.95, 1.15]
0.74 [0.65, 0.83]
0.05 [−0.03, 0.13]
0.80 [0.70, 0.89]
0.20 [0.12, 0.28]
0.56 [0.48, 0.65]
0.20 [0.12, 0.28]
0.44 [0.35, 0.52]
0.29 [0.21, 0.37]
−0.08 [−0.16, 0.004]
0.35 [0.26, 0.43]
0.66 [0.57, 0.74]

<.001
<.001
<.001
<.001
<.001
<.001
=.065
<.001
<.001

Cohen’s d and CI

<.001
<.001
<.001
<.001
<.001
=.264

p

/
/
/
/
/
/
2.97
4.14

3.51

/
/
/
/
/
/

t-stat

/
/
/
/
/
/
5
11

5

/
/
/
/
/
/

df

/
/
/
/
/
/
.031
.002

.017

/
/
/
/
/
/

p

/
/
/
/
/
/
1.21 [0.099, 2.26]
1.20 [0.43, 1.90]

1.43 [0.23, 2.58]

/
/
/
/
/
/

Cohen’s d and CI

Item-level analyses

13

11.44

10.28

20.84

17.15

9—Brooding

10—Depression

11—Life satisfaction

12—Subjective happiness

5.55

7.75

6.60

3.95

5.97

.88

.92

.88

.87

.88

.67

1

0.54***
[0.48, 0.60]
0.88***
[0.86, 0.90]
0.58***
[0.53, 0.63]
0.32***
[0.25, 0.39]
0.52***
[0.46, 0.58]
0.21***
[0.13, 0.28]
0.19***
[−0.11, 0.27]
0.30***
[.22, .37]
0.30***
[0.22, 0.37]
−0.09*
[−0.17, −0.13]
−0.09*
[−0.17, −0.11]

Note. df = 592. SCO = social comparison orientation.
*
p < .05. **p < .01. ***p < .001.

18.36

8—Loneliness

6.27

—

9.07 13.83

35.30

—

16.2

59.2

—

15.7

—

50.47 14.27

54.2

—

16.1

54.9

—

Alpha

16.4

SD

46.1

M

4—Intensity estimates of
negative emotions (extension)
5—Intensity estimates of
positive emotions (extension)
6—Intensity estimation error
overall (extension)
7—SCO (extension)

1—Prevalence estimates for
negative emotions
2—Prevalence estimates for
positive emotions
3—Prevalence estimates overall

Variable

0.88***
[0.86, 0.89]
0.35***
[0.28, 0.42]
0.63***
[0.58, 0.67]
0.57***
[0.51, 0.62]
0.04
[−0.04, 0.12]
−0.07
[−0.15, 0.01]
0.06
[−0.02, 0.14]
0.01
[−0.07, 0.09]
0.13*
[0.05, 0.21]
0.17***
[0.09, 0.25]

2

0.53***
[0.47, 0.59]
0.54***
[0.48, 0.59]
0.62***
[0.47, 0.67]
0.14***
[0.06, 0.22]
0.07
[−0.01, 0.15]
0.20***
[0.13, 0.28]
0.18***
[0.10, 0.25]
0.02
[−0.06, 0.10]
0.05
[−0.04, 0.13]

3

0.50***
[0.44, 0.56]
0.86***
[0.84, 0.88]
0.14***
[0.06, 0.22]
0.18***
[0.10, 0.26]
0.17***
[0.09, 0.25]
0.19***
[0.11, 0.27]
−0.05
[−0.13, 0.03]
−0.10*
[−0.18, −0.02]

4

6

0.87***
[0.85, 0.89]
0.08
0.12**
[−0.01, 0.16] [0.04, 0.20]
−0.07
0.06
[−0.15, 0.01] [−0.02, 0.14]
0.02
0.11**
[−0.07, 0.10] [0.03, 0.19]
−0.05
0.08*
[−0.13, 0.03] [0.00, 0.16]
0.15***
0.06
[0.07, 0.23] [−0.02, 0.14]
0.20***
0.06
[0.12, 0.28] [−0.02, 0.14]

5

7

8

9

0.28***
[0.20, 0.35]
0.45***
0.56***
[0.38, 0.51]
[0.51, 0.62]
0.32***
0.72***
0.69***
[0.25, 0.39]
[0.68, 0.75]
[0.64, 0.73]
−0.17***
−0.54***
−0.45***
[−0.25, −0.09] [−0.59, −0.48] [−0.52, −0.39]
−0.18***
−0.65***
−0.47***
[−0.26, −0.11] [−0.69, −0.60] [−0.53, −0.40]

Table 8. Correlations Between Prevalence and Intensity Estimates with Well-Being and Social Comparison Measures.

−0.55***
[−0.60, −0.49]
−0.65***
[−0.69, −0.60]

10

0.65***
[0.61, 0.70]

11

14

Personality and Social Psychology Bulletin 00(0)

Table 9. Prevalence Estimates Regression Coefficients with Outcomes: Comparison Between Jordan et al. (2011)’s Study 3 Versus
Replication.
Jordan et al. (2011)

Replication

R2

β for negative
prevalence
estimate

β for positive
prevalence
estimate

Measure

Cronbach’s α

β for negative
prevalence
estimate

Negative
Loneliness

.81

−.30**

.08

.08

.33***

−.25***

Rumination/brooding

.72

−.28**

.17

.08

.37***

−.15**

Depressive symptoms

.73

.00

−.02

.00

.41***

−.22***

Positive
Satisfaction with life

.84

.23*

−.37***

.15

−.23***

.25***

Subjective Happiness

.80

.19

−.16

.05

−.26***

.32***

Number of confidants
Social orientation scale
(extension)

/
/

N/A
/

N/A
/

N/A
/

−.11*
.26***

.19***
−.10*

β for positive
prevalence
estimate

R2

Interpretation

.08 Signal; inconsistent
opposite
.10 Signal; inconsistent
opposite
.12 Signal; inconsistent
opposite
.05 Signal; inconsistent
opposite
.08 Signal; inconsistent
opposite
.03
.05 Supported

Note. Linear regression, N = 594. N/A = not reported.
Source. The “original” column was adopted from Jordan et al. (2011, p. 130). The interpretation of the outcome is based on LeBel et al. (2019).
*
p < .05. **p < .01. ***p < .001.

We conclude that our findings are in the opposite direction to
that reported in the target article, for both negative and positive emotional experiences prevalence estimates.

Intensity Estimates Associations with Well-Being
(Extension)
We conducted the same analyses on intensity estimates as the
ones reported above for prevalence, and summarized those in
Tables 8 and 10. We found a similar pattern, with support for
a positive association between negative emotional experiences intensity estimates and loneliness (β = .29, t(591) =
6.21, p < .001), brooding (β = .22, t(591) = 4.74, p < .001),
and depressive symptoms (β = .29, t(591) = 6.31, p < .001),
and negative association with life satisfaction (β = −.18,
t(591) = −3.79, p < .001), and subjective happiness (β =
−.28, t(591) = −6.07, p < .001).
On the other hand, we found that positive emotional experiences intensity had a negative association with loneliness
(β = −.21, t(591) = −4.63, p < .001) and depressive symptoms (β = −.20, t(591) = −4.23, p < .001).

Complementary Analysis: Self-Reports
Associations with Well-Being (Exploratory
Extension)
We explored the associations between self-reports of emotional experiences and well-being measures. We found that
reporting more of the negative emotional experiences was
positively correlated with loneliness (r = .16 [0.08, 0.23]),

brooding (r = .25 [0.17, 0.32]), and depression (r = .26
[0.19, 0.34]), and that reporting more the listed positive emotional experiences was positively correlated with life satisfaction (r = .22 [0.14, 0.29]) and happiness (r = .27 [0.20,
0.35]) and negatively associated with loneliness (r = −.16
[−0.23, −0.07]; all p < .001).

Complementary Analysis: Interaction Between
Self and Others in Predicting Well-Being
(Exploratory Extension)
We also explored interactions between one’s own negative
experiences and estimates of others’ negative experiences in
predicting well-being, and found support for an interaction
for both loneliness, brooding, and depression, such that the
positive association between prevalence estimates and negative factors of well-being was stronger the less negative
experiences one had. We also found support for an interaction with happiness and well-being, such that the negative
association between prevalence estimates and positive factors of well-being was stronger, the less negative experiences
one had. We did not find such interactions for the positive
experiences.

External Analysis: Suppression Using Target’s
Analyses
When consulting with external expert reviewers to examine
the possible explanation for our associations being in the
opposite direction from that of the target article, a reviewer

Ip and Feldman

15

Table 10. Intensity Estimates Regression Coefficients with
Outcomes (Extension).

Measure

no support, we only summarized interactions that were supported and documented below p < .05, yet we strongly caution against over-interpreting those and recommend focusing
on the much stronger and clearer main effects.

β for negative
prevalence
estimate

β for positive
prevalence
estimate

R2

.29***
.22***
.29***

−.21***
−.10*
−.20***

.07
.04
.07

−.18***
−.28***
−.07
.13**

.24***
.34***
.15**
.01

.05
.10
.02
.02

Negative well-being
Loneliness
Rumination/brooding
Depressive symptoms
Positive well-being
Satisfaction with life
Subjective happiness
Number of confidants
Social orientation scale
(extension)

Discussion

Note. Linear regression, N = 594.
*
p < .05. **p < .01. ***p < .001.

was kind to review the target article’s and our results. In his
analyses (shared on https://osf.io/zy5qa/), he pointed out that
there might be suppression in the regression analyses (Sharpe
& Roberts, 1997; Thompson & Levine, 1997) that were conducted by the target article and that we repeated in our replication. His conclusion was that the regression analyses
should at best be interpreted with caution, and that interpretations should be focused on the raw correlation effects,
which were weaker yet in the same direction (see Table 8).
We reached out to the original authors of the target article,
and received the reply that the raw data, analysis code, and
correlations for the target article’s studies are not available.
However, the reviewer, the original authors, and we agreed
that suppression alone is not likely to explain the complete
reversal of the pattern of effects.

SCO (Exploratory Extension)
We conducted Pearson’s correlation tests to examine the
associations between SCO and well-being variables, and
summarized them in Table 8. We found support for
Hypotheses 6a to 6c, in that SCO was positively associated
with depressive symptoms (r(592) = .32, 95% CI [0.25,
0.39], p < .001), loneliness (r(592) = .28, [0.20, 0.35],
p < .001), and rumination (r(592) = .45, [0.38, 0.51],
p < .001). We also found support for Hypotheses 6d and 6e
that SCO was negatively associated with subjective happiness (r(592) = −.18, [−0.26, −0.11], p < .001), and life satisfaction (r(592) = −.17, [−0.25, −0.09], p < .001).
We also examined interactions with the misestimation of
emotional experiences in predicting psychological wellbeing. We failed to find support for SCO as moderating the
associations between misestimations and well-being measures. We summarized the results with plots of the moderation analysis in the Supplemental Material. Given the many
analyses conducted and the many findings that have shown

We conducted a preregistered replication of Studies 1b and 3
from Jordan et al. (2011) and tested their theoretical framework, measurement, and analysis strategy with a larger sample. We also went beyond the target article by adding additional
tests that seem fit for the experimental design, and by adding
extensions testing for the generalizability to emotional experience intensity and examining SCO.
Our results were mixed, and we summarized a comparison of our findings to that of the target in Tables 6 and 9. We
concluded a successful replication only regarding people’s
underestimation of the prevalence of others’ negative emotions, yet with an unexpected underestimation of the prevalence of positive emotions. In addition, we only observed
differences between the underestimation of positive and
negative emotional experiences when conducting the betterpowered participant-level analysis, yet not when repeating
that target’s item-level analysis, likely due to the small number of items and the analysis being underpowered.
Most surprising was that we found opposite effects to the
target’s theory and findings regarding the associations
between prevalence estimates and psychological well-being
factors. Based on the target’s findings, we expected higher
estimations for prevalence of negative emotions to be positively associated with higher well-being, and instead, we
found support for a negative relationship. We discuss possible explanations for the mixed findings, followed by a discussion of limitations and suggestions for future research
directions.

Replication: Prevalence
We conclude mixed findings concerning our replication of
the systematic misperception of others’ emotional experiences. We found that (a) people underestimated the prevalence of others’ negative and positive emotional experiences,
(b) underestimation errors were stronger for negative experiences than for positive experiences, (c) prevalence estimation of others’ negative emotions was positively associated
with loneliness, rumination, depressive symptoms, and negatively associated with life satisfaction and subjective happiness, (d) prevalence estimation of others’ positive emotions
was positively associated with life satisfaction and subjective happiness and negatively associated with loneliness,
rumination, and depressive symptoms.
Jordan et al. (2011) argued that underestimation was
mainly about negative emotional events, yet instead, we
found that participants also underestimated positive emotional experiences, albeit to a lesser extent than they did for

16
negative events. This supports a needed reframing of their
null hypothesis that we suggested in our preregistration
(Hypothesis 2b; see Table 2), that instead of null effects for
positive emotions, the hypothesis could be that the underestimation of positive events is weaker than that of negative
events. We note, however, that there would still be an unexplained inconsistency with the target article’s Study 2, which
extended the idea from Study 1, and using a preexisting dataset from Srivastava et al. (2009) showed very large differences in misestimation between negative and positive
emotions, such that negative emotions were underestimated
(8 out of 9 emotions), compared to an overestimation of positive emotions (7 out of 8 emotions). We therefore see much
value in conducting also a follow-up replication of the target’s Study 2 with possible theoretical and empirical extensions that would try and resolve the differences in results.

Prevalence Estimations and Well-Being
We failed to find support for the findings regarding correlates of prevalence estimates with well-being measures of
loneliness, rumination, depressive symptoms, life satisfaction, and subjective happiness. Instead, we found support for
the opposite effects of those reported in the target article.
It is difficult to resolve the inconsistent findings regarding the associations between prevalence estimations and
well-being. We reached out to the target article’s authors to
consult with them regarding the diverging findings (May,
2023), and we were unable to identify the reason for the
divergence.
We note that in our view there was a misalignment
between the theoretical framework and the analyses performed in the target article that may at least partly account
for the contradictory replication findings. Jordan et al. (2011)
argued that feeling alone in negative emotional experiences
may lead to the feeling that negative emotions are less common and subsequently pathologize their experiences, causing negative psychological impact. Consistent with the
target’s findings, Whillans et al. (2017) found that first-year
arrivals who perceived themselves as less socially connected
than their peers reported lower belonging and well-being.
However, the methods in the target article are not fully
aligned with the testing of their main theory, since the main
argument seems to refer to a comparison between self and
others’ emotional experiences, which differs from the main
effect correlational analyses that they reported to test their
hypotheses. The original analyses may have stemmed from
an assumption that the rating person had experienced the
negative emotional events before and so perceiving others as
also experiencing those negative events helped them feel less
alone. However, an alternative scenario might be that the rating person had not experienced the negative emotional event
before, and so thinking that negative experiences were common contributed to them feeling more alone. In our replication analyses, we first simply followed their methodology

Personality and Social Psychology Bulletin 00(0)
and data analysis strategy, yet a more suitable data analysis
would have been to examine the interaction between one’s
own experiences and one’s perception of others and its association with factors like loneliness. We added exploratory
analyses that indeed suggest that self-reports and estimates
of others’ negative emotional experiences may interact in
predicting well-being factors, such that having fewer negative experiences makes viewing more others as having negative experiences predict one’s own lower well-being.
We therefore offer several suggested hypotheses to be
confirmed in future studies: (a) feeling alone in emotional
experiences is negatively associated with well-being, (b) for
those who have experienced negative emotions, a lower
prevalence estimate makes them feel as struggling alone and
hence be associated with poorer well-being, and (c) for those
who have not experienced negative emotions, a higher prevalence estimate of negative emotions is associated with
poorer well-being. Given the correlational methods, the
causal direction remains unclear and future research may try
to further explore causality in the interplay between prevalence estimations and well-being.

Extension: Intensity
We ran extensions examining if the underestimations extend
to an underestimation of others’ emotional intensity. Our
findings showed that (a) people overestimate the intensity of
others’ negative emotional experiences, (b) people overestimate the intensity of others’ positive emotional experiences,
but to a lesser extent than for negative emotions. This
diverged from our initial predictions that misestimation of
intensity would differ for negative versus positive, and
should therefore be subjected to further confirmation with
additional research.

Challenging and Reframing Misestimation:
Prevalence and Intensity, Positive and Negative
Our findings challenge the target article’s findings in several
important ways. First, the effects seem to encompass both
positive and negative emotional events, yet with stronger
prevalence underestimations for negative than for positive
emotional events. We also found that misestimation of others’
emotions extends to misestimation of others’ emotional intensity, yet, crucially, in opposite directions. Our findings suggest
that people underestimate prevalence yet at the same time
overestimate intensity. If that is indeed the case, then the story
shifts from people not being sensitive enough to others’ positive emotions to people sensing fewer but stronger intensity
instances of both negative and positive emotions.
One direction is focusing on the broadcasting side of
interpersonal exchanges, on how people exhibit their emotional experiences. It could be that estimations are accurate
and effectively capture what people indeed exhibit—that
people suppress or are able to hide low intensity emotional

Ip and Feldman
events (Srivastava et al., 2009) yet are less able to suppress
their high intensity emotional events.
A different possibility all together is focusing on the
receiving side, in that it might not be at whether emotional
experiences occur in solitude or how they are exhibited, but
rather more about how we sense, code, remember, and recall
social information about emotional experiences, in that people tend to focus on, respond to, and remember the strongest
emotional events. People rely on vivid memories of past
expressions of emotional experiences (Doré et al., 2016),
which tend to be events that are emotionally intense
(Yonelinas & Ritchey, 2015). Hence, the most salient expressions may color their evaluations of others’ experiences.
While they may not recall many instances, leading to an
underestimate of prevalence, the ones that are recalled are
the most vivid ones, leading to an overestimation of
intensity.

Extension: SCO
We found support for associations between SCO, misestimations, and well-being: (a) SCO was positively associated
with depressive symptoms level, loneliness, and brooding,
(b) SCO was negatively associated with life satisfaction and
subjective happiness, and (c) SCO was positively associated
with prevalence estimates and intensity estimates overall.
These associations are in line with the wealth of literature
suggesting that SCO, which is mostly upward social comparison (Festinger, 1954), created a discrepancy between the ideal
self-presented to others and the real self (Yu & Kim, 2020).
This induced a sense of inferiority and distress as these comparisons maintain or even exacerbate negative self-evaluations (Vogel et al., 2015). As a result, these negative
evaluations about oneself were found to cause detrimental
effects on well-being characterized by lower life satisfaction
and lower subjective well-being (Verduyn et al., 2015).
We predicted yet found no support for the idea that SCO
interacts with the association between misestimation and
psychological well-being. This may be suggestive of the
story being less about comparisons between self and others
and more about how people communicate or receive socialemotional information, though null effects should be interpreted with caution and humility. Future research can
further contrast social comparisons with social attention/
awareness and/or empathy as impacting emotional event
misestimation.

Limitations and Future Directions
We note several limitations in our current replication, which
may partly explain some of our diverging findings. We noted
several weaknesses we spotted in the target’s analyses and
adjustments we made to address those (see Table 5 for the
summary of deviations), and so it is possible that one of

17
those affected the findings. For example, we followed the
target’s method and their items as closely, and yet, some
items were specific about an emotion regarding a specific
event (e.g., feeling sad because of a low grade), and it is possible that the estimate of the frequency of the emotions was
based on estimates of the frequency of the event and that for
the described events there were other emotions that were
more relevant than those that the authors had in mind (e.g.,
feeling shame rather than sadness over a low grade). To
avoid that, future research may aim to separate context from
emotions, to help gain a more accurate understanding of the
cause for misestimation.
We note that we adopted the same emotional experiences
items that appeared in the original article, trying to stay as
close as possible to the setting of the original article. Yet, the
procedure in the target article included a study aiming to generate the items, meant to adjust the items to be as relevant as
possible to the target sample. Though we were careful to run
the study with a sample of students that would find the experiences listed relevant, it could be that not following the
whole procedure may have affected the findings somehow.
We do not think this to be a major issue, given the strong support for the targets’ findings regarding prevalence underestimation, which suggests these were suitable.
Our sample was similar to the target article’s sample with
U.S. American students, yet with a more diverse and more
heterogeneous population recruited online on Prolific compared to the target article’s sample which was recruited from
a single school. It is possible that this change may have
impacted our findings in some way and that there are some
differences (sociocultural, economics, etc.), which would on
the one hand show that the consistent findings are generalizable, yet that the heterogeneity may have led to some of the
inconsistent findings. We believe that research should aim
for more diverse and heterogenous samples to go beyond a
singular context, sample, or point in time, in order to maximize impact and practical use. Future research is needed to
examine the generalizability of the phenomenon and retest
our and the target article’s methods in other contexts.

Conclusion
Overall, we found only partial support for the findings of
Jordan et al. (2011) research regarding misestimation of
the prevalence of others’ emotional experiences.
Consistent with the original article, we found support for
an underestimation of the prevalence of others’ negative
and positive emotional experiences. However, inconsistent with the target article, we found that the estimation of
negative emotions prevalence was positively associated
with loneliness, rumination, and depressive symptoms,
and negatively associated with life satisfaction and subjective happiness. On the other hand, the estimation of
positive emotions prevalence was positively associated

18
with life satisfaction and subjective happiness and negatively associated with loneliness, rumination, and depressive symptoms. We also ran an extension examining
estimation of others’ emotional intensity, and unexpectedly found that people tended to overestimate others’ positive and negative emotional intensity. In another
extension, we found SCO as associated with misestimation and well-being, yet does not moderate the link
between prevalence estimations and well-being.
Acknowledgments
We thank Nikolay Petrov (ORCID: 0000-0002-1305-0547) and
Hirotaka Imada (ORCID: 0000-0003-3604-4155) for their help
with checking and verifying our analyses and thinking about the
surprising associations. We are very grateful for their insights.

Authorship Declaration
Ho Ching Ip conducted the replication as part of her thesis.
Gilad Feldman was the thesis supervisor and led the replication
efforts. He supervised each step in the project, conducted the preregistrations, ran data collection, revised and edited the manuscript
for final submission.

Author Contributions
Ho Ching Ip: Conceptualization; Pre-registration; Formal analysis;
Investigation; Methodology; Software; Visualization; Writing –
original draft.
Gilad Feldman: Conceptualization; Preregistration; Data curation;
Funding acquisition; Preregistration peer review/verification; Data
analysis peer review/verification; Project administration; Resources;
Supervision; Validation; Writing – review and editing.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
research was supported by the University of Hong Kong seed funding awarded to Gilad Feldman.

ORCID iDs
Ho Ching IP
https://orcid.org/0000-0002-9549-7544
Gilad Feldman
https://orcid.org/0000-0003-2812-6599

Supplemental Material
Supplemental material is available online with this article.

Note
1. This is our reframing of the target’s original framing claiming no
misestimation of positive emotions, a null hypothesis, more on
that below.

Personality and Social Psychology Bulletin 00(0)
References
Alfasi, Y. (2019). The grass is always greener on my Friends’ profiles:
The effect of Facebook social comparison on state self-esteem
and depression. Personality and Individual Differences, 147,
111–117. https://doi.org/10.1016/j.paid.2019.04.032
Allport, F. H. (1924). Social psychology. Riverside Press.
Burke, M., & Kraut, R. (2013, February). Using Facebook after
losing a job: Differential benefits of strong and weak ties
[Conference session]. Proceedings of the 2013 Conference
on Computer Supported Cooperative Work, pp. 1419–1430.
https://doi.org/10.1145/2441776.2441936
Cole, J. C., Rabin, A. S., Smith, T. L., & Kaufman, A. S. (2004).
Development and validation of a Rasch-derived CES-D short
form. Psychological Assessment, 16(4), 360. https://doi.
org/10.1037/1040-3590.16.4.360
Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and
Social Psychology, 8(4), 377–383.
Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The
satisfaction with life scale. Journal of Personality Assessment,
49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13
Doré, B. P., Meksin, R., Mather, M., Hirst, W., & Ochsner, K. N.
(2016). Highly accurate prediction of emotions surrounding the
attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals. Journal of Experimental Psychology: General,
145(6), 788. https://doi.org/10.1037/xge0000168
Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal
behavior: Categories, origins, usage, and coding. Semiotica,
1(1), 49–98. https://doi.org/10.1515/semi.1969.1.1.49
Feldman, G. (2023). Registered report stage 1 manuscript template.
https://doi.org/10.17605/OSF.IO/YQXTP
Festinger, L. (1954). A theory of social comparison processes.
Human Relations, 7, 117–140. https://doi.org/10.1177/0018
72675400700202
Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in
social comparison: Development of a scale of social comparison orientation. Journal of Personality and Social Psychology,
76(1), 129. https://doi.org/10.1037/0022-3514.76.1.129
Hays, R. D., & DiMatteo, M. R. (1987). A short-form measure of
loneliness. Journal of Personality Assessment, 51(1), 69–81.
https://doi.org/10.1207/s15327752jpa5101_6
Jané, M., Xiao, Q., Yeung, S., Ben-Shachar, M. S., Caldwell, A.,
Cousineau, D., Dunleavy, D. J., Elsherif, M., Johnson, B.,
Moreau, D., Riesthuis, P., Röseler, L., Steele, J., Vieira, F.,
Zloteanu, M., & Feldman, G. (2024). Guide to effect sizes
and confidence intervals. http://doi.org/10.17605/OSF.IO/
D8C4G
Jordan, A. H., Monin, B., Dweck, C. S., Lovett, B. J., John, O. P.,
& Gross, J. J. (2011). Misery has more company than people
think: Underestimating the prevalence of others’ negative
emotions. Personality and Social Psychology Bulletin, 37(1),
120–135.
Larson, R., Csikszentmihalyi, M., & Graef, R. (1982). Time alone
in daily experience: Loneliness or renewal. In: L. A. Peplau &
D. Perlman (Eds.), Loneliness: A sourcebook of current theory,
research and therapy (pp. 40–53). John Wiley & Sons.
LeBel, E. P., McCarthy, R. J., Earp, B. D., Elson, M., & Vanpaemel,
W. (2018). A unified framework to quantify the credibility

Ip and Feldman
of scientific findings. Advances in Methods and Practices in
Psychological Science, 1, 389–402.
LeBel, E. P., Vanpaemel, W., Cheung, I., & Campbell, L. (2019). A
brief guide to evaluate replications. Meta-Psychology, 3, 1–9.
https://doi.org/10.15626/MP.2018.843
Liu, P., He, J., & Li, A. (2019). Upward social comparison on social
network sites and impulse buying: A moderated mediation model
of negative affect and rumination. Computers in Human Behavior,
96, 133–140. https://doi.org/10.1016/j.chb.2019.02.003
Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46, 137–155. https://doi.
org/10.1023/A:1006824100041
Miller, D. T., & Prentice, D. A. (1994). Collective errors and
errors about the collective. Personality and Social Psychology
Bulletin, 20(5), 541–550.
Nolen-Hoeksema, S. (1991). Responses to depression and their
effects on the duration of depressive episodes. Journal of
Abnormal Psychology, 100(4), 569. https://doi.org/10.1037/
0021-843X.100.4.569
Nosek, B. A., Hardwicke, T. E., Moshontz, H., Allard, A., Corker,
K. S., Dreber, A., Fidler, F., Hilgard, J., Struhl, M. K., Nuijten,
M. B., Rohrer, J. M., Romero, F., Scheel, A. M., Scherer, L. D.,
Schönbrodt, F. D., & Vazire, S. (2022). Replicability, robustness, and reproducibility in psychological science. Annual
Review of Psychology, 73(1), 719–748.
Palan, S., & Schitter, C. (2018). Prolific. Ac—A subject
pool for online experiments. Journal of Behavioral and
Experimental Finance, 17, 22–27. https://doi.org/10.1016/j.
jbef.2017.12.004
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale
for research in the general population. Applied Psychological
Measurement, 1(3), 385–401. https://doi.org/10.1177/0146621
67700100306
Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised
UCLA Loneliness Scale: concurrent and discriminant validity
evidence. Journal of Personality and Social Psychology, 39(3),
472. https://doi.org/10.1037/0022-3514.39.3.472
Sargent, R. H., & Newman, L. S. (2021). Pluralistic ignorance research in psychology: A scoping review of topic
and method variation and directions for future research.
Review of General Psychology, 25(2), 163–184. https://doi.
org/10.1177/1089268021995168
Schanck, R. L. (1932). A study of a community and its groups
and institutions conceived of as behaviors of individuals. Psychological Monographs, 43(2), i–133. https://doi.
org/10.1037/h0093296
Sharpe, N. R., & Roberts, R. A. (1997). The relationship among
sums of squares, correlation coefficients, and suppression. The
American Statistician, 51(1), 46–48.
Simonsohn, U. (2015). Small telescopes: Detectability and the
evaluation of replication results. Psychological Science, 26(5),
559–569.
Steers, M. L. N., Wickham, R. E., & Acitelli, L. K. (2014).
Seeing everyone else’s highlight reels: How Facebook usage
is linked to depressive symptoms. Journal of Social and
Clinical Psychology, 33(8), 701–731. https://doi.org/10.1521/
jscp.2014.33.8.701

19
Srivastava, S., Tamir, M., McGonigal, K. M., John, O. P., &
Gross, J. J. (2009). The social costs of emotional suppression: a prospective study of the transition to college. Journal
of Personality and Social Psychology, 96(4), 883–897. https://
doi.org/10.1037/a0014755
Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use,
envy, and depression among college students: Is Facebooking
depressing? Computers in Human Behavior, 43, 139–146.
https://doi.org/10.1016/j.chb.2014.10.053
Thompson, F. T., & Levine, D. U. (1997). Examples of easily explainable suppressor variables in multiple regression
research. Multiple Linear Regression Viewpoints, 24(1),
11–13.
Treynor, W., Gonzalez, R., & Nolen-Hoeksema, S. (2003).
Rumination reconsidered: A psychometric analysis.
Cognitive Therapy and Research, 27, 247–259. https://doi.
org/10.1023/A:1023910315561
Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A.,
Bayer, J., Ybarra, O., Jonides, J., & Kross, E. (2015).
Passive Facebook usage undermines affective wellbeing: Experimental and longitudinal evidence. Journal
of Experimental Psychology. General, 144(2), 480–488.
https://doi.org/10.1037/xge0000057
Vogel, E. A., Rose, J. P., Roberts, L. R., & Eckles, K. (2014).
Social comparison, social media, and self-esteem. Psychology
of Popular Media Culture, 3(4), 206.
Vogel, E. A., Rose, J. P., Okdie, B. M., Eckles, K., & Franz, B.
(2015). Who compares and despairs? The effect of social
comparison orientation on social media use and its outcomes.
Personality and Individual Differences, 86, 249–256. https://
doi.org/10.1016/j.paid.2015.06.026
Wang, J. L., Wang, H. Z., Gaskin, J., & Hawk, S. (2017). The mediating roles of upward social comparison and self-esteem and
the moderating role of social comparison orientation in the
association between social networking site usage and subjective well-being. Frontiers in Psychology, 8, 771. https://doi.
org/10.3389/fpsyg.2017.00771
Whillans, A. V., Christie, C. D., Cheung, S., Jordan, A. H., &
Chen, F. S. (2017). From misperception to social connection: Correlates and consequences of overestimating others’
social connectedness. Personality and Social Psychology
Bulletin, 43(12), 1696–1711. https://doi.org/10.1177/014616
7217727496
Yonelinas, A. P., & Ritchey, M. (2015). The slow forgetting of
emotional episodic memories: An emotional binding account.
Trends in Cognitive Sciences, 19(5), 259–267. https://doi.
org/10.1016/j.tics.2015.02.009
Young, S. D., & Jordan, A. H. (2013). The influence of social networking photos on social norms and sexual health behaviors.
Cyberpsychology, Behavior, and Social Networking, 16(4),
243–247. https://doi.org/10.1089/cyber.2012.0080
Yu, E., & Kim, H. (2020). Is she really happy? A dual-path model
of narcissistic self-presentation outcomes for female Facebook
users. Computers in Human Behavior, 108, 106328. https://doi.
org/10.1016/j.chb.2020.106328
Zwaan, R. A., Etz, A., Lucas, R. E., & Donnellan, M. B. (2018).
Making replication mainstream. Behavioral and Brain Sciences,
41, e120.

