RESEARCH AND PRACTICE
Exploring Alcohol-Use Behaviors Among Heterosexual
and Sexual Minority Adolescents: Intersections With
Sex, Age, and Race/Ethnicity
Amelia E. Talley, PhD, Tonda L. Hughes, RN, PhD, Frances Aranda, PhD, Michelle Birkett, PhD, and Michael P. Marshal, PhD
Although alcohol consumption by youths has
declined in recent years, it remains a major
public health problem.1 Underage drinking is
associated with a range of physical, academic,
and social problems. Youths who drink, especially those who drink heavily, are more likely
to engage in delinquent behavior, experience
violence and victimization, and commit suicide.1,2 Of great concern is that alcohol consumption is a leading contributor to injury, the
main cause of death for people younger than 21
years.1 Early onset drinking is also associated
with increased risk for developing an alcoholuse disorder during the lifespan.3
Despite the fact that almost all US youths
grow up in a culture permeated by alcohol, the
prevalence of early and heavy drinking and
its consequences vary across demographic
groups. For example, considerable variation
exists in alcohol consumption between White
and racial/ethnic---minority youths; data consistently show the highest rates of drinking and
drinking-related problems among White and
American Indian or Alaska Native youths,
followed by Hispanic/Latino, Black/African
American, and Asian youths.4---6 Non-Hispanic
White youths generally start drinking at younger ages than their racial/ethnic minority
counterparts. Greater percentages of racial/
ethnic minority youths abstain or drink very
little,7 and significant differences exist in levels
of drinking between racial/ethnic minority
boys and girls.7 Generally, across racial/ethnic
groups, prevalence rates of drinking for boys
and girls tend to be similar in younger age
groups2; among older adolescents, however,
more boys than girls engage in frequent and
heavy drinking,2 and boys show higher rates of
drinking-related consequences.8
Alcohol use and heavy drinking are more
prevalent among lesbian, gay, and bisexual
(LGB) youths and adults than among their
Objectives. We examined sexual orientation status differences in alcohol use
among youths aged 13 to 18 years or older, and whether differences were
moderated by sex, age, or race/ethnicity.
Methods. We pooled data from the 2005 and 2007 Youth Risk Behavior
Surveys and conducted weighted analyses, adjusting for complex design effects.
We operationalized sexual orientation status with items assessing sexual
orientation identity, sexual behavior, sexual attraction, or combinations of these.
Results. Compared with exclusively heterosexual youths, sexual-minority
youths were more likely to report each of the primary study outcomes
(i.e., lifetime and past-month alcohol use, past-month heavy episodic drinking,
earlier onset of drinking, and more frequent past-month drinking). Alcohol-use
disparities were larger and more robust for (1) bisexual youths than lesbian or
gay youths, (2) girls than boys, and (3) younger than older youths. Few
differences in outcomes were moderated by race/ethnicity.
Conclusions. Bisexual youths, sexual-minority girls, and younger sexual-minority
youths showed the largest alcohol-use disparities. Research is needed that focuses on
identifying explanatory or mediating mechanisms, psychiatric or mental health
comorbidities, and long-term consequences of early onset alcohol use, particularly
frequent or heavy use, among sexual-minority youths. (Am J Public Health. 2014;104:
295–303. doi:10.2105/AJPH.2013.301627)
heterosexual counterparts, and this is especially
true for LGB girls and women.9---12 Because most
research on alcohol use among youths has
focused on heterosexual youths or has not
assessed sexual orientation, little is known about
how sexual orientation interacts with other demographic characteristics to influence drinking
patterns. Studies comparing sexual-minority
and heterosexual adults suggest that drinking
patterns of sexual minorities differ in substantial
ways from those of the general population. For
example, differences in alcohol-use patterns
between lesbian or bisexual women and gay or
bisexual men are much smaller than those
between heterosexual women and men.12---18
Also, rates and patterns of drinking may be
more similar among sexual minorities across
racial/ethnic minority statuses, compared with
their heterosexual counterparts.19
Few studies have examined alcohol-use disparities among subgroups of sexual-minority
youths (SMYs; < 18 years). In a meta-analysis,
February 2014, Vol 104, No. 2 | American Journal of Public Health
Marshal et al.20 found that SMYs had more
than twice the odds of ever drinking alcohol,
and 3 of the 4 studies that examined alcoholrelated effects for boys and girls separately
reported larger alcohol-use disparities among
girls than among boys.21---23 Moreover, although studies analyzed by Marshal et al.20
included racially and ethnically diverse samples, none examined the intersecting influences
of sexual-minority and racial/ethnic status on
substance-use outcomes.
The current analyses build on existing literature in 2 ways. First, previous studies with
SMYs have examined a limited number of
alcohol-use outcomes (e.g., any lifetime drinking).21,22,24 Second, researchers have typically
combined subgroups of SMYs25---31 in analyses—
often because of small subgroup sample sizes.
Because alcohol-use patterns appear to differ
on the basis of how sexual orientation is
defined—that is, whether data are analyzed on
the basis of sexual identity, behavior, attraction,
Talley et al. | Peer Reviewed | Research and Practice | 295
RESEARCH AND PRACTICE
or some combination of these17,32---34—we
extended previous work25,35 by examining
a variety of sexual-orientation subgroup
differences. In addition to our primary goal
of examining sexual-orientation differences in
alcohol-use patterns, we conducted moderation analyses to determine whether relations
between sexual orientation and drinking patterns varied on the basis of sex, age, or race/
ethnicity.
METHODS
We used pooled data from 2005 and 2007
Youth Risk Behavior Survey (YRBS) samples.
The YRBS study design details and data set
characteristics are described elsewhere in this
issue.36 The YRBS data provide an opportunity
to compare alcohol-use patterns among SMYs
and their heterosexual peers in a populationbased school analytic sample. In the current
analyses, we used data from 14 US jurisdictions
that assessed sexual-orientation identity, sex of
sexual partners, or sexual attraction. We excluded youths who did not respond to the
sexual-orientation questions or who were 12
years old or in seventh grade. The final sample
size (n 37 543) varies across statistical
models depending on which sexual-orientation
variable is included.
Measures
Sexual orientation. We used a binary variable, constructed from questions about sexual
identity, behavior, and attraction, to categorize
participants as SMYs or exclusively heterosexual.36 We considered participants who
reported a nonheterosexual identity, any
same-sex behavior, or any same-sex attraction
to be SMYs. We coded participants who indicated that they were unsure of their sexual
identity as missing unless they also reported
any same-sex sexual behavior (n = 116;
0.30%), in which case they were coded as
SMYs. Otherwise, we considered participants to
be exclusively heterosexual. Sexual attraction
was only assessed in Hawaii; thus, sexualminority status is the only sexual-orientation
variable that distinguishes participants on the
basis of attraction.
We coded participants as having a sexualmajority identity if they identified as heterosexual or a sexual-minority identity if they
identified as lesbian, gay, or bisexual. We
coded participants who were unsure of their
sexual identity and were not sexually active
(n = 273; 1.0%) or did not answer a question
about sexual partners (n = 187; 0.7%) as
missing. We considered participants who were
sexually experienced (n = 294; 1.0%) yet
unsure of their sexual identity to be SMYs.
We based this decision on preliminary data
analyses and previous studies showing heightened risk for substance-use outcomes among
individuals who are unsure of their sexual
identity.14,17
Because alcohol-use patterns for individuals
who report both same-sex and opposite-sex
sexual partners may be distinct from those who
report only same- or only opposite-sex sexual
partners,17,28,37 we constructed 2 dummy variables to distinguish participants who reported
only opposite-sex sexual partners from those
who reported only same-sex sexual partners
and those who reported both male and female
sexual partners.
We used responses to questions about sexual identity and sex of sexual partners to create
6 nominal sexual-orientation subgroups: we
coded 2 groups as lesbian or gay, or bisexual,
respectively, on the basis of self-identity. Those
who identified as heterosexual or were unsure
of their sexual identity yet also reported any
same-sex sexual partners constituted the third
group. Participants who were unsure of their
identity and reported only opposite-sex sexual
partners were the fourth group, and those
unsure of their identity and not sexually active
constituted the fifth group. The sixth, exclusively heterosexual group, included youths
who identified as heterosexual and reported
that they were not sexually active or reported
only opposite-sex sexual partners. We created
dummy variables for the first 5 subgroups, with
the last serving as the reference group.38
Alcohol outcomes. Participants were asked,
“How old were you when you had your first
drink of alcohol, other than a few sips?” Response options were (1) I have never had
a drink of alcohol other than a few sips, (2) 8
years old or younger, (3) 9---10 years old, (4)
11---12 years old, (5) 13---14 years old, (6)
15---16 years old, and (7) 17 years old or older.
We recoded responses to create a variable
ranging from 8 to 17, corresponding to the
approximate age that alcohol was first consumed.
296 | Research and Practice | Peer Reviewed | Talley et al.
We coded participants who never had a drink
of alcohol (n = 10 825) as missing.
We used responses to the age of drinking
onset question to construct a dichotomous
variable indicating any versus no lifetime
drinking.
To create a variable reflecting number of
past-month drinking days, we used a question in
which participants were asked: “During the past
30 days, on how many days did you have at
least one drink of alcohol?” Response options
were no days, 1---2 days, 3---5 days, 6---9 days,
10---19 days, 20---29 days, and all 30 days.
We used responses to the past-month
drinking days question to create a dichotomous
indicator of any versus no past-month drinking.
To assess heavy episodic drinking, participants were asked on how many days in the past
month they drank 5 or more drinks of alcohol
within a couple of hours. We recoded response
options (0 days, 1 day, 2 days, 3---5 days, 6---9
days, 10---19 days, 20 or more days) to construct a dichotomous variable indicating any
versus no past-month heavy episodic drinking.
Demographics. We created a dichotomous
variable that differentiated younger participants (aged 13---15 years) from older participants (aged 16 to 18 years or older). This
variable represented roughly equal-sized age
groups and allowed for examination of moderated relationships relevant to an adolescent
milestone (obtaining a driver’s license) associated with greater independence and access to
alcohol.
We used the nominal race/ethnicity variable
constructed by Mustanski et al.36 that recategorized 8 racial/ethnic groups into 6 (Asian,
Black/African American, Hispanic/Latino,
American Indian/Alaskan Native/Native
Hawaiian/Pacific Islander, White, and multiple
or other ethnicity). We created 5 dummy
variables; White youths served as the reference
group.
Covariates. Our models included covariates
shown to be strong correlates of problematic
drinking among adolescents in the general
population39---42 as well as among sexual minorities.14,43---45 Age of first sexual intercourse
was assessed by asking, “How old were you
when you had sexual intercourse for the first
time?” We created a grand-mean-centered
variable with response options ranging from (1)
aged 11 years or younger to (8) aged 17 years or
American Journal of Public Health | February 2014, Vol 104, No. 2
RESEARCH AND PRACTICE
older. We coded sexual assault (0 = no; 1 = yes)
on the basis of the question “Have you ever
been physically forced to have sexual intercourse when you did not want to?”
Data Analysis
We conducted descriptive analyses with
SPSS version 21 (IBM, Somers, NY); we fit
multiple regression models with HLM, version
7 (Scientific Software International Inc, Skokie,
IL). We used the complex sample module in
SPSS, which takes into account weight, stratum,
and primary sampling unit variables, in analyses to adjust for the complex sampling design of
the YRBS.36 We used hierarchical linear modeling to account for clustering of the data, with
jurisdiction entered at level 2 in each model.
We used full-information maximum likelihood
estimation. We modeled dichotomous outcomes with a Bernoulli distribution; we modeled number of past-month drinking days with
a Poisson distribution, accounting for overdispersion. We estimated separate models for
each of the sexual-orientation indices; all
models included sex, age, race/ethnicity, and
covariates. We conducted directed tests of
interaction effects for hierarchical linear
models based on results from preliminary
ordinary least squares regression analyses.
Coefficients associated with the covariates
were similar across models examining each of
the primary outcomes. Older age of first sexual
intercourse was associated with lower likelihood of lifetime drinking (odds ratio [OR] =
0.71; 95% confidence interval [CI] = 0.69,
0.73; P < .001), any past-month drinking
(OR = 0.72; 95% CI = 0.70, 0.73; P < .001),
and any past-month heavy episodic drinking
(OR = 0.72; 95% CI = 0.70, 0.73; P < .001).
It was also associated with older age of drinking
onset (b = 0.38; SE = 0.01; 95% CI =0.36,
0.40) and fewer past-month drinking days
(b = –0.28; SE = 0.002; 95% CI = -0.28, ---0.28).
History of sexual assault was associated with
greater likelihood of lifetime drinking (OR = 1.82;
95% CI = 1.61, 2.06; P < .001), any past-month
drinking (OR = 1.72; 95% CI = 1.55, 1.91;
P < .001), and any past-month heavy episodic
drinking (OR = 1.74; 95% CI = 1.57, 1.93;
P < .001), as well as a younger drinking onset
(b = –0.37; SE = 0.06; 95% CI = ---0.49, ---0.25)
and more past-month drinking days (b = 0.52;
SE = 0.01; 95% CI = 0.50, 0.54; data not
shown). Because coefficients were similar
across models and because of space limitations,
we do not present associations between
covariates and study outcomes in other models.
Sexual Orientation
RESULTS
Table 1 summarizes sample characteristics
and provides P values for bivariate comparisons among moderator and primary study
variables. Results of cross-tabulations between
select sexual orientation variables and alcohol
outcomes are presented in Table 2. In Table 3,
we show main effects between sexual orientation variables and alcohol-use outcomes, as
well as interactions with sex and age. Main
effects are discussed first, followed by interaction effects.
Sexual Minority Status
As shown in Table 3, SMYs reported higher
rates of each of the outcomes than did exclusively heterosexual youths. Sexual-minority
youths were more likely to report lifetime
drinking and earlier drinking onset. They
were also more likely to report past-month
drinking and heavy episodic drinking, as well
as more drinking days in the past month.
Main effects between sexual-orientation
identity and alcohol use replicated those
reported previously. Youths who identified as
gay or lesbian, or bisexual or who were unsure
of their sexual identity yet reported same-sex
sexual behavior, were at higher risk than their
exclusively heterosexual counterparts for all
outcomes.
Youths who reported both male and female
sexual partners differed from those who
reported only opposite-sex sexual partners on
all outcomes. It was notable that youths who
reported only same-sex sexual partners differed significantly from those with only
opposite-sex sexual partners on just 1 outcome
(number of past-month drinking days).
Gay- or lesbian-identified youths differed
from exclusively heterosexual youths only in
that they reported more past-month drinking
days. By contrast, bisexual-identified youths,
and youths who identified as heterosexual
or unsure but reported same-sex sexual
behaviors had greater odds of lifetime drinking,
February 2014, Vol 104, No. 2 | American Journal of Public Health
past-month drinking, and past-month heavy
episodic drinking. These youths also began
drinking at earlier ages and reported a greater
number of past-month drinking days. Youths
who were unsure of their sexual identity
and not sexually active were less likely to
report lifetime drinking, past-month drinking,
or heavy episodic drinking; they also reported
fewer past-month drinking days.
Moderated Differences in Alcohol-Use
Patterns
There were 8 significant interaction effects
between sexual orientation indicators and sex.
The pattern of effects was robust across the
majority of interactions, indicating larger
alcohol-use disparities for girls than for boys,
on the basis of sexual-minority status. Sexualminority girls reported higher rates of lifetime
alcohol use and past-month heavy episodic
drinking than did sexual-minority boys, heterosexual girls, or heterosexual boys. More
than three quarters (81.3%) of sexualminority girls were lifetime drinkers, compared with 68.9% of sexual-minority boys,
66.9% of heterosexual girls, and 65.6% of
heterosexual boys. We found a similar pattern
in the interaction with sexual-orientation
identity. Nearly one third (30%) of sexualminority girls reported past-month heavy episodic drinking compared with 25.4% of
sexual-minority boys, 16.4% of heterosexual
girls, and 19.3% of heterosexual boys.
Bisexual-identified girls were more likely to be
lifetime drinkers than were bisexual-identified
boys or heterosexual girls or boys. Likewise,
girls with both-sex sexual partners were more
likely to be lifetime drinkers than were boys
with both-sex sexual partners and boys or girls
with only opposite-sex sexual partners. Girls
who reported only same-sex sexual partners
began drinking at earlier ages than did girls
who reported only opposite-sex sexual partners. Finally, compared with heterosexual
girls, bisexual-identified girls and girls who
were unsure of their sexual identity and not
sexually active reported younger drinking
onset.
For all outcomes except age of drinking
onset, participant age interacted with sexualminority status and sexual-orientation identity,
respectively. The pattern of interactions
indicated that sexual orientation---related
Talley et al. | Peer Reviewed | Research and Practice | 297
RESEARCH AND PRACTICE
TABLE 1—Sample Characteristics in Analysis of Sexual Orientation Status Differences in Alcohol Use Among Youths Aged 13 to 18 Years or
Older: Youth Risk Behavior Surveys, United States, 2005 and 2007
Sex
a
Race
Age
d
White
(n = 13 548),
No. (%)
Non-White
(n = 23 080),
No. (%)
13–15 y
(n = 15 500),
No. (%)
16–‡18 y
(n = 21 843),
No. (%)
5956 (32.1)
3738 (27.9)
7381 (33.7)
5727 (39.1)
5652 (25.3)
1021 (5.1)
1549 (8.0)
895 (4.6)
1111 (5.9)
698 (4.8)
1827 (8.4)
1162 (7.6)
1385 (5.9)
593 (4.1)
1376 (6.4)
996 (6.6)
3873 (11.0)
1844 (10.4)
1010 (4.4)
2002 (11.7)
1455 (10.8)
2329 (11.3)
1976 (14.0)
1880 (9.2)
13–14 y
8141 (24.6)
15–16 y
‡ 17 y
5918 (18.6)
1004 (3.2)
4429 (27.3)
3663 (22.0)
3608 (27.3)
4370 (21.9)
3577 (26.3)
4548 (23.6)
3172 (19.9)
501 (3.4)
2709 (17.3)
498 (3.1)
2611 (21.7)
414 (3.5)
3195 (15.4)
559 (2.9)
824 (6.5)
0 (0)
5077 (26.3)
1001 (5.3)
Never had alcohol
11 433 (30.7)
5408 (29.3)
5956 (32.1)
3738 (27.9)
7381 (33.7)
5727 (39.1)
5652 (25.3)
Reported lifetime use
23 553 (69.3)
11 863 (70.7)
11 532 (67.9)
9379 (72.1)
13 657 (66.3)
8536 (60.9)
14 902 (74.7)
Total Sample
(n = 37 543),
No.,b (%)
Female
(n = 18 471),
No. (%)
Male
(n = 18 822),
No. (%)
11 433 (30.7)
5408 (29.3)
£8 y
2595 (6.6)
9–10 y
2022 (5.2)
11–12 y
Variables
Age of drinking onset
Never had drink
< .001
Lifetime drinking
Pc
< .001
< .001
Past-mo drinking days
0d
Pc
< .001
< .001
< .001
< .001
< .001
21 605 (58.4)
10 722 (57.9)
10 782 (59.0)
6920 (51.9)
14 189 (65.5)
9900 (66.7)
11 626 (53.1)
1–2 d
6740 (20.7)
3598 (22.3)
3084 (19.1)
2760 (22.0)
3823 (19.3)
2506 (18.8)
4208 (22.0)
3–5 d
6–9 d
3063 (9.9)
1664 (5.7)
1528 (10.0)
782 (5.6)
1515 (10.0)
870 (5.9)
1546 (12.2)
936 (7.6)
1462 (7.5)
695 (3.7)
997 (7.5)
460 (3.6)
2058 (11.5)
1196 (7.1)
10–19 d
1028 (3.6)
462 (3.4)
560 (3.9)
557 (4.6)
448 (2.5)
292 (2.3)
732 (4.5)
20–29 d
224 (0.7)
83 (0.5)
138 (0.9)
112 (0.8)
102 (0.6)
67 (0.5)
153 (0.9)
30 d
308 (0.8)
76 (0.4)
224 (1.2)
121 (0.8)
172 (0.8)
91 (0.6)
190 (0.9)
Past-mo. drinking
NS
No
21 605 (58.4)
10 722 (57.9)
10 782 (59.0)
Yes
13 027 (41.6)
6530 (42.1)
6390 (41.0)
Past-mo HED
No
Yes
< .001
6920 (51.9)
14 189 (65.6)
6031 (48.1)
6703 (34.4)
< .001
29 385 (77.5)
14 768 (78.6)
14 443 (76.3)
6991 (22.5)
3286 (21.4)
3649 (23.7)
Sexual minority statuse
34 430 (91.6)
Sexual minority
3113 (8.4)
16 598 (89.7)
17 610 (93.4)
1872 (10.3)
9566 (70.7)
19 111 (84.4)
3723 (29.3)
3121 (15.6)
962 (7.5)
21 030 (90.7)
2050 (9.3)
< .001
4413 (33.3)
8537 (46.9)
13 040 (84.6)
16 234 (72.8)
2053 (15.4)
4874 (27.2)
14 366 (92.4)
19 909 (91.0)
1134 (7.6)
1934 (9.0)
10 629 (94.8)
14 684 (93.5)
< .001
.003
< .001
25 427 (94.0)
12 411 (92.5)
Sexual minority
Sex of sexual partners
1597 (6.0)
1006 (7.5)
Only opposite sex
14 972 (53.0)
6802 (49.4)
8170 (56.7)
5679 (51.7)
9002 (54.7)
4852 (42.2)
Both sex partners
1015 (3.7)
718 (5.3)
284 (2.0)
397 (3.7)
601 (3.6)
354 (3.1)
614 (2.2)
287 (1.9)
327 (2.4)
180 (1.9)
409 (2.4)
181 (1.7)
423 (2.4)
6809 (43.4)
5707 (38.9)
5617 (42.7)
6683 (39.3)
6516 (53.0)
6026 (33.9)
No sex partners
575 (4.5)
8875 (94.8)
15 961 (93.4)
489 (5.2)
1062 (6.6)
.001
Sexual majority
Only same sex
12 847 (95.5)
11 626 (53.1)
< .001
12 586 (92.5)
1212 (6.6)
Sexual orientation identity
< .001
9900 (66.7)
< .001
< .001
Sexual majority
< .001
12 598 (41.2)
Pc
578 (5.2)
995 (6.5)
.001
< .001
10 084 (59.7)
649 (4.0)
Note. HED = heavy episodic drinking; NS = not significant.
a
Totals may differ because of missing or excluded cases.
b
Unweighted frequencies (No.) are provided; percentages (%) reflect adjusted sampling weights.
c
P value for associated v2 test of independence using adjusted sampling weights.
d
Non-White defined as African American/Black, Hispanic/Latino, Asian, Native Hawaiian, Pacific Islander, American Indian, Alaska Native, multiple, or other.
e
Constructed from questions about sexual identity, behaviors, and attractions.
298 | Research and Practice | Peer Reviewed | Talley et al.
American Journal of Public Health | February 2014, Vol 104, No. 2
RESEARCH AND PRACTICE
TABLE 2—Cross-Tabulations for Sexual Orientation Variables by Alcohol Outcomes Among Youths Aged 13 to 18 Years or Older:
Youth Risk Behavior Surveys, United States, 2005 and 2007
Sexual Minority Status,a No. (%)
Sexual Orientation Identity, No. (%)
Sexual Majority
(n = 34 430)
Sexual Minority
(n = 3113)
10 752 (31.7)
2196 (6.0)
681 (19.4)
399 (12.8)
7829 (30.7)
1648 (6.2)
267 (17.2)
220 (13.5)
2507 (16.4)
1198 (7.3)
9–10 y
1785 (5.0)
237 (7.9)
1361 (5.2)
135 (9.2)
1022 (6.3)
100 (9.0)
53 (8.8)
11–12 y
3463 (10.7)
411 (14.9)
2613 (11.0)
215 (14.4)
1939 (13.2)
168 (17.8)
72 (14.4)
13–14 y
7435 (24.4)
706 (26.9)
5559 (25.3)
381 (26.4)
4040 (30.0)
264 (30.7)
138 (26.5)
15–16 y
5508 (18.9)
410 (15.5)
3986 (18.3)
238 (16.6)
3021 (22.9)
154 (17.1)
85 (16.2)
‡ 17 y
925 (3.3)
78 (2.6)
690 (3.3)
41 (2.6)
517 (3.9)
25 (1.9)
25 (4.5)
Variable
Sexual Majority
(n = 25 427)
Sex of Sexual Partners, No. (%)
Sexual Minority
(n = 1597)
Only Opposite Sex
(n = 14 972)
Both Sexes
(n = 1015)
Only Same Sex
(n = 614)
70 (7.6)
177 (15.8)
131 (18.1)
73 (11.4)
Age of drinking onset
Never had drink
£8 y
Lifetime drinking
Never had alcohol
Reported lifetime use
10 752 (31.7)
21 312 (68.3)
681 (19.4)
2241 (80.6)
7829 (30.7)
15 858 (69.3)
267 (17.2)
1231 (82.8)
2507 (16.4)
11 736 (83.6)
70 (7.6)
889 (92.4)
131 (18.1)
445 (81.9)
Past-mo drinking days
0d
20 279 (59.7)
1326 (43.6)
15 168 (60.1)
622 (41.7)
6663 (44.2)
288 (30.7)
236 (41.2)
1–2 d
6143 (20.5)
596 (23.1)
4680 (21.0)
347 (25.1)
3498 (25.9)
217 (24.1)
119 (23.9)
3–5 d
2751 (9.7)
312 (13.0)
2054 (9.6)
168 (12.6)
1788 (14.0)
164 (17.6)
56 (14.1)
6–9 d
1491 (5.5)
174 (8.6)
1049 (5.1)
91 (8.5)
1023 (8.6)
86 (11.0)
35 (10.0)
10–19 d
884 (3.4)
144 (6.5)
613 (3.1)
80 (6.3)
625 (5.4)
68 (8.9)
30 (7.0)
20–29 d
30 d
188 (0.6)
204 (0.6)
36 (1.6)
103 (3.5)
118 (0.5)
143 (0.6)
16 (1.5)
66 (4.4)
124 (1.0)
144 (0.9)
16 (2.1)
65 (5.6)
9 (1.2)
17 (2.6)
Past-mo drinking
No
20 279 (59.7)
1326 (43.6)
15 168 (60.1)
622 (41.7)
6663 (44.2)
288 (30.7)
236 (41.2)
Yes
11 661 (40.3)
1365 (56.4)
8658 (39.9)
769 (58.3)
7202 (55.8)
617 (69.3)
268 (58.8)
No
27 294 (78.5)
2091 (66.0)
20 500 (79.6)
1033 (65.0)
10 499 (67.5)
553 (54.7)
395 (64.1)
Yes
6135 (21.5)
856 (34.0)
4374 (20.4)
485 (35.0)
4088 (32.5)
411 (45.3)
167 (35.9)
Past-mo HED
Note. HED = heavy episodic drinking. Unweighted frequencies (No.) are provided; percentages (%) reflect adjusted sampling weights. Age of drinking onset = “How old were you when you had your
first drink of alcohol other than a few sips?” (continuous); past-mo. drinking days = “During the past 30 days, on how many days did you have at least one drink of alcohol?” (Poisson with
overdispersion factor); lifetime drinking = dichotomous indicator of whether participants had had at least 1 drink of alcohol in their lifetime (binary); past-mo. drinking = dichotomous indicator of
whether participants had had at least 1 drink of alcohol in the past month (binary); past-mo. HED = “During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row,
that is, within a couple of hours?” used to create a dichotomous indicator of whether participants had had at least 1 heavy drinking episode in the past month (binary). All v2 test for independence
results were significant (P < .001) with adjusted sampling weights. Totals may differ because of missing or excluded cases.
a
Constructed from questions about sexual identity, behaviors, and attractions.
alcohol-use disparities were larger for
younger than for older participants. Two
thirds (76%) of older SMYs reported lifetime
alcohol use—a 4% higher rate than older
heterosexual youths. In contrast, the rate of
lifetime alcohol use among younger SMYs
(74%) was 16% higher than among younger
heterosexual youths. We found similar
patterns for past-month drinking and heavy
episodic drinking.
We found only 2 significant interaction
effects between sexual orientation and race/
ethnicity in predicting outcomes. The first
interaction (OR = 0.65; 95% CI = 0.47, 0.89;
P = .008) showed that, similar to White SMYs
who reported higher lifetime drinking rates
than White exclusively heterosexual youths
(79.9% vs 69.1%), Asian SMYs were more
likely than their exclusively heterosexual
counterparts to report lifetime drinking
(54.8% vs 46.2%). The second interaction
(b = –0.48; SE = 0.19; 95% CI = -0.85, -0.11),
showed that bisexual White and racial/ethnic
minority youths initiated drinking at similar
ages, whereas exclusively heterosexual racial/
ethnic minorities were significantly younger
February 2014, Vol 104, No. 2 | American Journal of Public Health
than their White counterparts when they first
drank alcohol.
DISCUSSION
Consistent with findings from previous
studies,20,25 SMYs—whether defined on the
basis of any sexual minority identity, behavior,
or attraction, or on sexual identity alone—were
more likely than heterosexual youths to
report lifetime drinking and to have initiated
drinking at younger ages. Also consistent with
previous work,26---28,34,46 SMYs in the current
Talley et al. | Peer Reviewed | Research and Practice | 299
RESEARCH AND PRACTICE
TABLE 3—Estimated Coefficients From Hierarchical Linear Models in Analysis of Sexual Orientation Status Differences in Alcohol Use Among
Youths Aged 13 to 18 Years or Older: Youth Risk Behavior Surveys, United States, 2005 and 2007
Age of Drinking Onset
Sexual Orientation Status Differences
Lifetime Drinking
b
SE
95% CI
b
OR (95% CI)
...
...
...
...
...
–0.33
0.06
–0.45, –0.21
0.44
1.55 (1.33, 1.82)
–0.56
–0.38
0.57 (0.40, 0.82)
0.68 (0.53, 0.88)
Past-Mo. Drinking Days
b
Past-Mo. Drinking
OR (95% CI)
Past-Mo. HED
RR (95% CI)
B
B
OR (95% CI)
...
...
...
...
...
...
0.37
1.44 (1.41, 1.48)
0.40
1.49 (1.34, 1.65)
0.36
1.44 (1.29, 1.60)
–0.45
0.64 (0.60, 0.68)
–0.38
0.69 (0.55, 0.86)
–0.40
0.55
0.67 (0.54, 0.84)
0.58 (0.46, 0.73)
Sexual minority status
Sexual majority (Ref)
Sexual minority
· Sexa,b
· Agea,c
Sexual orientation identity
...
...
...
...
...
...
...
...
...
...
...
–0.27
0.08
–0.43, –0.11
0.66
1.94 (1.63, 2.32)
0.39
1.48 (1.43, 1.53)
0.49
1.63 (1.41, 1.88)
0.41
1.51 (1.30, 1.75)
· Sexa,b
–0.93
0.39 (0.28, 0.57)
· Agea,c
–0.62
0.54 (0.37, 0.78)
–0.57
0.57 (0.53, 0.61)
–0.57
0.57 (0.42, 0.77)
–0.60
0.55 (0.40, 0.75)
Sexual majority (Ref)
Sexual minority
Sex of sexual partners
OSPs (Ref)
SSPs
...
–0.05
...
0.15
...
–0.34, 0.24
· Sexa,b
0.91
0.30
0.32, 1.50
–0.52
0.09
–0.70, –0.34
...
–0.13
...
0.88 (0.65, 1.19)
...
0.15
...
1.16 (1.10, 1.23)
...
0.05
...
1.05 (0.83, 1.33)
...
0.05
...
1.05 (0.83, 1.33)
0.89
2.45 (1.77, 3.38)
0.44
1.56 (1.50, 1.61)
0.48
1.62 (1.34, 1.95)
0.46
1.59 (1.34, 1.88)
–0.80
0.45 (0.24, 0.84)
–0.57
0.57 (0.37, 0.87)
–0.62
0.54 (0.36, 0.79)
· Agea,c
Both-sex partners
· Sexa,b
· Agea,c
Sexual orientation subgroups
...
–0.13
...
0.20
...
–0.52, 0.26
–0.39
0.10
–0.56
0.23
–0.32
Not sure, no SP
· Sexa,b
Heterosexual, no SP or OSP (Ref)
Gay or lesbian
...
–0.01
...
0.99 (0.71, 1.39)
...
0.32
...
1.38 (1.27, 1.51)
...
0.14
...
1.15 (0.83, 1.58)
...
0.17
...
1.19 (0.83, 1.70)
–0.59, –0.19
1.00
2.73 (2.11, 3.52)
0.46
1.59 (1.52, 1.67)
0.66
1.93 (1.60, 2.33)
0.49
1.63 (1.35, 1.97)
–0.01, –0.11
–0.80
0.45 (0.28, 0.73)
0.14
–0.59, –0.05
0.79
2.21 (1.55, 3.15)
0.50
1.66 (1.57, 1.75)
0.56
1.75 (1.36, 2.26)
0.55
1.74 (1.37, 2.21)
–0.52
0.31
–1.13, 0.09
–0.93
0.39 (0.27, 0.56)
–1.01
0.37 (0.29, 0.47)
–1.08
0.34 (0.21, 0.55)
–0.79
0.45 (0.25, 0.84)
1.45
0.58
0.31, 2.59
0.01
0.23
–0.44, 0.46
0.38
1.46 (0.89, 2.38)
0.31
1.36 (1.22, 1.52)
0.34
1.35 (0.90, 2.02)
0.22
1.25 (0.82, 1.90)
· Sexa,b
· Agea,c
Bisexual
· Sexa,b
· Agea,c
Heterosexual or not sure, SSP
· Sexa,b
· Agea,c
· Agea,c
Not sure, OSP
· Sexa,b
· Agea,c
Note. CI = confidence interval; HED = heavy episodic drinking; OR = odds ratio; OSP = opposite-sex partner; RR = rate ratio; SP = sexual partner; SSP = same-sex partner. All models included sex,
age, race/ethnicity, and covariates (age of first sexual intercourse; history of sexual assault). Age of drinking onset = “How old were you when you had your first drink of alcohol other than a few
sips?” (continuous); past-mo. drinking days = “During the past 30 days, on how many days did you have at least one drink of alcohol?” (Poisson with overdispersion factor); lifetime drinking =
dichotomous indicator of whether participants had had at least 1 drink of alcohol in their lifetime (binary); past-mo. drinking = dichotomous indicator of whether participants had had at least 1
drink of alcohol in the past-month (binary); past-mo. HED = “During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row, that is, within a couple of hours?” used to
create a dichotomous indicator of whether participants had had at least 1 heavy drinking episode in the past-month (binary). Sexual orientation subgroups: gay or lesbian and bisexual subgroups
were based on sexual identity, regardless of behavior. Heterosexual or not sure, SSP, were participants who identified as heterosexual or were unsure of their sexual identity yet also reported any
same-sex sexual partners. Not sure, OSP, were participants who were unsure of their identity and reported only opposite-sex partners. Not sure, no SP, were those unsure of their identity and not
sexually active. Heterosexual, no SP or OSP, were participants who identified as heterosexual and reported that they were not sexually active or only had opposite-sex sexual partners.
a
Only significant interaction model results are presented. Because of the paucity of interaction effects related to race/ethnicity, these results were not included in Table 3 and only appear in text.
b
Sex: female = 0; male = 1.
c
Age: 13–15 y = 0; 16–18 y or older = 1.
300 | Research and Practice | Peer Reviewed | Talley et al.
American Journal of Public Health | February 2014, Vol 104, No. 2
RESEARCH AND PRACTICE
study were more likely to report any drinking,
more frequent drinking, and heavy episodic
drinking in the past month.
In contrast to other studies that combined
youths reporting only same-sex sexual partners
with those reporting both-sex sexual partners,25---31,47 we examined these groups separately. Although youths with same-sex sexual
partners and those with both-sex sexual partners reported more frequent drinking in the
previous month than youths with only
opposite-sex sexual partners, youths with only
same-sex sexual partners did not differ significantly from those with only opposite-sex sexual
partners on any of the other 4 outcomes.
However, youths who reported both male and
female sexual partners showed consistently
higher risk for all outcomes.22,28
By examining diverse subgroups of SMYs,
we found insight about subgroups that may be
at greater risk for problematic drinking. For
example, consistent with Udry and Chantala,22
gay- or lesbian-identified youths in our study
did not show heightened risk on most outcomes. That is, gay or lesbian youths did not
differ from heterosexual youths on age of
drinking onset, lifetime drinking, and pastmonth drinking or heavy episodic drinking,
whereas bisexual-identified youths and heterosexual or unsure youths who reported
same-sex sexual partners were more likely than
exclusively heterosexual youths to report these
drinking outcomes.
Consistent with previous research
findings, which have suggested that
heterosexual-identified women who report
discordant (i.e., same-sex) sexual behavior are
at heightened risk for alcohol-use disorders
compared with behaviorally concordant
heterosexual women,48 our results provide
evidence that discordant sexual identity and
behavior may be a marker of risk for
alcohol-related problems.49 This finding
points to the value of interventions aimed at
validating and supporting the development of
minority sexual identities, with the ultimate
goal of reducing sexual-orientation healthrelated disparities.
Findings from the examination of interactions among sexual orientation indicators and
sex, age, and race/ethnicity support previous
research on alcohol use among SMYs and can
be used to inform tailored alcohol-use
interventions. For example, our findings are
consistent with studies that have shown greater
drinking-related disparities among sexualminority females than sexual-minority
males.22,23,34 Moreover, current findings show
that sexual orientation---related alcohol-use
disparities are larger among younger than
older adolescents. Although previous research
has not explicitly examined how the relation
between sexual orientation and alcohol use is
moderated by age, findings from longitudinal
studies have indicated that alcohol-use disparities may become less or more pronounced
during emerging adulthood depending on
which subgroup is examined.50,51 Unlike most
previous studies in which sample sizes were too
small to permit racial/ethnic---group comparisons, we examined differences in alcohol-use
patterns across racial/ethnic groups. Our results are consistent with one study of sexualminority adult women19 that found fewer
racial/ethnic differences in alcohol use among
sexual-minority than among heterosexual
women. Findings suggest that racial/ethnic
minority status may not provide the same
level of protection against alcohol-related
problems among sexual minorities as among
heterosexuals.
Despite numerous strengths, this study has
limitations. First, because of content variations
in surveys across jurisdictions, we were unable
to include data from all jurisdictions in all
analyses. Second, we based variables included
in the study on single questions, limiting understanding of alcohol-use history and consequences, such as symptoms of alcohol-use
disorders. Third, we generally constructed
nominal sexual-orientation subgroups from
identity categories, combining sexually active
and non---sexually active youths. Although we
further distinguished some youths (e.g., those
unsure of their sexual identity) on the basis of
sexual behavior, this was not the case across all
identity categories. We chose to present findings by using a variety of operationalizations of
sexual orientation to allow for comparisons
across models. Fourth, readers should be aware
of the inherent potential for response bias in
surveys that assess sensitive topics such as
sexual orientation and underage alcohol use.
Finally, unlike other studies that provide
a broader range of sexual-identity response
options,50,52,53 YRBS participants chose
February 2014, Vol 104, No. 2 | American Journal of Public Health
among heterosexual, lesbian or gay, bisexual,
or unsure. Intermediate response options, such
as mostly heterosexual or mostly lesbian or gay
were not included despite assertions that
these identities may be particularly salient for
SMYs.29,54 A broader range of options could
provide valuable, nuanced information about
subgroups at greatest risk.
Future research is needed to better understand at which points of the sexual-identity
development process alcohol use or misuse is
most likely to occur. More information is
needed about predictors of early onset of
alcohol use as well as predictors of frequent
and heavy alcohol use among SMYs.51,54 More
generally, the field would benefit from further
empirical work that focuses on identifying
explanatory or mediating mechanisms, such as
psychiatric comorbidities, of known alcoholuse disparities. Additional information about
long-term consequences of alcohol use, particularly frequent or heavy use, among SMYs is
also needed.
Findings from our study provide information
that can be used in the development of more
effective prevention and early intervention
strategies to reduce sexual-orientation alcoholrelated disparities. In particular, strategies are
needed that focus on delaying the initiation of
alcohol use as well as preventing frequent or
heavy use. In addition to understanding the
overall greater risk of hazardous drinking
among SMYs, it is important to know that
sexual-minority girls, younger SMYs, and SMYs
who are also racial/ethnic minorities may be at
particularly high risk for early initiation of
drinking and for hazardous drinking. In light of
consistent findings that bisexual youths are at
particularly high risk for hazardous drinking,
additional research is needed to determine what
aspects of bisexual orientation (e.g., identity,
attraction, or behavior) are most predictive of
risk. Ultimately, school- or community-based
prevention or intervention programs may benefit from including messages that are inclusive of
SMYs and that target these at-risk groups. j
About the Authors
Amelia E. Talley is with the Department of Psychology,
Texas Tech University, Lubbock. Tonda L. Hughes and
Frances Aranda are with the Department of Health Systems
Science, the University of Illinois at Chicago. Michelle
Birkett is with the Department of Medical Social Sciences,
Northwestern University, Chicago. Michael P. Marshal is
Talley et al. | Peer Reviewed | Research and Practice | 301
RESEARCH AND PRACTICE
with the Department of Psychiatry and Pediatrics, the
University of Pittsburgh, Pittsburgh, PA.
Correspondence should be sent to Amelia E. Talley, PhD,
Texas Tech University, Psychology Building, Room 217,
Lubbock, TX 79409 (e-mail: amelia.talley@TTU.edu).
Reprints can be ordered at http://www.ajph.org by clicking
the “Reprints” link.
This article was accepted August 13, 2013.
Contributors
A. E. Talley and T. L. Hughes contributed equally to this
article as lead coauthors. All authors contributed extensively to the conceptualization of the research questions
and analytic approach. A. E. Talley and F. Aranda culled
descriptive statistics. M. Birkett assembled the input data
file and conducted the primary inferential statistical tests.
A. E. Talley, T. L. Hughes, F. Aranda, and M. P. Marshal
aided in the literature review, provided conceptual advice,
and contributed to the writing of the article.
Acknowledgments
This project was supported by a grant from the Eunice
Kennedy Shriver National Institute of Child Health and
Human Development (award R21HD051178) and by
the IMPACT LGBT Health and Development Program at
Northwestern University. The authors would also like to
acknowledge support to A. E. Talley (K99 AA019974),
T. L. Hughes (R01 AA13328), and M. P. Marshal
(DA030385; DA026312).
Assistance from the Centers for Disease Control and
Prevention, Division of Adolescent and School Health,
and the work of the state and local health and education
departments who conduct the Youth Risk Behavior
Survey made the project possible.
Note. The content is solely the responsibility of the
authors and does not necessarily represent the official
views of the National Institutes of Health, the Centers for
Disease Control and Prevention, or any agencies involved
in collecting the data.
Human Participant Protection
Human participant protection approval was unnecessary
because Youth Risk Behavior Survey data were
de-identified and are publicly available.
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permission.
Clin Child Fam Psychol Rev (2014) 17:1–18
DOI 10.1007/s10567-013-0135-1
Growing Up Wired: Social Networking Sites and Adolescent
Psychosocial Development
Lauren A. Spies Shapiro • Gayla Margolin
Published online: 4 May 2013
Springer Science+Business Media New York 2013
Abstract Since the advent of social networking site
(SNS) technologies, adolescents’ use of these technologies
has expanded and is now a primary way of communicating
with and acquiring information about others in their social
network. Overall, adolescents and young adults’ stated
motivations for using SNSs are quite similar to more traditional forms of communication—to stay in touch with
friends, make plans, get to know people better, and present
oneself to others. We begin with a summary of theories that
describe the role of SNSs in adolescents’ interpersonal
relationships, as well as common methodologies used in
this field of research thus far. Then, with the social changes
that occur throughout adolescence as a backdrop, we
address the ways in which SNSs intersect with key tasks of
adolescent psychosocial development, specifically peer
affiliation and friendship quality, as well as identity
development. Evidence suggests that SNSs differentially
relate to adolescents’ social connectivity and identity
development, with sociability, self-esteem, and nature of
SNS feedback as important potential moderators. We
synthesize current findings, highlight unanswered questions, and recommend both methodological and theoretical
directions for future research.
Keywords Adolescent psychosocial development
Social networking sites Friendships Identity
Self-esteem
L. A. Spies Shapiro (&) G. Margolin
Psychology Department-SGM 922, University
of Southern California, 3620 McClintock, Los Angeles,
CA 90089-1061, USA
e-mail: lspies@usc.edu
Introduction
Although computers initially were developed for adults,
adolescents have fully embraced these technologies for
their own social purposes and typically are the family
experts on how to use electronic media and social networking sites (SNSs). Adolescents and young adults initially dominated SNSs such as MySpace and Facebook,
with parents often following their children into this youthdriven phenomenon. The preponderance of adolescents has
access to and engages in use of SNSs: Based on relatively
recent data, although perhaps presently an underestimate,
73 % use social networking sites (Lenhart 2009, 2012;
Lenhart et al. 2010). Moreover, despite the terms of service
of Facebook restricting its use to those age 13 or older, it is
estimated that 7.5 million younger children also have
accounts (‘‘That Facebook Friend’’ 2011). The sheer
amount of time that adolescents and young adults spend
using electronic media is perhaps the most revealing: on
average, 11–18 year olds spend over 11 h per day exposed
to electronic media (Kaiser Family Foundation 2010). Late
adolescents and emerging adults average approximately
30 min per day just on Facebook alone (Pempek et al.
2009). Many adolescents begin and end their day by
checking SNS posts. Furthermore, SNS use commonly
disrupts adolescents’ solitary activities as well as their
ongoing face-to-face interactions. The presence of SNS use
in many adolescents’ lives is thus indisputable; however,
the impact on adolescents’ individual development and
social lives is only starting to be understood.
Scientific study of adolescence has long targeted the
development of one’s identity and the formation of
friendships and peer relationships as important topics of
study (Institute of Medicine 2010). Two of the key tasks in
adolescence are ‘‘to stand out—to develop an identity and
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2
pursue autonomy… and to fit in—to find comfortable
affiliations and gain acceptance from peers’’ (Brown 2008).
Although seemingly divergent goals, the interplay between
the need for one’s own personal identity and the need for
close personal ties and strong group affiliations permeates
all domains of adolescents’ everyday lives (Crosnoe and
Johnson 2011) and clearly intersects with SNS use. The
literature on SNSs and adolescents’ quest to fit in examines
whether SNS use extends and deepens adolescents’ ongoing relationships or expands their contacts in new directions. Whereas childhood friendships are rooted in shared
interests and activities, close friendships in adolescence
involve trust, self-disclosure, and loyalty (Collins and
Steinberg 2006; Brown and Larson 2009). SNSs potentially
offer additional avenues for support and communication—
crucial to the development of age-appropriate adolescent
relationships; yet, there are questions to be addressed about
why adolescents might differentially benefit from SNSs.
Social networking sites offer adolescents new opportunities as well as new challenges to express to the world who
one is. In one-on-one communications within SNSs (e.g.,
‘‘Facebook messages’’), adolescents can express their likes
and dislikes as well as their worldviews and get immediate
feedback. With SNSs, adolescents express their views and
the recipients of this information include both known as well
as unknown targets. Although there has been variability over
time in the specific format of SNS profiles, adolescents have
the option of choosing what self-identifying information to
provide. Thus, with the advent of SNSs, most adolescents
will widely share, with varying degrees of accuracy, honesty, and openness, information that previously would have
been private or reserved for select individuals. Key questions include whether adolescents accurately portray their
identities online, and whether use of SNSs might impact
adolescents’ identity development.
Straddling these two developmental tasks, adolescents
also can join Internet ‘‘groups’’ reflecting the aspects of
their identity that they wish to explore or deepen. Thus,
SNSs may simultaneously amplify dimensions of selfidentify and extend group identities. Moreover, SNSs create more publicly prominent avenues for adolescents to
commit to preferred activities, groups, and, in some cases,
beliefs.
Social comparison is another dimension of SNSs that is
highly relevant to adolescents. Invitations to social gatherings, such as a spontaneous party, and good news, such as
a won football game or a college acceptance, can be shared
and congratulated but also serve as a point of comparison
for one’s own accomplishments. Similarly, distressing or
objectionable information—including unflattering and
compromising pictures, untrue information, or unfortunate
news, for example, a car accident or an arrest—can spread
throughout adolescents’ social network and beyond in a
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Clin Child Fam Psychol Rev (2014) 17:1–18
nanosecond. Teens’ tendencies to share information
impulsively, coupled with the power of SNSs for rapid and
widely distributed communications, can have important
ramifications for teens’ personal and interpersonal worlds.
Goals and Scope of the Present Paper
This literature review examines how SNSs intersect with
and impact adolescents’ social and identity development.
After first summarizing theoretical perspectives that provide a framework for SNS use and implications for adolescents, we then review the extant literature on SNSs and
(a) adolescent social relationships, as well as (b) identity
development. Finally, we offer suggestions for future
directions, which call for more nuanced investigations of
SNSs that focus less on positive versus negative impacts
and more on the mechanisms by which SNSs both reflect
and shape varied dimensions of adolescents’ lives.
Beyond the Scope Here
There are also several important topics in the SNS literature that deserves brief mention because of their relevance
for adolescent development but are outside of the scope of
this paper. Specifically, these topics include cyber-bullying, the Internet and parent–child relationships, Internet
addiction, and the impact of SNSs on sleep and academic
performance.
Cyber-Bullying
Considerable attention has been directed to the negative
side of SNSs, namely online bullying, harassment, and
humiliation, which have been extensively detailed in other
reviews (Strom and Strom 2005; Tokunaga 2010). Beyond
findings that the vast majority of adolescents encounter
some degree of negative experience through SNSs, the
viciousness of online bullying is exacerbated due to the
depersonalized yet public nature of technology-based
postings coupled with the pervasiveness of SNSs (Bazelon
2013).
SNSs and Parent–Child Relations
Social networking site use further complicates parent–child
relationships during adolescence (Mesch 2003, 2006; Subrahmanyam and Greenfield 2008; Punamaki et al. 2009).
As adolescents’ preoccupation with SNSs potentially takes
priority over and interferes with everyday family activities,
SNS use among adolescents has been linked with greater
parent–youth conflict (Subrahmanyam and Greenfield
2008; Mesch 2006) and less time with parents (Lee 2009).
Although some parents are unaware of what their teens are
Clin Child Fam Psychol Rev (2014) 17:1–18
posting online, other parents utilize SNSs to maintain
greater contact with their teens, requiring them to be connected to them through their SNS of choice (Kanter et al.
2012). It is worth noting that changes in the parent–child
relationship associated with SNSs are likely to influence
peer relations and vice versa. However, the extant literatures on how computer-mediated communications impact
these two domains are relatively distinct with only a few
exceptions (e.g., Punamaki et al. 2009; Subrahmanyam and
Greenfield 2008).
Internet Addiction
A note about extreme use of the Internet is in order, as
distinctions often are blurred between Internet addiction
and subthreshold, albeit heavy use, of SNSs. Extreme
degrees of Internet and electronic media use are increasingly recognized as Internet addiction, a disorder with
symptoms that are analogous to those of substance use and
gambling disorders. Findings from epidemiological studies
of Internet addiction in youth vary, with prevalence rates
ranging widely from less than one percent to 38 %
(Aboujaoude 2010; Leung 2004). Some adolescents may
be more vulnerable to develop symptoms of Internet
addiction than others, including those experiencing other
psychological symptoms and disorders such as depression,
ADHD symptoms, or hostility (Ha et al. 2007; Yen et al.
2007). Researchers recently proposed the concept of
‘‘Facebook addiction’’ and developed a scale to measure
the symptoms of addiction related to Facebook use specifically (Andreassen et al. 2012).
Sleep Disturbance
Adolescents’ use of the computer, including use of computer-mediated communication, has been related to disruptions in sleep. A study of computer use in relation to
adolescents’ sleep quality, perceived health, and tiredness
upon awakening found that for young adolescent boys,
intensive computer use was associated with less sleep and
more irregular sleep, which in turn related to poorer perceived health (Punamaki et al. 2007). Similarly, a sample
of high school seniors with Internet addiction and overuse
reported greater daytime sleepiness (Choi et al. 2009).
Research demonstrates that the use of computers before
bed relates to sleep disruptions in adolescents (see Cain and
Gradisar 2010 for review).
Academic Disturbance
There is recent evidence that SNS use can also hinder
academic performance (Huang and Leung 2009; Jacobsen
3
and Forste 2011; Kirschner and Karpinski 2010). Proposed
mechanisms for the link between SNS use and lower academic performance include less total time studying as well
as inefficient studying due to multitasking (Jacobsen and
Forste 2011; Junco and Cotten 2012; Kirschner and Karpinski 2010) and could also reflect the sleep disturbance.
College students who used Facebook had lower GPAs and
spent less time studying than those who did not use
Facebook (Kirschner and Karpinski 2010), despite no differences regarding total time spent online. Another study
showed that two-thirds of students reported using electronic media during class, while studying, or while doing
homework, with amount of electronic media use negatively
associated with self-reported GPA (Jacobsen and Forste
2011). Some recent evidence shows that Facebook use
specifically relates to lower college GPAs (Junco and
Cotten 2012), whereas other studies indicate that computer
use is detrimental to the academic performance of some,
but not all adolescents (Hofferth and Moon 2011).
Theories Relating SNSs to Psychosocial Development
Theories examining SNSs and adolescent development
address for whom and under what circumstances SNSs
accord advantages versus disadvantages for adolescent
development. Two theoretical questions in particular are
examined here in order to conceptualize how SNSs impact
adolescents’ social connectivity as well as their identity
development.
In What Ways Does SNS Use Advance the Goal
of Establishing Close Interpersonal Ties for Adolescents?
Some theories contend that SNS use is generally beneficial
for the enhancement of adolescents’ social connections.
For example, the stimulation hypothesis (McKenna and
Bargh 2000) describes how adolescents in general have an
easier time self-disclosing in online versus face-to-face
communication, which is a less threatening format in which
adolescents can share more freely. With self-disclosure
facilitating relationship closeness, this theory also posits
that online communications lead to closer, higher-quality
friendships among adolescents. Second, the rich-get-richer
hypothesis posits a stratified advantage for SNS use, that is,
for highly sociable adolescents, there are added benefits
from extending options for communication through electronic means (Kraut et al. 2002), and iterative effects such
that more online communication relates to more cohesive
relationships overall (Lee 2009). However, it is also
hypothesized that individuals with limited offline social
networks and poor social skills do not develop quality
friendships through online connections and may spend time
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4
engaging in low-quality connections in lieu of cultivating
relationships in real life. It further has been suggested that
spending excessive amounts of time on SNSs actually can
lead to symptoms of depression, which then increase the
risk for social isolation (O’Keeffe et al. 2011). Thus, these
theoretical perspectives provide potential frameworks for
hypotheses about who benefits most from SNS use and who
might experience SNS use as detrimental.
Another perspective proposes differential impacts associated with SNS use but actually gives the advantage to
those who are disenfranchised in face-to-face communications. The social compensation hypothesis (McKenna
et al. 2002) proposes that adolescents who are uncomfortable interacting with peers in face-to-face contexts are
better able to develop social networks and meet their social
needs online where certain channels of communication,
including voice tone, eye contact, and facial expressions,
are not available. That is, the more limited number of
communication channels of SNSs may offer unique benefits to those who are uncomfortable with face-to-face
interaction, whereas others do not directly benefit (McKenna et al. 2002).
All three of the theories mentioned thus far focus on
relationship benefits as contrasted with the earlier, and
largely discredited reduction hypothesis, stating that
forming friendships with strangers online that are low in
quality detracts from time spent cultivating pre-existing
offline friendships (Locke 1998). This earlier theory,
however, emerged in response to Internet use more generally, before the advent of SNSs, and before large numbers of adolescents had access to the Internet.
In What Ways Can SNS Use Foster Identity Development
for Adolescents?
There are two dimensions of SNS use that may contribute
to adolescents’ development of self-identity. First, SNS use
provides opportunities for self-disclosure and, in some
circumstances, demands self-disclosure, which plays a role
in adolescents’ identity development. Decisions about how
adolescents identify themselves, the feedback received on
these decisions, and how they view their own profile in
comparison with others’ profiles are potential factors in
individual identity. The hyperpersonal model for computer-mediated communication, for example, posits that
adolescents engage in selective self-presentations online;
moreover, the feedback from these presentations may, in
turn, alter individuals’ self-perceptions (Walther et al.
2011). Second, the Internet makes it feasible for some
adolescents to affiliate with other, likeminded individuals
online when such opportunities may not be possible in
face-to-face interaction. The Internet allows adolescents to
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Clin Child Fam Psychol Rev (2014) 17:1–18
make connections with persons like themselves, that is,
ethnic or sexual minority youth (Larson et al. 2002), particularly if such affiliations are not available through local
peer networks. Adolescents can join ‘‘groups’’ reflecting
aspects of their identity that they wish to explore or deepen
and thereby foster a group identity. Relatedly, adolescents
can explore and expand their ideas and interests into new
arenas through the Internet, for example, communicating
with others from more diverse backgrounds and expanding
into new intellectual, political, and social networks that
create opportunities for transnational and global connections (Markstrom 2010). Such connections can broaden as
well as deepen self-identity while, at the same time,
enhance feelings of belongingness and affiliation.
Literature Review
Method for Review
To examine the intersection of SNS use and adolescent
development, we conducted a search on PsycInfo and
Google Scholar using several inclusion criteria. First, we
searched for articles examining the use of SNSs from 2006
to the present, as 2006 is the year that Facebook opened to
any individual over 13 who had a valid email address
(Abram 2006). Keywords searched included the combinations of ‘‘adolescent’’ with ‘‘Internet communication,’’
‘‘electronic communication,’’ ‘‘social networking site,’’
‘‘computer-mediated communication,’’ ‘‘Facebook,’’ and
‘‘MySpace,’’ in conjunction with search terms related to the
key tasks of adolescent development, including ‘‘peer
relationships,’’ ‘‘friendship quality,’’ ‘‘identity,’’ ‘‘intimacy,’’ and ‘‘autonomy.’’ We also examined related articles from reference lists of the resulting studies from the
above searches. In this literature review, we include articles
that address the relationship between SNS use and tasks of
adolescent development, focusing on peer relationship and
identity development, specifically with an emphasis on
studies including adolescent samples. In addition, select
articles examining college samples were included that link
SNS use to outcomes relevant to adolescent development.
Because this literature is growing at an unusually rapid
pace (Wilson et al. 2012), our review identifies and synthesizes representative articles of the present topics of
review.
With the frequent introduction of new technology and
applications, characteristics of SNSs also change rapidly.
Whereas ‘‘MySpace’’ was once the SNS of choice and the
subject of early research (Kujath 2011), this SNS is rarely
used today. Facebook is now the SNS of choice (Chubb
2010), but this, too, is potentially losing popularity (Guynn
Clin Child Fam Psychol Rev (2014) 17:1–18
and Faughnder 2012) with other SNSs briefly taking hold,
for example, Formspring, an innovative, more anonymous
SNS that originated in 2009 and then shut down in spring
2013. In particular for this research domain, the rapidly
evolving modifications in technology and consequent
alterations in adolescents’ use of the technology present
challenges when designing, conducting, and comparing
studies on SNS use (Wilson et al. 2012). Different findings
not only are attributable to different research methods but
also to changes in SNSs and their functionality.
Measurement of Adolescents’ Use of and Response
to SNSs
Self-Report
The majority of studies to date examining adolescents’ use
of SNSs are based on investigator-developed questions to
elicit respondents’ self-report of SNS behaviors. Behaviors
most commonly assessed include frequency of use, with
questions typically inquiring about general use on average
(e.g., Pempek et al. 2009; Reich et al. 2012), duration of
use (e.g., Chou and Edge 2012), and, less frequently, time
of use, for example, ‘‘after lights out’’ (Van den Bulck
2007). Internet use and related behaviors are sometimes
measured with the Internet Addiction Test, a 20-item scale
assessing compulsive use, mood changes, and impairment
of functioning due to Internet use (IAT; Young 1998, for
example, ‘‘Do you feel depressed, moody, or nervous when
you are offline, which goes away once you are back
online?’’). Shorter self-report questionnaires also are
available including Morahan-Martin and Shumacher’s
(2000) 13-item scale that assesses similar issues, including
distress, academic decline, and interpersonal problems
related to Internet use. Of note, these measures assess
Internet use in general and do not single out SNS use.
Several investigators (e.g., Kirschner and Karpinski
2010; Punamaki et al. 2009) acknowledge potential limitations associated with possible self-report biases. For
example, questions assessing adolescents’ number of
Facebook friends may inadvertently pull for inflated
answers because of adolescents’ desire to appear more
popular. Adolescents also might underestimate the degree
to which SNS use interferes with their daily activities,
similar to the underreporting of other problem behaviors.
Furthermore, it may be difficult for adolescents to report
how much time they spend on SNSs, particularly if they are
multitasking with homework, watching television, or even
eating dinner with their family; yet, self-report questionnaires do not always assess the context in which SNS use is
taking place. Adolescents also may keep their profile page
open throughout the day even though their activities on
SNSs may occur in bursts.
5
Experimental Studies
Other studies used experimental conditions that manipulate
some feature of SNSs to investigate the impacts of that
feature. In a study designed to capture the public nature of
SNS use, Gonzales and Hancock (2011) asked participants
to complete questionnaires either in front of a mirror or in
front of their Facebook profile page. Thomaes et al. (2010)
manipulated the feedback participants received (positive
versus negative) in response to a personal profile they
created on the Internet as part of a game, and self-esteem
was measured at three points during the laboratory procedure. After creating a profile, participants were exposed to
feedback from confederates judging their profiles.
Haferkamp and Kramer (2011), in contrast, highlighted the
social comparison aspect of SNSs by testing the effects of
viewing others’ SNS profile pages on individuals’ body
image and career satisfaction; these investigators presented
participants with online profiles depicting those who were
either attractive or unattractive and those with either high
or low occupational success. These standardized, simulated
online interactions are informative in isolating precise
features of SNSs, although perhaps fall short on ecological
validity, particularly compared to research that examines
actual records of adolescents’ SNS use (Forest and Wood
2012; Tynes et al. 2008).
Objective Assessments of SNS Use
A small but growing number of SNS studies objectively
examine the specific content of SNS interactions, including
content from adolescents’ Facebook postings. A study that
utilized individuals’ ten most recent Facebook posts (as
reported by the participant, not obtained from the profile
page) involved systematic coding of the posts for positivity, negativity, and the amount of ‘‘likes’’ that the posts
received (Forest and Wood 2012). In general, public profiles on Facebook allow for the observation of certain
dimensions related to teen communication. Nonetheless,
questions can be raised about the accuracy of information
and the influence of self-presentational guidelines on
Facebook content. Moreover, teens are increasingly
encouraged to use privacy settings to restrict the information that is publicly available.
Summary of Findings: SNS Use, ‘‘Fitting in,’’
Acceptance and Affiliation
Table 1 presents the empirical research examining the
association between SNS use and adolescent peer relationships. We include 13 representative studies that
describe the nature of adolescents’ SNS use and answer
questions about ways in which SNS use is associated with
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Clin Child Fam Psychol Rev (2014) 17:1–18
Table 1 Social networking site use, fitting in, acceptance, and affiliation
Authors (year)
Sample
characteristics
Measures
Primary results
Ellison et al.
(2007)
N = 286
Undergraduate
students
Affiliation measures
Adapted measures of bonding, bridging, and
maintained social capital (self-report)
SNS measures
Facebook usage intensity, Facebook profile
elements, purpose of Facebook use (self-report)
Ellison et al.
(2011)
N = 450
Undergraduate
students
Affiliation measures
Measures of bridging social capital (6-item selfreport questionnaire) and bonding social capital
(5-item self-report questionnaire)
SNS measures
Facebook use, number of Facebook friends
(‘‘total’’ vs. ‘‘actual’’), Facebook connection
strategies measure (self-report)
Forest and
Wood (2012)
N = 80 (Study 1)
N = 177 (Study 2)
N = 98 (Study 3)
Undergraduate
Facebook users
Affiliation measures
Likeability of Facebook user
Positivity and negativity of status updates (coded)
Self-esteem (self-report)
SNS measures
10 most recent status updates (self-report)
Number of ‘‘likes’’ and comments on status
updates (self-report)
Grieve et al.
(2013)
N = 344 (Study 1)
N = 274 (Study 2)
Australian
university
Facebook users
Age M = 28.12
Affiliation measures
Social Connectedness Scale-revised
(20-item self-report questionnaire).
SNS measures
Facebook social connectedness
(20-item self-report questionnaire)
Kwon and Wen
(2010)
N = 229
Users of Korean
SNSs
Age: 66.2 % in
their 20s, 21.0 %
in their 30s
N = 487
Teens with
household
telephones and a
SNS profile
Age 12–17
Affiliation measures
Social identity (self-report)
SNS measures
Perceived ease of use, perceived encouragement,
and perceived usefulness of SNSs (self-report)
Intensity of Facebook use relates to greater
perceived bridging social capital after adjusting
for demographic factors, b = .34, p \ .0001, as
well as to greater bonding social capital,
b = .37, p \ .001.
Students with lower self-esteem, b = .34,
p \ .0001, and general life satisfaction, b = .31,
p \ .0001, perceived greater bridging social
capital with greater intensity of Facebook use.
Analogous results were found for bonding social
capital, b = .34, p \ .001 (life satisfaction),
b = .37, p \ .0001 (self-esteem).
Greater social information-seeking behaviors on
Facebook related to greater perceived bridging
social capital, b = .22, p \ .0001, and bonding
social capital, b = .18, p = .0006.
There were diminishing returns for those with
high numbers of actual friends on Facebook,
demonstrating a curvilinear relationship between
actual Facebook friends ([500) and types of
social capital.
Youth with low self-esteem viewed Facebook as a
safer place for self-expression than did youth
with high self-esteem, b = -.31, p = .005.
Youth with low self-esteem had higher coded
negativity, b = -.31, p = .001, and lower
positivity, b = .26, p = .004, in their Facebook
status updates in comparison with youth with
high self-esteem.
Youth with low self-esteem were rated as less
likeable by coders than those with high selfesteem, b = .22, t(71) = 2.01, p = .048.
A factor analysis revealed that Facebook
connectedness is distinct from social
connectedness.
Facebook facilitates social connections and relates
to lower depression, r = -.22, p \ .001 and
anxiety, r = -.17, p \ .001, and greater life
satisfaction, r = .26, p \ .001.
Social identity was positively related to perceived
SNS usefulness, b = 6.03, p \ .01, which in
turn related to greater SNS use, b = 3.95,
p \ .01.
Social identity related positively to perceived
encouragement via SNSs, b = 2.81, p \ .01.
Lenhart and
Madden
(2007)
McMillan and
Morrison
(2006)
123
N = 72
College students
Age 19–25
Affiliation measures
Interview questions related to social connectivity
online
SNS measures
Interview questions assessing use of SNSs,
including motivations for use
Affiliation measures
Autobiographical narratives coded for building
and forming social relationships online
SNS measures
Autobiographical narratives coded for use of
computer-mediated communication and feelings
toward computer-mediated communication
82 % of teens reported using SNSs to send private
messages to friends.
91 % of teens reported using SNSs to stay in
touch with friends they see frequently.
72 % of teens reported using SNSs to make plans
with friends.
The coded narratives revealed that participants
viewed computer-mediated communication as
something that helped them form bonds with
others. The narratives also underlined the view
of computer-mediated communication as
something that facilitates participation in
various activities, including special interest
groups.
Clin Child Fam Psychol Rev (2014) 17:1–18
7
Table 1 continued
Authors (year)
Sample
characteristics
Measures
Primary results
Pempek et al.
(2009)
N = 92
Undergraduate
students
Age M = 20.6
(1.07)
Participants reported using Facebook for 27.9 min
on average per day.
Coded diary entries revealed that 85 % of students
reported using Facebook to communicate with
friends.
Participants reported viewing others’ profiles and
pictures more often than posting information or
updating their own profiles.
Quinn and
Oldmeadow
(2013)
N = 443
Age 9–13
Primary and
secondary school
students north of
England
Reich et al.
(2012)
N = 251
High school
students in Los
Angeles
Age 13–19
M = 16.3 (1.2)
N = 97
Canadian university
students
Age M = 21.69
Affiliation measures
Diary and questionnaire (54 items) assessing
social activities (e.g., getting to know people
better) on Facebook
SNS measures
7-day diary measure assessing Facebook use
frequency and duration
Self-report measure assessing Facebook activities
(54 items)
Affiliation measures
Belonging measure (10-item self-report
questionnaire)
SNS measures
Intensity of SNS use (6-item self-report
questionnaire)
Affiliation measures
Lists of top 10 friends through SNS, IM, and faceto-face interactions
SNS measures
Experimenter-developed self-report survey
assessing use of and attitudes toward SNSs
Affiliation measures
NEO-PI-R to measure personality (including
extroversion and openness to experience) and
group affiliations on Facebook
SNS measures
The Facebook Questionnaire (basic use, attitudes
toward Facebook, and posting of identifying
information; 28-item self-report questionnaire)
CMC competence measure (motivation,
knowledge, and efficacy; 13-item self-report
questionnaire)
Affiliation measures
Perceptions of SNS use on relationships
List of top 10 offline friends
List of top 10 online friends
SNS measures
Typical Internet activities, motivation for Internet
use, and
SNS activities (self-report questions developed by
research team)
Ross et al.
(2009)
Subrahmanyam
et al. (2008)
N = 131
Undergraduate
students in Los
Angeles
Valkenburg
et al. (2006)
N = 881
Age 10–19
Dutch users of SNS
Affiliation measures
Social self-esteem (12-item self-report
questionnaire).
SNS measures
Use of SNSs (3-item self-report questionnaire)
Frequency and tone of reactions to profiles (4item self-report questionnaire)
relationship quality with friends. Eight studies are based on
undergraduate students (Ellison et al. 2007, 2011; Forest
and Wood 2012; Grieve et al. 2013; McMillan and
Intensity of SNS use was positively associated
with feelings of belonging for boys, b = .37,
p \ .001, but not girls.
Older boys who do not use SNSs (vs. SNS users)
report lower perceptions of belonging to their
group of friends, b = -.30, p = .004.
43 % of adolescents felt that SNS use made their
friendships closer.
17 % of adolescents listed SNS friends that had no
overlap with their face-to-face and IM friends.
Girls used IM and SNSs more than boys, X2(1,
N = 67) = 9.3, p = .002.
Extroversion related to greater numbers of group
affiliations on Facebook, t(42) = 2.44,
p = .019.
Individuals with high motivation to use computermediated communication spent more time on
Facebook, t(36) = 4.45, p \ .001, and checked
their Facebook wall more frequently,
t(36) = 3.77, p = .001.
20 % of participants reported that SNSs bring
them closer to their friends.
A small number of participants indicated that
SNSs cause them problems (2.5 %).
73 % of participants reported that SNSs did not
impact their relationships.
Youth utilize SNSs to keep in touch with friends
they do not see often (reported by 81 % of
youth)
49 % of students listed the same names for their
closest online friends and offline friends.
Adolescents’ social self-esteem related to the tone
of profile feedback they received, with positive
feedback relating to enhanced self-esteem, and
negative feedback relating to deflated selfesteem, b = .48, p \ .01
Adolescents who consistently received negative
feedback from their profile reported lower close
friendship self-esteem, r(881) = .40, p \ .001.
Morrison 2006; Pempek et al. 2009; Ross et al. 2009;
Subrahmanyam et al. 2008), two are based on adolescents
(Lenhart and Madden 2007; Reich et al. 2012), two also
123
8
include younger children (Quinn and Oldmeadow 2013;
Valkenburg et al. 2006), and one includes a broader range
of ages from adolescents to 30 year olds (Kwon and Wen
2010). Measurements of SNS use assess frequency, intensity, and duration of SNS use, in addition to more detailed
measures of specific SNS content shared.
SNSs and Friendship Quality
Although SNSs have provided notable structural changes to
adolescents’ social relations, adolescents and young adults’
stated motivations for using SNSs are quite similar to more
traditional forms of communication—to stay in touch with
friends, make plans, and get to know people better (Lenhart
and Madden 2007; Pempek et al. 2009). That said, evidence suggests that connectedness through SNSs may be
slightly different from general social connectedness. A
factor analysis indicated ‘‘Facebook connectedness’’ as a
distinct construct from general connectedness that was
uniquely related to general well-being and negatively
related to depression and anxiety (Grieve et al. 2013).
Nonetheless, the most common use of SNSs is to maintain
and extend existing offline friendships (McMillan and
Morrison 2006; Reich et al. 2012; Subrahmanyam et al.
2008). On average, in a college sample, 49 % of respondents’ top face-to-face friends were also their SNS friends
(Subrahmanyam et al. 2008). In addition, the content of
most SNS communication focuses on everyday events
related to school, mutual friends, and upcoming activities.
Only 29 % reported using SNSs to ‘‘look for new people.’’
The online–offline friend overlap findings were replicated
in a study of high school students, with only 17 % of
adolescents listing SNS friends that had no overlap with
their face-to-face and IM friends (Reich et al. 2012). Thus,
although young people might list hundreds of ‘‘friends’’ on
SNS sites, the majority of their SNS time involves extensions of their offline relationships.
Cross-sectional studies examining the relationship
between frequency of SNS use and friendship quality,
specifically, show that SNS use is associated with enhanced
relationship quality and intimacy (Ellison et al. 2007;
Grieve et al. 2013; McMillan and Morrison 2006; Reich
et al. 2012), suggesting some support for the concept that
SNSs enhance social connections, i.e., the stimulation
hypothesis. To investigate how SNS connectedness is
linked to relationship quality, McMillan and Morrison
(2006) coded emerging adults’ narratives about computermediated communication. Findings demonstrated that
participants viewed this type of communication as something that facilitates planning social activities, maintaining
ties with friends, and feeling part of a community. Interestingly, Korean SNS users with a high (versus low) sense
of social identity, in particular, found SNSs to be a useful
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Clin Child Fam Psychol Rev (2014) 17:1–18
and good resource for social support (Kwon and Wen
2010). In a study of younger participants, Reich and colleagues (2012) reported that 43 % of their high school
student participants believed that SNS use made them feel
closer to their friends. Similarly, 20 % of college students
indicated that SNSs brought them closer to their friends,
whereas only 2.5 % indicated that it had a negative impact;
however, the majority (73 %) indicated SNS use did not
have an effect on their relationships (Subrahmanyam et al.
2008).
Despite overall support for SNSs enhancing friendship
quality, interactive effects suggest that some individuals
may benefit more than others. For example, in support of
the ‘‘rich-get-richer’’ hypothesis, Canadian undergraduates
scoring high on extroversion who likely have more offline
friends reported more affiliations on Facebook than
undergraduates scoring low on extroversion (Ross et al.
2009). Examining closeness to friends more thoroughly,
Ellison et al. (2007) reported that those who use Facebook
intensely (i.e., actively engage in Facebook activities frequently and feel emotionally connected to the use of
Facebook) perceive greater bonding social capital, or
strong social ties and closeness to others who can provide
emotional support. Interestingly, in support of the social
compensation hypothesis, an interaction effect revealed
that those with low self-esteem and low life satisfaction
particularly benefitted from Facebook use in terms of more
emotional support. However, a later study indicated a
ceiling related to social capital benefits; after individuals
report having more than 500 ‘‘actual’’ (vs. online only)
Facebook friends, there are diminishing returns on social
capital gains. Focusing on early adolescents’ sense of
belongingness, a highly salient social construct for adolescents, Quinn and Oldmeadow (2013) demonstrated that
SNS use is related to a greater sense of belongingness for
boys, but not girls in a sample of young students north of
England. Older boys who did not use SNSs reported a
lower sense of belongingness than SNS users, which may
be indicative that this sample of boys also had poorer social
skills or other personality differences impacting their social
connections both online and offline.
Research incorporating objective SNS content to
examine the role of self-esteem in SNS use and social
connectedness supports the ‘‘rich-get-richer’’ hypothesis.
Facebook users with low, compared to high, self-esteem
posted status updates that were rated as lower in positivity
and higher in negativity by trained undergraduate coders
(Forest and Wood 2012). Based on the recently posted
status updates and the number of ‘‘likes’’ and comments
received by those posts, Facebook users with low selfesteem also were not as ‘‘likable’’ to the trained coders. In a
related vein, Dutch adolescents who frequently received
negative feedback from their SNS profiles also reported
Clin Child Fam Psychol Rev (2014) 17:1–18
lower social self-esteem (Valkenburg et al. 2006). In general, the direction of effects is unclear and may indeed be
reciprocal, with low self-esteem individuals posting more
negative messages and receiving less positive feedback,
which then fuels the low self-esteem. It is worth noting,
however, that even the Facebook users with low selfesteem reported that Facebook was a safe way for them to
self-disclose. Thus, although individuals with low selfesteem may view SNSs as a useful way to feel connected to
others, supporting the social compensation hypothesis
(McKenna et al. 2002), those with poor social skills may be
at risk for opening themselves up to harmful feedback from
others.
Summary
Generally, SNS use appears to benefit and not detract from
adolescents’ sense of peer affiliation, but adolescents’
offline level of social functioning is a consideration in the
overall impact. Those adolescents who have strong offline
social skills also appear to have more online connections
and contacts. Whether online communication actually
improves the overall quality of their relationships or simply
resembles their already strong relationships is difficult to
tease apart without longitudinal studies. On the other hand,
some adolescents who have more limited social success
offline appear to derive enhanced relationship satisfaction
online, particularly if they find online communications
more comfortable than offline social interaction. The
Internet may provide a leveling effect in relationship satisfaction for certain individuals, as described in the social
compensation hypothesis (McKenna et al. 2002). There
are, however, some caveats to the ameliorating influences
of online communication, that is, adolescents who post
more negative messages, which may include those with
low self-esteem or poor social skills, open themselves up to
negative feedback from others. Thus, there is evidence for
an overall positive association between SNS use and adolescents’ sense of social connectivity. While those who are
less socially inclined may report feeling more socially
connected through SNSs, as described in the social compensation hypothesis, those who are less socially inclined
may also be likely to receive less positive input from others
via SNSs. It remains unclear whether this translates into
fewer social benefits from SNSs, or whether SNSs actually
are detrimental to less socially skilled adolescents.
Review of Findings: SNS Use and Identity
Table 2 displays 14 studies that examine the link between
SNS use and constructs related to identity. Five of these
studies examined adolescent samples (Hillier and Harrison
2007; Tynes et al. 2008; Valkenburg and Peter 2007;
9
Valkenburg et al. 2011; Yu et al. 2011), four included
adolescents as well as older participants (Back et al. 2010;
Haferkamp and Kramer 2011; McLaughlin et al. 2012;
Silenzio et al. 2009), and five are based on college samples
(Chou and Edge 2012; Christofides et al. 2009; Grasmuck
et al. 2009; Walther 2007; Walther et al. 2011).
SNSs, Self-Disclosure, and Self-Presentation
Self-disclosure, which involves an iterative process of
sharing personally relevant information and receiving
feedback, is central to identity formation. SNSs bring both
sides of this information-sharing into a highly public arena.
Based on Canadian participants’ self-reports, Facebook
disclosures are ‘‘likely’’ or ‘‘very likely’’ to include information about salient recent or upcoming happenings—by
sharing pictures with friends, information about relationship status, and mention of their birthday (Christofides,
Muise, and Desmarais 2009). Some adolescents report that
online interactions are more conducive to self-disclosure
than face-to-face interactions (Valkenburg and Peter 2007).
Valkenburg, Sumter and Peter (2011) indicated that online
self-disclosure may be a ‘‘rehearsal’’ for other types of selfdisclosure, that is, online self-disclosure to known friends
in early adolescent years was associated with greater
offline self-disclosure at the next wave of data collection,
one-half year later; yet, offline self-disclosure did not lead
to greater online self-disclosure.
With adolescents controlling what information and
photographs they wish to share to a broad audience through
SNS profiles, there is considerable speculation that some
adolescents may post misinformation or at least idealized
versions of themselves. To examine this possibility,
researchers asked individuals to report on themselves as
they are and as they ideally would like to be. Additionally,
several close friends also completed personality measures
about the participant, and objective research assistants
coded individuals’ actual SNS profiles. Results demonstrated that adolescents did not portray their ‘‘ideal selves’’
through their SNS profiles, and that certain personality
characteristics, such as extroversion and openness, came
across accurately through SNS profiles (Back et al. 2010).
There is also evidence, however, that while individuals
may not express idealized versions of themselves via SNSs,
they may alter or highlight different aspects of themselves.
Walther (2007) created several conditions in which
undergraduate students were told that an online message
would be received by different individuals who varied by
age, status, and relevance to their own life (e.g., professor
from their university, high school student from another
state, or college student from another university). Time
spent on the message, as well as number of edits and level
of message complexity, were all objectively measured
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Clin Child Fam Psychol Rev (2014) 17:1–18
Table 2 SNS use and identity
Authors
(year)
Back et al.
(2010)
Sample
characteristics
Measures
Primary results
N = 236
Identity measures
SNS users from the
United States and
Germany
Ten-item Personality Inventory
Observers accurately rated participants’ personalities
based on viewing their SNS profile, particularly for
extroversion, r = .39, p \ .001, a...
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