Article

Students in High-Achieving Schools: Perils of Pressures to Be “Standouts”

Abstract

Youth in high-achieving schools (HASs) are now declared to be an “at-risk group,” largely because of strong, ongoing pressures to achieve. In this study, we sought to disentangle processes that might underlie how achievement pressures might exacerbate distress, considering five dimensions conceptually important in HAS settings: feelings of envy, comparisons with others on social media, negative feedback from others, the ability to maintain supportive friendships with peers, and overall time pressures. Also included were two potential confounds: time spent on social media and attachment to parents. Across three different HAS samples (total N = 1608), these dimensions were examined in relation to anxious-depressed, withdrawn-depressed, and somatic symptoms, and rule-breaking behaviors using multivariate analyses conducted separately by school and gender. Results revealed that associations between social comparisons and internalizing symptoms were consistent in all subgroups, with robust effect sizes throughout. Additionally, negative feedback on social media was linked with rule-breaking behavior in five out of six subgroups. Results indicated the critical value of targeting social comparisons, in particular, followed by negative feedback on social media in future interventions aimed at fostering resilient adaptation among HAS youth.
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Running head: Pressures at high-achieving schools
Vulnerabilities among students in high-achieving schools:
Potential ill-effects of pressures to be ‘standouts’
Suniya S. Luthar1,2 Bin Suh3 Ashley M. Ebbert 1,3, Nina L. Kumar1
Prepublication version.
For published manuscript, please email SLuthar@AuthConn.com
1 Authentic Connections, 2 Emerita, Columbia University’s Teachers College
3 Arizona State University, Edson College of Nursing and Health Innovation
4 Arizona State University, Department of Psychology,
The authors gratefully acknowledge support provided by Authentic Connections.
Correspondence concerning this article should be addressed to Suniya Luthar, Ph.D., 1545 E
Jeanine Drive, Tempe AZ 85284. Email SLuthar@authconn.com.
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Running head: Pressures at high-achieving schools
Abstract
Youth in high achieving schools (HASs) are now declared to be an “at-risk group,” largely
because of strong, ongoing pressures to achieve. In this study, we sought to disentangle
processes that might underlie how achievement pressures might exacerbate distress, considering
five dimensions conceptually important in HAS settings: Feelings of envy, comparisons with
others on social media, negative feedback from others, the ability to maintain supportive
friendships with peers, and overall time pressures. Also included were two potential confounds:
time spent on social media and attachment to parents. Across three different HAS samples (total
N = 1,608), these dimensions were examined in relation to Anxious-depressed, Withdrawn-
depressed, and Somatic symptoms, and Rule-breaking behavior using multivariate analyses
conducted separately by school and gender. Results revealed that the relations between social
comparisons on internalizing symptoms were consistent in all subgroups, with robust effect sizes
throughout. Additionally, negative feedback on social media was linked with Rule-breaking
behavior in five out of six subgroups. Results indicated the critical value of targeting social
comparisons, in particular, followed by negative feedback on social media in future interventions
aimed at fostering resilient adaptation among HAS youth.
Keywords: Resilience, social media, social comparisons, adolescents, high achieving schools
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Running head: Pressures at high-achieving schools
The focus of this paper is on critical risk and protective processes linked with well-being
among youth at high achieving schools (HASs), now declared to be at risk in two major policy
reports (Geisz & Nakashian, 2018; NASEM, 2019). Referenced here are schools with good
standardized test scores, rich extracurricular offerings, and students heading to selective
universities. A 2019 report by the National Academies of Science, Engineering, and Medicine
(NASEM; 2019) included HAS students among subgroups of youth “at-risk” along with others
typically thought as vulnerable, such as children in poverty, and those who have experienced
parental incarceration or placement in foster care. These assertions echo statements in a Robert
Wood Johnson Foundation (RWJF) report (Geisz & Nakashian, 2018), wherein the top four
environmental risks compromising adolescent well-being, in order, were listed as exposure to
poverty, trauma, discrimination, and excessive pressure to achieve – usually seen in relatively
affluent communities.
The goal in this paper is to disentangle mechanisms that might underlie, or explain, the
process via which attendance at a HAS might confer risk for maladjustment. There is now
growing evidence showing that HAS youth manifest disturbingly high rates of both internalizing
and externalizing symptoms, as well as substance use. As noted above, a core underlying reason
is posited to be ongoing pressures to accumulate distinctions in academics and extracurriculars
(for a review, see Luthar, Kumar, & Zillmer, 2020). The effort here was to explore the potential
role of several constructs that are conceptually related to this overarching risk factor – high
achievement pressures – following recommendations for resilience research on any little studied
group of at-risk youth (Luthar, Cicchetti, & Becker, 2000).
Achievement Pressures and Peer Relationships: Social Comparisons
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Running head: Pressures at high-achieving schools
An unfortunate byproduct of exposure to ongoing achievement pressures (Geisz &
Nakashian, 2018; NASEM, 2019) is heightened competitiveness and comparisons among peers
(Luthar et al., 2019). Within HAS settings, research has, in fact, demonstrated the “Big Fish
Little Pond Effect,” wherein growing up in a group of academically well-performing students is
apparently worse for students’ academic self-concepts than being the best among average
students (Becker & Neumann, 2018; Fang et al., 2018). The underlying mechanism is posited to
be ongoing comparisons with a group of highly talented schoolmates, exacerbating anxiety
(about falling behind) and distress (when not among the very top performers).
When students are in constant competition for distinctive status, there is also potential for
envy, which in turn can increase risks for psychopathology (Vogel, Rose, Roberts, & Eckles,
2014). Envy refers to “an unpleasant and often painful blend of feelings characterized by
feelings of inferiority, hostility, and resentment” when individuals see others as doing better than
they are, themselves (Smith & Kim, 2007, p. 47). Past research has shown that as compared with
their high-achieving counterparts in a low-income, magnet school, suburban HAS youth, in fact,
reported significantly higher levels of peers surpassing them in several realms, including
popularity, attractiveness, and sports (Lyman & Luthar, 2014). Furthermore, suburban students’
high levels of envy were linked with greater levels of externalizing problems, as well as poorer
relatedness with others, especially among females.
We also examined the potential role of HAS adolescents’ comparisons with others on
social media. In the United States, large proportions of teens in general report using sites, such
as YouTube (85%), Instagram (72%), and Snapchat (69%). The potential for ill-effects is seen in
the emergent phenomenon that social media invites comparisons; in fact, “the comparison trap”
is another name given to social media – connoting a place where numbers of followers and likes
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Running head: Pressures at high-achieving schools
are a “rock-solid proof of a person’s worth” (Webber, 2017). On social media platforms,
adolescents’ comparisons with others – and thus, levels of self-esteem – can stem not only from
the number of ‘likes’ they receive on postings (Burrow & Rainone, 2017), but also from
judgments on the quality of their lives versus those of others (Vogel et al., 2014). Unfortunately,
individuals tend to compare their true, offline identity to others’ idealized identities. Studies
have also shown that upward comparisons – i.e., comparisons with others who are perceived to
be superior – consistently mediate the links between social media use and poor well-being
(Fardouly & Vartanian, 2014; Vogel et al., 2014).
Negative and Positive Interactions with Peers
In highly competitive environments, there are also potential risks for heightened
resentment as many are vying to be the very best among the best (Lyman & Luthar, 2014), and
such hostility can be yet another conduit to distress. As noted above, envy involves antipathy to
others, and when expressed directly, can lead to distress from those victimized. Studies have
shown that when teens are on the receiving end of negative feedback from others, including
forms of relational victimization such as being gossiped about or being left out, they are
vulnerable to high distress (Murray-Close, Holterman, Breslend, & Sullivan, 2017). Such
reactions to negative social feedback tend to be particularly pronounced among those whose self-
esteem depends largely on external judgements (Li et al., 2016). These responses are also likely
heightened among adolescents generally, who are not only exquisitely sensitive to others’
feedback but also tend to internalize such feedback (Rodman, Powers, & Somerville, 2017). In
view of this evidence, we considered adolescents’ reports of negative feedback from others as a
third potential risk mechanism, in addition to feelings of envy and social media comparisons.
Also examined was support from close friends. In HAS settings, being in constant
competition with peers can threaten the ability to maintain closeness and trust within friendships
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Running head: Pressures at high-achieving schools
(Luthar et al., 2019); in instances when this closeness can be maintained, there could be
substantial beneficial effects. Studies have shown that during adolescence, friendship qualities
mitigated the effect of victimization (Kawabata & Tseng, 2019) through perceived emotional
support from their close friends (Schacter & Juvonen, 2019). In a longitudinal study that
followed healthy adolescents until young adulthood, supportive friendships had positive
associations with resilient functioning during both adolescence and adulthood, even more so than
family support (Van Harmelen et al., 2017). Accordingly, perceived support from close friends
was considered as a fourth potentially important risk-modifier in this study.
Time Pressures
Yet another likely factor implicated in high distress among HAS students is time
pressure. These adolescents tend to have demanding academic and extracurricular schedules,
with little down time, as exemplified in statements, such as this: "Even though I was getting A's
and B's, mostly A's, in all my classes — all my honors classes — I still felt it wasn't good
enough" (Aubrey & Greenhalgh, 2018). As reported in the national policy report on adolescents’
psychological health (Geisz & Nakashian, 2018), time pressure is associated with heightened
stress and low well-being in educational settings (see also Smith, 2019). Thus, feelings of being
pressured for time were included as another potential risk factor in this study with HAS youth.
Potential Confounds
In order to ascertain links between the core variables of interest outlined earlier – all
conceptually important in high-achieving settings – also considered were two variables that
could have served as confounds in any links with distress. The first of these variables was the
sheer amount of time spent on social media. It is possible that rather than specific emotions
evoked in social media use – around comparisons with others or feeling ill-treated by them – it is
simply spending too much time on social media that is inimical for mental health. Several
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Running head: Pressures at high-achieving schools
studies have shown associations between high social media use and internalizing symptoms
(Barry, Sidoti, Briggs, Reiter, & Lindsey, 2017; Pittman & Reich, 2016; Turel & Serenko, 2012;
Twenge, Martin, & Campbell, 2018). In a recent longitudinal cohort study, spending more than
30 minutes on social media was linked with higher internalizing problems among adolescents,
even after adjusting for demographics, substance use, and past mental health problems (Riehm et
al., 2019). A critical underlying mechanism likely involves lack of time to invest in quality, in-
person relationships with peers (Twenge, Spitzberg, and Campbell, 2019).
Finally, we included adolescents’ levels of attachment to parents, essentially to control
for any general proclivities among teens to see relationships as being generally supportive or
negative. When trying to identify aspects of adolescents’ perceived interpersonal interactions
that have unique significance for mental health, it is useful to partial out, in multivariate
analyses, other variables that are likely to share variance with them. Attachment research clearly
shows that the quality of relationships with parents is critical in forming the lens through which
relationships with others, outside the family, are viewed (Gorrese & Ruggieri, 2012; Yates,
Egeland, & Sroufe, 2003). Attachment relationships may also be related to how adolescents
perceive criticism from others (Morris, Criss, Silk, & Houltberg, 2017). Thus, subjective
feelings on both positive and negative dimensions were assessed in relation to mothers and
fathers, separately, and both were included in multivariate analyses aimed at disentangling
ramifications of the relational processes of central interest here.
Operationalization of Outcomes
With regard to operationalization of outcome variables, the focus here was on indices
known to be elevated among youth in high achieving schools (Luthar et al., 2019). These
included multiple internalizing symptoms, including depression, anxiety, and somatic problems,
all of which are exacerbated among youth contending with high, ongoing achievement pressures.
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Running head: Pressures at high-achieving schools
Also included were rule-breaking, which includes aspects of cheating and stealing, as well as
substance use.
A final design feature was that this study included multiple cohorts of HAS students. In
essence, this allowed for conceptual replication of findings across multiple samples, all assessed
using the same methods and procedures (Maner, 2014; Sheldon & Hoon 2007; Stroebe & Strack
2014). Such replication is especially useful when examining issues and populations heretofore
little studied; any associations recurrently found can provide the basis for specific a priori
hypotheses in future research (Cumming, 2012; Luthar & Ciciolla, 2015; Vosgerau, Simonsohn,
Nelson, & Simmons, 2019). Thus, all analyses in this study were conducted with three different
HAS cohorts, one each from the Southwest, the Midwest, and the Northeast regions of the
United States.
Summary
In summary, in this study of teens in highly competitive schools, the purpose was to
examine the potential unique effects of five dimensions associated with ongoing pressures to
achieve: Feelings of envy, comparisons on social media, negative feedback from others, support
from friends, and overall time pressures. Also examined were two variables that might have
been confounds in any associations found for the five constructs of central interest, i.e., time
spent on social media and attachment to parents. All analyses were conducted separately for
boys and girls, in line with prior work on HAS youth showing that associations involving risk
and protective effects can differ considerably by gender (Luthar & Kumar, 2018).
Methods
Participants
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Running head: Pressures at high-achieving schools
The study used data from three different high achieving private school cohorts: School A
(Southwest), School B (Midwest), and School C (Northeast). Students were all from high
school, grades 9 through 12. Among students who were eligible to participate in the survey, a
total of 1,608 students participated – i.e., 461 from School A, 724 from School B, and 423 from
School C, representing participation rates of 95%, 95%, and 97%, respectively. Of the total n,
1,075 participants responded to all questionnaires for the measures used in this study.
The schools were all considered high-achieving given average SAT scores ranging from
1290 to 1360 (90th – 95th percentile) and over 17 AP course offerings on average. Across three
schools, the average age of the participants was 15.96 years (SD = 1.26); 52% were boys; 52%
were enrolled in grades 11 and 12, while the rest were in grades 9 and 10. Seventy percent
identified themselves as Caucasian; 9% and 7% were Asian American and African American,
respectively. The majority of the participants had two married parents (82%); 86% of fathers and
90% of mothers had a college degree or higher. Eighty-six percent of fathers and 53% of
mothers worked more than 20 hours per week; 7% of fathers and 16% of mothers worked less
than 20 hours. Annual school tuition rates were approximately $25,000 for School A, $17,000
for School B, and $30,000 for School C.
Data in this study are from a larger packet of questionnaires administered by all school
officials as part of ongoing initiatives on positive youth development. Students and their parents
had the option to decline participation. Participating students completed the survey on
computers during regular classroom time. No identifying information of study participants were
collected; analyses presented here are based on entirely anonymous, de-identified data.
Measures
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Running head: Pressures at high-achieving schools
Social comparisons on online platforms were measured by four items asking how
participants feel after viewing other people’s social media accounts. Items included statements
such as, “Your life is not as exciting as others”; and, “You are not as happy as others.”
Cronbach’s s across genders and schools ranged from .79 to .87, with a median of .80. In the α
interest of brevity, this variable is referred to as SM-Social Comparisons from here on.
Envy was measured by asking the extent to which participants would feel envious toward
friends doing better than them in looks, popularity, sports, and wealth (Lyman & Luthar, 2014).
The six Cronbach’s s across both genders and all schools ranged from .92 to .94, with a median α
of .93.
Time spent on social media (henceforth referred to as SM-Time Spent) was measured by
asking, “On a typical day, how much time do you spend” on each of the following six social
media platforms: (1) Snapchat, (2) Facebook, (3) Instagram, (4) Twitter, (5) YouTube, and (6)
Online forums or chatrooms (e.g., Reddit, blogs, etc.). Responses were rated as 0 = I don’t use, 1
= less than 30 minutes, 2 = 30 minutes, 3 = 1 hour, 4 = 2 hours, 5 = 3 hours, 6 = 4 hours, and 7 =
5 or more hours. Averages were computed for time spent across these various platforms (as
many could, potentially, have been accessed simultaneously during the same time period).
Cronbach’s s across genders and schools ranged from .75 to .85, with a median of .81. α
Negative feedback on social media (SM-Negative Feedback) was assessed by two
questions, each with a five-point response scale, and the second with three items subsumed.
These were, “How often do people say mean things to you or about you on social media?” and
“How often do you get negative reactions to messages or pictures that you posted on social
network sites (on your own profile or on another’s profile) from (a) good friends, (b) people you
don’t know very well, and (c) people you know but are not friends?” Cronbach’s s across α
genders and schools ranged from .89 to .94, with a median of .91.
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Running head: Pressures at high-achieving schools
Support from friends was measured by the Network of Relationships Inventory (NRI;
Furman & Buhrmester, 1985). For this study, seven subscales (21 items) assessing positive
dimensions of relationships with a close friend were included, e.g., companionship, intimacy,
and admiration. Examples include, “How much does [your close friend] have a strong feeling of
affection (loving or liking) toward you?” and “How much does this person like or approve of the
things you do?” Cronbach’s s across genders and schools ranged from .93 to .94, with a α
median of .94.
To assess time pressure, participants were asked to indicate the degree to which they felt
pressure related to time constraints, e.g., because of “too many assignments,” and “too many
exams and tests.” Cronbach’s s across genders and schools ranged from .86 to .91, with a α
median of .89.
Adolescents’ attachment with their parents/caregivers was measured by the revised
version of Inventory of Parent and Peer Attachment (IPPA; Greenberg & Armsden, 2009). This
measure includes 50 items assessing three subscales: Trust (e.g., “My mother/father respects my
feelings”); Communication (e.g., “I tell my mother/father about my problems and troubles”); and
Alienation (reverse coded, e.g., “I get upset a lot more than my mother knows about”).
Cronbach’s s for attachment with mother (25 items), across genders and schools, ranged from .α
91 to .95, median .93; for attachment with father (25 items), Cronbach’s s ranged from .91 to .α
95, median .93.
Finally, adjustment outcomes were measured by the Youth Self-Report (YSR; Achenbach
& Rescorla, 2001) subscales of Anxious-depressed, Withdrawn-depressed, and Somatic
symptoms, and Rule-breaking behavior. Cronbach’s s for the four subscales across genders andα
schools were as follows: .87-.89 for Anxious-depressed, median .89; .80-.83, for Withdrawn-
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depressed, median .82; .79-.86 for Somatic, median .83; and, .76-.87 for Rule-breaking, median .
84 for rule-breaking.
Statistical Analysis
Considering each of the four YSR subscales as outcomes in turn, central analyses entailed
six multivariate regressions (i.e., separate analyses for 2 gender groups in 3 schools). The
regression model included all 8 predictor variables: Envy, SM-Social Comparisons, SM-
Negative Feedback, Friend Support, Time Pressure, SM-Time Spent, Dad Attachment, and Mom
Attachment. All analyses were run using SPSS version 25 (IBM Corp, 2017). Any missing
values in the variables were treated with the list-wise deletion method.
Results
Descriptive Analyses and Correlations
Means and standard deviations of all variables and maladjustment indicators are shown
in Table 1, separately by gender and school. On average, scores on SM-Negative Feedback were
higher among boys than girls, while those on SM-Social Comparisons were higher among girls.
With regard to maladjustment problems, Anxious-depressed and Somatic symptoms were higher
among girls than boys, whereas the opposite was true for Rule-breaking behaviors.
INSERT TABLES 1 AND 2 HERE
Correlations among all variables, for all schools and both boys and girls, are shown in
Tables 2a-c. These values were much as would be expected, with each of the predictor variables
showing significant links with at least one (and some, with most) of the four adjustment
outcomes. The only notable exception was closeness to friends, where few coefficients were
significant.
Multivariate Regression Analyses
As indicated earlier, of central interest in the multiple regressions were associations that
recurrently were found to be significant, across the six different subgroups defined by gender and
school. In the model with Anxious-depressed symptoms as the outcome (see Table 3), SM-
Social Comparisons was the most robust indicator across all six subgroups, with moderate effect
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Running head: Pressures at high-achieving schools
sizes (0.28 s β 0.37, median 0.32). The second most common association was found between
Dad Attachment and symptoms (-0.28 s β -0.19, median -0.20). The remaining variables
showed more sporadic links. Altogether, the 8 independent variables explained 28-44% of the
total variance.
With Withdrawn-depressed symptoms as the outcome, results again showed that
associations between SM-Social Comparisons was most pronounced across all subgroups (0.15
s β 0.35, median 0.26; see Table 3). Additionally, Mom Attachment had a significant, negative
association with these symptoms in four out of six subgroups (-0.32 s β -0.19, median -0.22),
whereas Dad Attachment was significant only among boys ( s = -0.34, -0.21, and -0.31 for boys β
in Schools A, B, and C, respectively).
In relation to Somatic symptoms, regression analyses showed significant effects for
SM-Negative Feedback in five of the six subgroups (0.14 s β 0.33, median 0.19; see Table 4);
the exception was boys in School A. Additionally, Time Pressure was significant in four out of
six subgroups (0.10 s β 0.18, median 0.15). Across all models predicting to Somatic
symptoms, the total variances explained with the 8 variables ranged from 14-33%.
INSERT TABLES 3 AND 4 HERE
Finally, with Rule-breaking behaviors as the outcome, links involving SM-Time Spent
were significant in four of the six analyses (0.13 s β 0.34, median 0.19) and were of
borderline significance in a fifth ( = 0.12, βp < 0.10). Coefficients for SM-Negative Feedback
were significant in three cases and trending toward borderline significance (p < 0.10) in the other
three groups (0.14 s β 0.23, median 0.16). Also seen were links for attachment to parents.
Dad Attachment had significant inverse associations in four out of six subgroups (-0.31 s β
-0.19, median -0.21), while Mom Attachment was significant in three cases ( s = -0.24 among β
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Running head: Pressures at high-achieving schools
boys in School A, and among girls in School A and C, s = -0.26 and -0.24, respectively). The β
total variance explained in each model ranged from 19-36% with all 8 independent variables.
Discussion
The single most striking finding from this study was the recurrent, pronounced links
between SM-Social Comparisons and the two internalizing symptoms – i.e., Anxious-depressed
and Withdrawn-depressed – in all 6 subgroups (i.e., boys and girls at each of the 3 private school
samples from different parts of the country). These associations had robust effect sizes across
the regression models. In addition, SM-Negative Feedback showed multiple links with Somatic
symptoms and Rule-breaking behaviors; average SM-Time Spent also had a statistically
significant relationship with Rule-breaking behaviors.
Social Comparisons as a Vulnerability Process
Consistent with past studies that examined the detrimental effect of social media (Fox &
Moreland, 2015; Vogel et al., 2014), social comparisons showed consistent links with distress
indices in the present HAS samples, with moderate effect sizes. Associations between SM-
Social Comparisons and internalizing symptoms remained significant (i.e., 0.31 s β 0.37 for
Anxious-depressed symptoms; 0.18 s β 0.35 for Withdrawn-depressed symptoms) even after
accounting for additional variables related to relationships with parents and peers. This
corroborates suggestions that pervasive social comparisons can be pernicious for adolescents in
high achieving communities not only in real life, but also on social media platforms (Luthar et
al., 2019).
Of course, one could argue that the links are equally likely in the reverse direction – i.e.,
students who are anxious or depressed are more prone to feel inferior to others after seeing the
appealing profiles on social media (Pera, 2018). At the same time, similar logic could be
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Running head: Pressures at high-achieving schools
extended to other independent variables, such that distressed students could also consistently
show envy of others, see others’ feedback as more negative on social media, and feel more
alienated from parents. However, none of these variables showed consistent, robust associations
with the two indicators for the internalizing symptoms in the study.
Given the robustness of these findings across multiple cohorts and their relative effect
sizes, it seems safe to assume that comparisons with others are likely to be inimical for the
adjustment of children in HAS contexts. As implicated in the “Big Fish Little Pond Effect” (Fang
et al., 2018), repeated social comparisons are likely to be particularly damaging in settings where
personal achievement is both highly valued and demonstrated by the majority. The
destructiveness of such comparisons is highlighted in results of a multinational cross-sectional
study (Rathmann, Rilz, Hurrelmann, Kiess, & Richter, 2018), where poor psychosomatic health
(e.g., difficulty falling asleep and physical pain) was evident when students (particularly those
with lower school performance) attended classes with a large group of students with better
school performance. The authors attributed this finding to ongoing comparisons within the
environment that fosters comparisons with the reference group that is ‘better off’. They went as
far as suggesting that perhaps teachers and school administrators should consider placing
students in groups where levels of school performance are relatively heterogeneous.
From the perspective of future preventions, the findings are important given that anxiety
and depression are problem domains in which HAS students are particularly vulnerable.
Relevant, for example, are findings from a recent report comparing rates of clinically significant
levels of symptoms among HAS students, with those in national normative samples (Luthar,
Kumar, & Zillmer, 2020). Considering both Anxious-depressed and Withdrawn-depressed
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Running head: Pressures at high-achieving schools
symptoms among multiple cohorts of HAS boys and girls, the relative risk ratios, with median
values in parentheses, were 4 to 10 times greater than norms among boys (median of 7), and 5 to
14 times greater than norms for girls (median of almost 8). Elevations were less pronounced in
externalizing domains, with HAS rates on Rule-breaking, for example, being 2 to 7 those in
norms among boys (median of 4), and for girls, less than 1 to 3 (median 2). In HAS settings,
marked elevations on these particular internalizing dimensions makes conceptual sense. Anxiety
is heightened when pressures for achievement are chronically high, as depression is exacerbated
at perceived failures on high standards across multiple spheres (Luthar et al., 2020).
Although descriptive findings in the present study showed higher average levels of SM-
Social Comparisons among girls, it is important to note that the links between this variable and
outcomes were strong for both boys and girls in all three schools. These findings are
incongruent with prior findings that reported gender differences in problems associated with
social media – in which females had higher rates of depression in relation to social media use
(Lin et al., 2016), and higher social media addiction than males (Hawi & Samaha, 2017).
Findings of moderate effect sizes associated with SM-Social Comparisons among boys in this
study indicate that the problem likely generalizes across both genders in HAS settings.
SM Comparisons and Other Overlapping Variables: Envy and Time Spent
Another noteworthy finding on social comparisons was that it shared much more unique
variance with internalizing symptoms than another construct with which it conceptually overlaps,
that is, envy. In simple correlations, associations between envy and distress indices, as well as
those between envy and social comparisons, were generally statistically significant. Yet in
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Running head: Pressures at high-achieving schools
multivariate analyses, it was only social comparisons that was significant in relation to both sets
of symptoms.
These findings are conceptually important because the construct of envy has connotations
that are different than social comparisons. Envy subsumes not just a sense of inferiority, but also
a sense of ill will toward others (Smith & Kim, 2007). Our findings suggest that in terms of
ramifications for their internalizing symptoms, what appears to be more critical for distress is
HAS students’ feeling inferior to others, as in social comparisons, rather than necessarily active
resentment of those doing better than themselves. This distinction is important for any future
theoretical conceptualizations of the nature of positive and negative peer group processes in HAS
settings.
A third important finding around social comparisons on social media was, again, in
relation to another construct with which it overlaps, and also was found to be not uniquely
associated with internalizing outcomes, and that was SM-Time Spent on social media. There
have been many suggestions that too much time spent on social media is what is causing this
generation of children to become depressed and anxious, probably because they are not spending
enough in-person time with close others (Twenge et al., 2018). Across these internalizing
outcomes as well as Somatic symptoms, SM-Time Spent showed few significant links.
By contrast, SM-Time Spent did show several links with Rule-breaking in this study,
across these HAS cohorts. With regard to underlying mechanisms, it is possible that high peer
connectedness via social media can promote some counter-conventional, externalizing behaviors,
including substance use (see Luthar et al., 2019). Adolescents might see these behaviors online
within peer groups as references, especially in social media applications like Twitter and
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Running head: Pressures at high-achieving schools
Instagram, which are the two single most frequently used platforms among teens. As online and
offline environments often share problems (Odger & Jensen, 2020), teens’ perception of risky
behaviors as the means to potentially bring higher status in the peer group may occur when they
are frequently exposed to the behaviors on social media. It would be useful to further explore
associations involving these constructs in future research, along with potential underlying
mechanisms.
SM Negative Feedback
In this study, negative feedback from others on social media showed multiple links with
somatic symptoms. Prior research on cyberbullying shows that online victimization contributes
to physical symptoms, as well as psychological distress (Albdour, Hong, Lewin, & Yarandi,
2019). Similarly, Szabo, Ward, and Fletcher (2019) stated that ways in which stressors are
appraised can matter for psychopathology. When they are perceived as ‘threats’, this contributes
to increased somatic symptoms, but when they are as ‘challenges’ – accompanied by
interpersonal and informational coping – somatic symptoms are lowered. In the present study,
harsh feedback from others on social media may have been perceived as threatening,
specifically, in relation to social standing or self-esteem, thus possibly elevating somatic
symptoms. (Similar explanations might underlie links between overall feelings of time pressure
in this study; this construct was associated only with somatic symptoms in this study.)
Negative feedback on social media also showed several associations with Rule-breaking
behaviors. Again, feelings of rejection could lead to heightened acting-out and aggression
(DeWall & Bushman, 2011). Thus, those who received condescending or derogatory comments
19
Running head: Pressures at high-achieving schools
from others online may have, in some instances, reacted with anger or frustration that was
manifested in externalizing, Rule-breaking behaviors.
At the same time, bidirectionality is possible, as well, such that teens who were already
acting out or aggressive received more negative comments from others on social media. Several
studies have shown links between externalizing behaviors and high impulsivity traits
(Beauchaine, Zisner, & Sauder, 2017; Johnson, Tharp, Peckham, Carver, & Haase, 2017) and
sensitivity to social rejection (Gao, Assink, Liu, Chan, & Ip, 2019). Furthermore, individuals
who are more sensitive to rejection are also more likely to behave aggressively, and therefore, be
victimized. Adolescents with externalizing symptoms may already carry trait impulsivity, which
could lead to negative interactions with others in their everyday lives (online and in person),
leading, in turn, to receiving more frequent negative feedback on social media.
Attachment to Parents and Friend Support
Three variables included here could have represented compensatory effects (i.e., main
effects in regressions) – attachment to mothers and fathers, and support from friends – and
findings showed associations for the former two. In multivariate regressions, attachment to
either mother or father was significantly associated with at least one of the four outcome
variables, across all subgroups in the study. These findings are consistent with a core tenet in
resilience research, namely that among children facing various types of life stressors, closeness
to at least one parent can serve critical protective functions (Luthar & Eisenberg, 2017).
Surprisingly, closeness to friends or support from friends, showed few associations in
this study. This was not apparently due to measurement problems with this construct, as
coefficients of internal consistency were high across all subgroups. In future research, it could
20
Running head: Pressures at high-achieving schools
be useful to consider this construct in relation to outcome variables other than those examined
here, within HAS contexts. For example, closeness to friends could be related to positive
adjustment outcomes, such as prosocial behaviors (Malonda, Llora, Mesurado, Samper, &
Mestre, 2019) more so than to indices of psychopathology.
Implications of the Current Study
In considering interventions to reduce social comparisons in HAS settings, two issues
will need attention. First, adults will need to proactively reduce the degree to which these
students’ sense of their own self-worth depends on the splendor of their accomplishments, and
instead, rests on stable feelings that they are loved for who they are as individuals. As
previously noted, feelings of unconditional acceptance from parents are critical for children to
thrive in the face of adversity (Luthar & Eisenberg, 2017; Masten & Tellegen, 2012).
Additionally, students with low self-esteem are especially vulnerable when they perceive
themselves to be inferior to others (on social media sites or in person), regardless of actual levels
of relative standing (Cramer, Song, & Drent, 2016).
Second, in the environmental context of high performing schools, it is important that
proactive steps are taken to reduce norms and rituals that tend to exacerbate students’ social
comparisons. Examples of these are public announcements of class ranks, and “sweater day”
where those who are not accepted by prestigious colleges may feel less than others (Luthar &
Kumar, 2018; Luthar, Kumar, & Zillmer, 2019). It will be important to include students
themselves in designing interventions to reduce unhealthy social comparisons. This can be done
by first showing them exactly how pernicious and destructive these comparisons can be for
mental health. Additionally, focus groups can be used to help get students’ creative ideas about
21
Running head: Pressures at high-achieving schools
how this can be addressed in their own school. Luthar and colleagues (2019) describe, for
example, how high school seniors volunteered to talk to middle schoolers about the need to
watch out for unhealthy competition and comparisons, which they themselves had become prey
to, but had then overcome.
Similar initiatives could be used to address the second salient aspect of social media
identified as a vulnerability factor, i.e., negative social feedback. Again, anti-bullying initiatives
– targeting unkindness in-person or online – can be useful, particularly when involving students’
own voices in design and implementation. Examples are seen in significant reduction of
antisocial and bullying behaviors followed by bullying prevention programs (e.g., Olweus &
Limber, 2010; for a policy report, see Flannery et al., 2016).
Limitations and Strengths
There were several limitations associated with this work, including the use of cross-
sectional data, the reliance on self-reports, and uncertain generalizability of findings. The cross-
sectional design precludes any conclusions about causality, i.e., that social media use leads to
adjustment problems, or vice versa (Bor, Dean, Najman, & Hayatbakhsh, 2014). Experimental
studies (e.g., Hunt, Marx, Lipson, & Young, 2018) could be helpful to investigate any
directionality of the relationships among social media and maladjustment variables (Appel,
Gerlach, & Crusius, 2016). With regard to the use of self-reports, retrospective estimates of
social media use can be biased because it is difficult to remember the actual time spent online
(Hunt, Marx, Lipson & Young, 2018). In the future, other prospective methods of reporting
social media use (such as daily diaries) could help to further illuminate the types of patterns
captured here. Finally, it would be useful to examine patterns documented here in schools with
varying levels of students’ achievement overall, as well as diversity in the demographic
characteristics of their families.
22
Running head: Pressures at high-achieving schools
Multifaceted measurement of social comparisons could also be useful in future research.
There could be differences in ramifications for adjustment, for example, depending on the
proximity of relationships with people to whom teens compare themselves, and the types of
contents that are viewed (e.g., image vs. text-type platforms; see Pittman & Reich, 2016).
Similarly, with regard to negative feedback, study results would be more informative if
assessments distinguished between multiple forms of online harassment, ranging from
cyberbullying to sexual harassment (Pater, Kim, Mynatt, & Fiesler, 2016).
Finally, future research might examine potential curvilinear links between time spent on
social media and adjustment problems, as well as alternative ways to capture what might
represent excessive preoccupation. With regard to the former issue, this study yielded negligible
evidence of linear links with maladjustment. While overly high use of social media is
detrimental, individuals with moderate use tend to have better outcomes than those with no use at
all (Twenge et al., 2018; see also Hanley, Watt, & Coventry 2019). With reference to alternative
measurement approaches, future studies might also assess the total number of times that teens
look at social media sites per day (e.g., in between school work or extracurriculars) – in
operationalizing potentially unhealthy levels of use.
Offsetting the limitations of this study are several strengths. First, it is based on a
sample of youth who are still understudied, clearly at risk, and often difficult for developmental
researchers to access (Luthar, Barkin, & Crossman, 2013) – students at elite private schools. The
total number of participants was large, i.e., over 1,000, and participants were recruited from three
HASs, located in different regions of the United States. This study design allowed for
determination of associations that were relatively robust, seen recurrently across discrete samples
of high achieving students.
23
Running head: Pressures at high-achieving schools
Most importantly, this study is the first to demonstrate strong, consistent links between
HAS students’ tendencies to compare themselves with peers and their own anxiety and
depression, across multiple cohorts. These findings are important in prioritizing targets for
preventive interventions, as HAS students are now clearly noted, in science as well as the media,
as being a group at high risk for these problems, as well as serious self-harm (Aubrey &
Greenhalgh, 2018, Denizet-Lewis, 2017; Geisz & Nakashian, 2018, Luthar et al., 2019; NASEM,
2019). In addition, findings here suggest that this construct is actually far more consistently
linked with anxiety and depression than one that is widely believed to be a culprit, i.e., amount of
time spent on social media use (at least in HAS settings). These cumulative findings imply the
importance of collaborative efforts among adult stakeholders – parental and educators – with
involvement of students themselves, to mitigate both unhealthy social comparisons in school
settings that are already rife with high levels of everyday stress and pressure.
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Table 1. Descriptive statistics: Means and standard deviations of Social Media (SM) dimensions and other variables, by school and gender
Variable
School A (Southwest) School B (Midwest) School C (Northeast) School Sex
Boys
(n = 235)
Girls
(n = 226)
Boys
(n = 377)
Girls
(n = 347)
Boys
(n = 219)
Girls
(n = 204) Fη2Fη2
MSD MSD MSD MSD MSD MSD
Envy 1.82 0.46 0.92 0.48 1.69 0.47 1.88 0.48 1.62 0.49 1.81 0.47 550.02**
*0.41 25.22*** 0.02
SM-Social
Comparisons 5.15 4.27 8.02 4.49 4.67 4.07 8.01 4.10 4.16 4.23 6.94 4.42 6.45** 0.08 204.47**
*0.12
SM-Negative
Feedback 1.89 2.64 1.36 2.10 1.66 2.46 1.06 1.80 1.59 2.64 1.26 2.07 1.68 19.48*** 0.01
SM-Average Time
Spent 1.68 1.04 1.67 0.91 1.65 0.86 1.64 0.85 1.46 1.00 1.31 0.65 13.90*** 0.02 1.06
Time Pressure 3.71 0.95 4.11 0.84 3.45 1.05 3.86 0.94 3.47 1.14 3.89 0.95 9.80*** 0.01 68.53*** 0.04
Total Friend Support 3.30 0.73 3.90 0.70 3.32 0.70 3.81 0.68 3.26 0.77 3.77 0.72 1.54 215.93
*** 0.12
Mom Attachment 95.66 16.77 98.89 19.80 98.88 16.13 97.93 19.31 99.67 16.9 99.32 21.18 1.45 0.17
Dad Attachment 92.41 17.01 93.28 19.47 95.09 16.96 91.88 20.28 96.09 18.89 94.34 22.36 1.63 2.71
Anxious-Depressed 6.35 5.18 10.46 6.23 5.27 5.14 7.41 5.37 4.93 5.28 7.88 5.88 21.37*** 0.03 107.36**
*0.07
T-score 60.0
1
67.9
5
58.1
2
62.0
1
57.5
162.91
Withdrawn-
Depressed 4.04 3.37 4.85 3.52 3.77 3.47 3.58 3.22 3.44 3.34 4.03 3.50 7.70*** 0.01 3.12
T-score 58.0
7
60.1
6
57.4
7
56.8
6
56.6
658.11
Somatic Symptoms 3.35 3.59 5.95 4.77 2.61 3.40 3.89 3.68 2.26 3.52 3.47 3.83 26.92*** 0.03 71.28*** 0.05
T-score 57.2
9
64.1
0
55.5
3
58.6
4
54.7
157.68
Rule-Breaking
Behaviors 5.05 4.56 4.74 4.39 4.55 4.39 3.51 3.36 4.68 4.71 3.61 3.33 5.94 ** 0.01 15.15*** 0.10
T-score 56.1
7
55.7
6
55.3
3
53.6
1
55.6
453.82
34
Running head: Social Media and Adolescents’ Resilience
Note. *p < 0.05; **p < 0.01; ***p < 0.001
35
Running head: Social Media and Adolescents’ Resilience
Table 2a. Simple correlations among Social Media (SM) dimensions and other variables: School A (Southwest)
1 2 3 4 5 6 7 8 9 10 11 12
1. Envy -0.46 ** 0.32 ** 0.24 ** 0.29 ** 0.05 -0.20 ** -0.21 ** 0.37 ** 0.18 ** 0.23 ** 0.24 **
2. SM-Social
Comparisons 0.39 ** - 0.24 ** 0.25 ** 0.27 ** -0.03 -0.29 ** -0.32 ** 0.52 ** 0.38 ** 0.31 ** 0.33 **
3. SM-Negative
Feedback 0.29 ** 0.26 ** - 0.41 ** 0.14 * -0.17 * -0.18 * -0.21 ** 0.29 ** 0.24 ** 0.31 ** 0.42 **
4. SM-Average Time
Spent 0.32 ** 0.15 * 0.38 ** - 0.03 -0.02 -0.22 ** -0.30 ** 0.19 ** 0.22 ** 0.21 ** 0.40 **
5. Time Pressure 0.25 ** 0.08 0.18 ** 0.13 - 0.11 -0.17 * -0.12 0.29 ** 0.17 * 0.29 ** 0.17 *
6. Total Friend Support -0.02 0.02 0.07 0.12 0.08 - 0.11 0.11 -0.11 -0.21 ** 0.01 -0.04
7. Mom Attachment -0.22 ** -0.20 ** -0.12 -0.21 ** 0.05 0.17 * - 0.58 ** -0.41 ** -0.47 ** -0.36 ** -0.43 **
8. Dad Attachment 0.20 ** -0.19 ** -0.12 -0.12 -0.09 0.26 ** 0.72 ** - -0.32 ** -0.33 ** -0.32 ** -0.45 **
9. Anxious-Depressed 0.35 ** 0.39 ** 0.28 ** 0.24 ** 0.11 -0.06 -0.34 ** -0.37 ** - 0.73 ** 0.68 ** 0.41 **
10. Withdrawn-Depressed 0.13 0.21 ** 0.20 ** 0.20 ** 0.07 -0.12 -0.40 ** -0.47 ** 0.69 ** - 0.61 ** 0.46 **
11. Somatic 0.32 ** 0.14 * 0.30 ** 0.30 ** 0.16 * 0.10 -0.23 ** -0.25 ** 0.65 ** 0.55 ** - 0.50 **
12. Rule-Breaking 0.30 ** 0.15 * 0.34 ** 0.44 ** 0.10 0.00 -0.42 ** -0.41 ** 0.51 ** 0.54 ** 0.60 ** -
Note. *p < 0.05; **p < 0.01; Correlations for boys are at the lower left corner; correlations for girls are at the upper right corner.
36
Running head: Social Media and Adolescents’ Resilience
Table 2b. Simple correlations among Social Media (SM) dimensions and other variables: School B (Midwest)
1 2 3 4 5 6 7 8 9 10 11 12
1. Envy -0.50 ** 0.21 ** 0.08 0.28 ** 0.01 -0.17 ** -0.15 ** 0.35 ** 0.19 ** 0.23 ** 0.26 **
2. SM-Social
Comparisons 0.47 ** -0.21 ** 0.16 ** 0.25 ** -0.05 -0.27 ** -0.20 ** 0.49 ** 0.33 ** 0.30 ** 0.23 **
3. SM-Negative
Feedback 0.32 ** 0.36 ** -0.13*0.03 0.02 -0.13*-0.12*0.22 ** 0.11*0.22 ** 0.31 **
4. SM-Average Time
Spent 0.19 ** 0.31 ** 0.38 ** - 0.05 -0.01 -0.06 -0.12*0.10 0.12*0.06 0.15 **
5. Time Pressure 0.23 ** 0.23 ** 0.11*0.09 - 0.07 -0.06 -0.06 0.23 ** 0.23 ** 0.24 ** 0.10
6. Total Friend Support -0.03 0.02 -0.04 0.08 0.04 - 0.07 0.12*-0.10 -0.18 ** -0.01 0.03
7. Mom Attachment -0.13*-0.25 ** -0.06 -0.16 ** -0.20 ** 0.10 - 0.67 ** -0.32 ** -0.34 ** -0.23 ** -0.29 **
8. Dad Attachment -0.19 ** -0.33 ** -0.12*-0.16 ** -0.19 ** 0.08 0.68 ** --0.33 ** -0.31 ** -0.21 ** -0.31 **
9. Anxious-Depressed 0.39 ** 0.52 ** 0.32 ** 0.21 ** 0.22 ** 0.05 -0.35 ** -0.40 ** -0.71 ** 0.67 ** 0.44 **
10. Withdrawn-Depressed 0.26 ** 0.45 ** 0.24 ** 0.15 0.17 ** -0.01 -0.43 ** -0.46 ** 0.79 ** -0.55 ** 0.33 **
11. Somatic 0.28 ** 0.39 ** 0.32 ** 0.26 ** 0.22 ** 0.14*-0.27 ** -0.36 ** 0.73 ** 0.61 ** -0.49 **
12. Rule-Breaking 0.35 ** 0.30 ** 0.33 ** 0.33 ** 0.19 ** 0.14 ** -0.32 ** -0.42 ** 0.54 ** 0.49 ** 0.62 ** -
Note. *p < 0.05; **p < 0.01; Correlations for boys are at the lower left corner; correlations for girls are at the upper right corner.
37
Running head: Social Media and Adolescents’ Resilience
Table 2c. Simple correlations among Social Media (SM) dimensions and other variables: School C (Northeast)
1 2 3 4 5 6 7 8 9 10 11 12
1. Envy -0.58 ** 0.20 ** 0.18*0.34 ** 0.02 0.33 ** -0.25 ** 0.48 ** 0.30 ** 0.34 ** 0.28 **
2. SM-Social
Comparisons 0.34 ** -0.20 ** 0.17*0.26 ** -0.03 -0.31 ** -0.34 ** 0.54 ** 0.48 ** 0.36 ** 0.20 **
3. SM-Negative
Feedback 0.14*0.25 ** - 0.05 0.00 -0.06 -0.12 -0.04 0.17*0.16*0.23 ** 0.19 **
4. SM-Average Time
Spent 0.02 0.19 ** 0.44 ** -0.15*0.12 -0.04 -0.10 0.11 0.09 0.18*0.21 **
5. Time Pressure 0.25 ** 0.25 ** 0.08 0.03 - 0.00 -0.26 ** -0.20 ** 0.36 ** 0.33 ** 0.36 ** 0.23 **
6. Total Friend Support -0.03 0.00 0.00 0.21 ** 0.08 - 0.14 0.04 0.04 -0.12 -0.01 0.11
7. Mom Attachment -0.15*-0.21 ** -0.23 ** -0.14*-0.13 0.24 ** -0.65 ** -0.37 ** -0.44 ** -0.39 ** -0.36 **
8. Dad Attachment -0.14 -0.31 ** -0.20 ** -0.09 -0.10 0.24 ** 0.79 ** --0.41 ** -0.41 ** -0.33 ** -0.31 **
9. Anxious-Depressed 0.17*0.42 ** 0.35 ** 0.32 ** 0.21 ** 0.09 -0.34 ** -0.40 ** -0.74 ** 0.55 ** 0.33 **
10. Withdrawn-Depressed 0.13 0.36 ** 0.29 ** 0.23 ** 0.21 ** 0.04 -0.36 ** -0.40 ** 0.80 ** -0.65 ** 0.44 **
11. Somatic 0.03 0.22 ** 0.34 ** 0.37 ** 0.18 ** 0.11 -0.22 ** -0.26 ** 0.69 ** 0.69 ** -0.44 **
12. Rule-Breaking 0.06 0.22 ** 0.30 ** 0.50 ** 0.11 0.14*-0.27 ** -0.20 ** 0.52 ** 0.53 ** 0.60 ** -
Note. *p < 0.05; **p < 0.01; Correlations for boys are at the lower left corner; correlations for girls are at the upper right corner.
38
Running head: Social Media and Adolescents’ Resilience
Table 3. Regression analyses prediction to Anxious Depressed Symptoms, by school and gender
School A - Southwest School B - Midwest School - Northeast
Outcome / Predictors Boys Girls Boys Girls Boys Girls
Anxious-Depressed
Envy 0.09 0.12^0.12*0.10^-0.02 0.21 **
SM-Social
Comparisons 0.31 *** 0.33 *** 0.35 *** 0.37 *** 0.28 *** 0.31 ***
SM-Negative
Feedback 0.04 0.08 0.10^0.16 ** 0.32 *** 0.08
SM-Ave Time Spent 0.01 -0.05 0.00 -0.04 -0.03 -0.06
Time Pressure 0.03 0.07 0.04 0.08 0.10 0.13*
Friend Support 0.01 -0.10 0.08 -0.04 0.13*0.06
Mom Attachment -0.16 -0.25 ** -0.09 -0.04 -0.04 -0.06
Dad Attachment -0.17^-0.03 -0.19 ** -0.21 ** -0.28 ** -0.19*
Total 0.29 0.36 0.38 0.38 0.44 0.43
Withdrawn-
Depressed
Envy -0.12 -0.03 -0.01 -0.02 -0.10 -0.09
SM-Social
Comparisons 0.18*0.25 ** 0.35 *** 0.21 ** 0.26 *** 0.32 ***
SM-Negative
Feedback 0.06 0.06 0.07 0.05 0.28 *** 0.09
SM-Average Time
Spent 0.02 0.03 -0.04 0.04 -0.06 0.01
Time Pressure 0.03 0.01 -0.01 0.15 ** 0.15*0.15*
Friend Support -0.05 -0.16*0.02 -0.16 ** 0.13^-0.07
Mom Attachment -0.14 -0.34 *** -0.20 ** -0.19 ** -0.04 -0.24 **
Dad Attachment -0.34 ** -0.05 -0.21 ** -0.12^-0.31 ** -0.14
Total 0.27 0.29 0.36 0.25 0.40 0.37
Note. SM = Social Media; ^ p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
39
Running head: Social Media and Adolescents’ Resilience
Table 4. Regression analyses prediction to Somatic Symptoms and Rule-Breaking, by school and gender
School A - Southwest School B - Midwest School - Northeast
Outcome / Predictors Boys Girls Boys Girls Boys Girls
Somatic Symptoms
Envy 0.17*0.00 0.03 0.01 -0.05 0.03
SM-Social
Comparisons 0.02 0.13 0.21 ** 0.19 ** 0.12 0.16^
SM-Negative
Feedback 0.03 0.15*0.14*0.19 ** 0.33 *** 0.19 **
SM-Average Time
Spent 0.05 -0.02 0.03 -0.07 0.12 0.03
Time Pressure 0.05 0.13^0.10*0.14 ** 0.16*0.18*
Friend Support 0.12 0.00 0.19 *** -0.03 0.10 0.04
Mom Attachment -0.08 -0.22*-0.06 -0.09 0.00 -0.26 **
Dad Attachment -0.19^-0.14 -0.21 ** -0.09 -0.19^-0.04
Total 0.14 0.24 0.26 0.18 0.33 0.30
Rule-Breaking
Envy 0.10 -0.01 0.23 *** 0.14*-0.05 0.10
SM-Social
Comparisons -0.02 0.12 -0.01 0.07 0.14*-0.07
SM-Negative
Feedback 0.13^0.23 *** 0.14 ** 0.16 ** 0.14^0.14^
SM-Average Time
Spent 0.20 ** 0.12^0.13*0.06 0.34 *** 0.18*
Time Pressure -0.01 -0.05 0.04 0.00 0.13*0.10
Friend Support 0.09 0.06 0.19 *** 0.04 0.12^0.11
Mom Attachment -0.24*-0.26 ** -0.06 -0.10 -0.13 -0.24*
Dad Attachment -0.21*-0.19*-0.31 *** -0.20 ** -0.03 -0.10
Total 0.30 0.35 0.32 0.19 0.36 0.23
Note. SM = Social Media; ^ p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
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Importance Social media use may be a risk factor for mental health problems in adolescents. However, few longitudinal studies have investigated this association, and none have quantified the proportion of mental health problems among adolescents attributable to social media use. Objective To assess whether time spent using social media per day is prospectively associated with internalizing and externalizing problems among adolescents. Design, Setting, and Participants This longitudinal cohort study of 6595 participants from waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the Population Assessment of Tobacco and Health study, a nationally representative cohort study of US adolescents, assessed US adolescents via household interviews using audio computer-assisted self-interviewing. Data analysis was performed from January 14, 2019, to May 22, 2019. Exposures Self-reported time spent on social media during a typical day (none, ≤30 minutes, >30 minutes to ≤3 hours, >3 hours to ≤6 hours, and >6 hours) during wave 2. Main Outcomes and Measure Self-reported past-year internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems during wave 3 using the Global Appraisal of Individual Needs–Short Screener. Results A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were studied. In unadjusted analyses, spending more than 30 minutes of time on social media, compared with no use, was associated with increased risk of internalizing problems alone (≤30 minutes: relative risk ratio [RRR], 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73); associations with externalizing problems were inconsistent. In adjusted analyses, use of social media for more than 3 hours per day compared with no use remained significantly associated with internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77) and comorbid internalizing and externalizing problems (>3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43) but not externalizing problems alone. Conclusions and Relevance Adolescents who spend more than 3 hours per day using social media may be at heightened risk for mental health problems, particularly internalizing problems. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.
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