ToStudyorToSleepResearch
1. Using the same article identify and explain the components of measurement and data collection, including but not limited to directness of measurement, level of measurement, measurement error, reliability, validity, type of measurement and/or scale used, recruitment, consistency in data collection, and control.
2. Provide an example of a study you would perform and describe your measurement and data collection plan using the terms above.
To Study or to Sleep? The Academic Costs of Extra Studying at the
Expense of
Sleep
Cari Gillen-O’Neel
University of California, Los Angeles
Virginia W. Huynh
California State University, Northridge
Andrew J. Fuligni
University of California, Los Angeles
This longitudinal study examined how nightly variations in adolescents’ study and sleep time are associated
with academic problems on the following day. Participants (N = 535, 9th grade Mage = 14.88) completed daily
diaries every day for 14 days in 9th, 10th, and 12th grades. Results suggest that regardless of how much a stu-
dent generally studies each day, if that student sacrifices sleep time to study more than usual, he or she will
have more trouble understanding material taught in class and be more likely to struggle on an assignment or
test the following day. Because students are increasingly likely to sacrifice sleep time for studying in the latter
years of high school, this negative dynamic becomes increasingly prevalent over time.
Students generally learn best when they keep a con-
sistent study schedule and distribute their study
time evenly across a number of days (e.g., Bahrick
& Phelps, 1987; Dempster & Farris, 1990). Although
this paced learning is ideal, the increasing demands
that high school students face may make such a
consistent schedule infeasible. Socializing with
peers and working for pay, for example, both
increase across the course of high school (Shanahan
& Flaherty, 2001; Wight, Price, Bianchi, & Hunt,
2009). As they advance through high school, adoles-
cents’ academic obligations also intensify and often
require more time and effort (Eccles et al., 1993). As
a result, many high school students end up with
irregular study schedules, often facing nights in
which they need to spend substantially more time
than usual studying or completing school work.
Although these nights of extra studying may
seem necessary, they can come at a cost. When allo-
cating their limited number of hours across a num-
ber of activities, adolescents often make trade-offs,
sacrificing certain activities to make time for others.
Among adolescents, sleep is one activity that is
commonly sacrificed for additional study time
(Adam, Snell, & Pendry, 2007). Indeed, study time
is one of the most significant determinants of high
school students’ sleep time, more so than time spent
with friends or family or time spent using media
(e.g., computers or television; Fuligni & Hardway,
2006). Even adolescents themselves report that too
much homework is a common barrier that prevents
them from getting enough sleep (Noland, Price,
Dake, & Telljohann, 2009). Studying is obviously a
contributor to academic achievement, yet adequate
sleep is also important for academics (Curcio, Ferr-
ara, & De Gennaro, 2006). Thus, it is possible that
nights of extra studying are not as effective as stu-
dents think. In fact, these nights of extra studying
may even be counterproductive because they are
likely to also be nights of less sleep and sleep sche-
dule irregularity, both of which can interfere with
school performance (Wolfson & Carskadon, 1998).
In high school, sacrificing sleep to study may be
especially problematic because, in general, high
school age adolescents are chronically sleep
deprived (Carskadon, 1990). Although the optimal
amount of sleep varies somewhat across individu-
als, most adolescents need just over 9 hr of sleep
each night (Wolfson & Carskadon, 1998). Only
Support for this study was provided by a grant from the Rus-
sell Sage Foundation. We are extremely grateful to the principals
and teachers who welcomed us into their schools and to the stu-
dents who shared their daily lives with us.
Correspondence concerning this article should be addressed to
Cari Gillen-O’Neel, Department of Psychology, University of
California, 1285 Franz Hall, Los Angeles, CA 90095-1563. Elec-
tronic mail may be sent to c.go@ucla.edu.
[Correction added on 9/7/2012, after first online publication
8/20/2012: Virginia W. Huynh’s affiliation has been corrected to
California State University, Northridge.]
Child Development, January/February 2013, Volume 84, Number 1, Pages 133–14
2
� 2012 The Authors
Child Development � 2012 Society for Research in Child Development, Inc.
All rights reserved. 0009-3920/2013/8401-0011
DOI: 10.1111/j.1467-8624.2012.01834.x
about 9% of high school students, however, sleep
for at least the requisite 9 hr per night (National
Sleep Foundation, 2006). One fourth of high school
students get a borderline amount of sleep (between
8 and 9 hr per night), and the vast majority of high
school students (62%) get insufficient sleep (< 8 hr
per night; National Sleep Foundation, 2006).
Across the course of high school, the biologically
needed hours of sleep remain constant, yet the
average amount that students sleep declines (Cars-
kadon, Acebo, & Jenni, 2004). In 9th grade, the
average adolescent sleeps for 7.6 hr per night, and
this time decreases to 7.3 hr in 10th grade, 7.0 hr in
11th grade, and 6.9 hr in 12th grade (National Sleep
Foundation, 2006). Thus, adolescents start high
school sleeping for fewer hours than they need,
and this sleep deprivation worsens over the course
of high school (Fukuda & Ishihara, 2001).
Presumably, when students choose to trade sleep
for studying, they do so because they believe that
the increased studying will help their grades. On
the one hand, this strategy may be effective
because, overall, study time is associated with aca-
demic achievement. On average, students who
spend more time studying tend to do better on
achievement tests (Fuligni & Stevenson, 1995). Stu-
dents who have high grade point averages (GPAs)
study for an average of about three fourths of an
hour longer on weeknights than their peers who
have low GPAs (Witkow, 2009). On the other hand,
sacrificing sleep, even for additional studying, may
be an ineffective strategy because average time
spent sleeping also contributes to higher achieve-
ment. Students whose schedules include more
hours of sleep per night tend to have higher grades
than their peers who sleep for fewer hours (Wolf-
son & Carskadon, 2003). When students are sleep
deprived, they experience greater fatigue at school
the following day, and greater fatigue can make
learning more difficult (Giannotti, Cortesi, Sebas-
tiani, & Ottaviano, 2002). Furthermore, sleep is a
key restorative process during which consolidation
of learning takes place (Diekelmann & Born, 2010).
Thus, additional study time, when it comes at the
expense of sleep time, may not benefit achievement
as much as students think.
Current Study
Most research examining studying, sleep, and
achievement have focused on average study times,
average sleep times, and broad indicators of aca-
demic achievement such as GPAs and test scores
(e.g., Taras & Potts-Datema, 2005; Wolfson & Cars-
kadon, 2003). Yet, average study and sleep times
are the aggregated result of the daily demands on
adolescents’ time and the daily time-allocation
choices that they make. Likewise, GPAs and test
scores are the result of daily behaviors such as pay-
ing attention in class and doing well on assign-
ments and examinations. Despite the fact that
studying, sleep, and school are all daily occur-
rences, most previous examinations of these issues
have only examined associations at the aggregate
level, leaving the daily dynamics of studying, sleep,
and academics unexplored.
In this longitudinal study, we examined how
daily choices regarding study time and sleep time
are associated with adolescents’ academic behaviors
at school on the following day, and how these daily
dynamics changed over the course of high school.
For 14 days in each the 9th, 10th, and 12th grades,
we obtained daily reports of study time, sleep time,
and academic functioning from an ethnically diverse
sample of students. Because our data were collected
among the same students over the course of many
days, we could conduct within-person analyses that
essentially allow participants to serve as their own
control group and, therefore, control for unmea-
sured factors that can confound traditional,
between-person analyses. In other words, we could
move beyond between-person questions (e.g., do the
students with the longest average study times tend
to be the students with more or fewer academic
problems?) and instead answer within-person,
daily-level questions (e.g., when a student studies
longer than usual on a particular day, what happens
to this student’s academic problems on the following
day?). Specifically, we sought to answer the follow-
ing questions: (a) Do adolescents have better or
worse academic performance on days after they
spent more time than usual studying and doing
homework? (b) Does extra time spent studying cut
into adolescents’ sleep on a daily basis? and (c) Does
reduced sleep either diminish any positive effects of
extra studying or explain any possible negative
effects of extra studying on academic performance?
Furthermore, by following the same participants
over multiple years of high school, we were also able
to examine how the dynamics of study time, sleep
time, and academic functioning change over time.
Method
Participants and Procedure
Beginning in 9th and continuing in 10th and
12th grades, we recruited students from three Los
134 Gillen-O’Neel, Huynh, and Fuligni
Angeles public high schools. The first school pri-
marily served students from Latin American and
Asian backgrounds whose families had lower-mid-
dle- to middle-class educational and occupational
statuses, the second school primarily served lower-
middle- to middle-class students from Latin Ameri-
can and European backgrounds, and the third
school primarily served middle- to upper-middle-
class students from Asian and European back-
grounds (California Department of Education,
2006). In the first two schools, we invited all stu-
dents in the target grade to participate in each year
of the study. Due to the large size of the third
school, we invited approximately half the 9th grad-
ers to participate the first year of the study, and we
only followed these students in 10th and 12th
grades. At all three schools, students who had par-
ticipated in earlier years but were no longer
enrolled in the school were contacted in subsequent
years and invited to participate by mail.
Participants were recruited during school hours
in the spring semesters of each school year. Stu-
dents who returned parental consent forms and
provided their own assent completed an initial
questionnaire, also during school hours. This ques-
tionnaire assessed students’ demographic informa-
tion such as their gender, ethnicity, and the
countries of birth for themselves and both parents.
After completing the questionnaire, students
received a 14-day supply of diary checklists and
were instructed to fully complete one checklist each
night before going to bed over the subsequent 2-
week period. Each checklist was three pages long
and took about 5–10 min to complete. To monitor
on-time completion of the checklists, we provided
participants with 14 manila envelopes and an elec-
tronic time stamper (a hand-held device that
imprints the current date and time and is pro-
grammed with a security code to prevent alterations
to the correct date). Participants were instructed to
seal their completed checklists into an envelope
each night and to stamp the seal with the time stam-
per. At the end of the 2-week period, participants
returned the completed materials to their school
and received $30 compensation. If inspection of the
data indicated that they had completed the check-
lists correctly and on time, participants also received
two movie passes. The time-stamper monitoring
and the cash and movie pass incentives resulted in a
high rate of compliance: Depending on the year of
data collection, participants in our sample com-
pleted 97%–99% of the possible 14 diaries (9th
grade: M = 13.79, SD = 1.25; 10th grade: M = 13.81,
SD = 1.13; 12th grade M = 13.65, SD = 1.52).
For this study, we examined responses from the
535 (52.1% female) Latino (n = 193), Asian American
(n = 233), and European American students (n = 109)
who reported on at least 7 of 14 days in at least 2 of the
3 years of data collection. Most of the Latino partici-
pants (82.4%) were from Mexican backgrounds, and
most of the Asian American participants (67.8%) were
from Chinese backgrounds. During the first wave of
data collection, participants ranged in age from 13.94
to 16.22 (M = 14.88, SD = 0.39).
As a measure of socioeconomic status (SES), we
combined students’ reports of their parents’ educa-
tional attainment and occupations. Students reported
how far their mothers and fathers went in school by
selecting one of the following categories: elementary
or junior high school, some high school, graduated from
high school, some college, graduated from college, or law,
medical, or graduate school. Students’ open-ended
reports of their mothers’ and fathers’ jobs were coded
into the following five categories: unskilled (e.g., food
service worker, parking attendant), semiskilled (e.g.,
construction worker, bus driver), skilled (e.g., nursing
assistant, mechanic), semiprofessional (e.g., accoun-
tant, social worker), or professional (e.g., attorney,
dentist). The measures of parental education and
occupation were each standardized and averaged to
provide an overall index of SES. On average, stu-
dents from European backgrounds reported higher
SES (M = 0.633, SD = 0.540) than students from
Asian backgrounds (M = )0.002, SD = 0.788), who in
turn, reported higher SES than students from Latin
American backgrounds (M = )0.586, SD = 0.700),
F(2, 529) = 104.79, p < .001, g2 = .28.
Measures
Daily study and sleep time. Each evening for
14 days, participants reported how much time (in
hours and minutes) they spent studying outside of
school and how much time they slept the previous
night. For study time, participants first checked
(yes or no) whether or not they studied or did home-
work while not in school and then gave a free
response to the question: ‘‘(If yes) for how long?’’
For sleep time, participants answered the question:
‘‘How many hours and minutes did you sleep last
night (for example, 7½ hr)?’’ Each year, the 14
response days spanned 10 school nights (i.e., Sun-
day through Thursday) and 4 weekend nights.
Given that our outcome of interest occurred on
school days, we only used school-night reports of
study and sleep time.
In comparison to surveys that often ask partici-
pants to retrospectively report behaviors from the
To Study or to Sleep? 135
previous week, month, or year, daily reports depend
less on memory and therefore are less susceptible to
errors of estimation. For sleep time in particular,
daily reports such as ours are highly correlated with
more objective measures of sleep duration, such as
those derived from wrist actigraphs (i.e., watch-like
devices that measure sleep by analyzing body move-
ments) and polysomnographic recordings (Lockley,
Skene, & Arendt, 1999; Wolfson et al., 2003).
Furthermore, daily reports of sleep time are mean-
ingfully associated with other daily experiences and
mood (e.g., fatigue; Fuligni & Hardway, 2006). For
study time, the validity of our participants’ reports
was indicated by the association between study time
(averaged across all days and years) and students’
GPAs across years (r = .50, p < .001).
Daily academic problems. On each school day,
participants indicated (yes or no) whether or not
they had various experiences at school. As a mea-
sure of daily academic problems, we summed par-
ticipants’ responses to two items: ‘‘did not
understand something taught in class’’ and ‘‘did
poorly on a test, quiz, or homework’’ (range = 0–2).
As a control variable, we also used participants’
daily responses to whether or not they ‘‘had a test
or a quiz at school.’’ The validity of our measure of
academic problems was indicated by the negative
association between students’ reports of academic
problems (averaged across all days and years) and
students’ GPAs across years (r = ).12, p = .005).
Analytical Plan
First, we averaged daily reports of study times,
hours of sleep, and academic problems from each
grade and used a series of repeated measures anal-
yses of variance to examine how students’ average
daily study times, average hours of school-night
sleep, and average number of academic problems
changed across the course of high school. Then, we
estimated a series of three-level hierarchical linear
models (HLMs; Bryk & Raudenbush, 1992) to
examine daily-level associations between study
time, sleep time, and academic problems and
whether these associations changed over the course
of high school.
Results
Study Time, Sleep Time, and Academic Problems Across
the Course of High School
As shown in Table 1, study time did not change
across the years of high school; in 9th, 10th, and
12th grades, students spent an average of just over
an hour studying each school night. Sleep time,
however, decreased over the course of high school.
By the 12th grade, students slept for an average of
41.4 fewer minutes each school night than they did
in 9th grade. Finally, the frequency of academic
problems also decreased. In the 9th and 10th
grades, students reported an average of one aca-
demic problem every 3 days; by the 12th grade, the
frequency of academic problems decreased to one
problem every 5 days.
Academic Problems After Days With Extra
Study Time
To examine whether adolescents had more or
fewer academic problems on days after they spent
more time studying than usual, we estimated an
HLM using the following equations:
academic problemstþ1ij
¼ p0ij þ p1ijðprior-day study timetijÞ
þ p2ijðhad a testtþ1ijÞþ etij
ð1Þ
p0ij ¼ b00j þ b01jðyearijÞþ r0ij ð2Þ
p1ij ¼ b10j þ b11jðyearijÞ ð3Þ
p2ij ¼ b20j þ b21jðyearijÞ ð4Þ
Table 1
Average Daily Study Time, Sleep Time, and Academic Problems Across the Course of High School
9th Grade 10th Grade
12th Grade
Statistical testM (SD) M (SD) M (SD)
Study time 1.12 (0.85) 1.10 (0.90) 1.06 (1.00) F(2, 732) = 0.32, p = .72, g2 = .001
Sleep time 7.63 (0.93) 7.40 (0.92) 6.94 (0.95) F(2, 730) = 112.45, p < .001, g2 = .236
Academic problems 0.33 (0.30) 0.32 (0.28) 0.20 (0.21) F(2, 732) = 27.88, p < .001, g2 = .071
Note. Both study time and sleep time are reported in hours. Academic problems could range from 0 to 2 each day.
136 Gillen-O’Neel, Huynh, and Fuligni
Study time was centered within each adolescent
within each year. The Level 1 (daily-level) equation
is shown as Equation 1: Adolescents’ academic
problems on a given day are modeled as a function
of their average daily academic problems (p0ij), the
extent to which the time they spent studying on the
prior night deviated from their personal norm (p1ij),
and whether or not they had a test at school that
day (p2ij). Equations 2–4 represent Level 2 (yearly
level) effects, allowing us to examine the extent to
which daily academic problems and the association
between prior-day study time and academic prob-
lems change over the years of high school. Years
were coded as 0 = 9th grade, 1 = 10th grade, and
3 = 12th grade. This analysis also accounted for the
nesting of observations within participants (Level
3), but these equations are not shown here because
the model did not include any Level 3 predictors.
As shown in the first column and third row of
Table 2, 9th-grade students’ deviation from their
average study time had no association with their
academic problems on the following day. Across
the course of high school, however, the association
between study time and academic problems chan-
ged such that study time became increasingly asso-
ciated with academic problems (see the first
column and fourth row of Table 2). As shown in
Figure 1, reestimating our HLM—first with year
centered at 10th grade and then with year centered
at 12th grade—indicated that in 10th grade, days
on which students reported longer than normal
study times tended to be followed by days with
more academic problems, and this daily association
was even stronger in 12th grade.
It is important to note that, in Equation 1, we
controlled for students’ daily reports of whether or
not they had a test or quiz at school. Including this
control rules out the possibility that nights of
increased studying were followed by days with
more academic problems only because students
were both more likely to study and to have aca-
demic problems when they have tests. Thus, our
results suggest that regardless of whether or not
students had a test, study time became increasingly
associated with academic problems such that, by
10th grade, nights with longer than average study
times tended to be followed by days with more aca-
demic problems.
The Level 3 (individual-level) variance compo-
nents for all effects of interest were nonsignificant.
In other words, neither the 9th-grade association,
nor the yearly change in the association between
study time and academic problems differed across
individuals. As such, we did not examine individ-
ual-level differences (e.g., gender, ethnic, or SES
differences) in these associations.
Table 2
Association Between Daily Study Time and Next Day’s Academic
Problems, Before and After Controlling for Daily Sleep Time
Academic problems
Model 1 Model 2
b (SE)
b (SE)
Intercept (average academic
problems
in 9th grade)
0.276 (0.013)*** 0.259 (0.013)***
Year (yearly change in
average academic problems)
)0.040 (0.005)*** )0.010 (0.006)
Study time (association
in 9th grade)
0.006 (0.008) 0.012 (0.009)
Year (yearly change
in association)
0.010 (0.004)* 0.001 (0.004)
Had a test (association
in 9th grade)
0.090 (0.018)*** 0.107 (0.018)***
Year (yearly change
in association)
0.035 (0.010)** 0.009 (0.010)
Sleep time (association
in 9th grade)
— 0.003 (0.007)
Year (yearly change
in association)
— )0.010 (0.003)**
Note. Both study time and sleep time were centered at each
individual’s mean at each year. Year was coded such that 9th
grade = 0, 10th grade = 1, and 12th grade = 3.
*p < .05. **p < .01. ***p < .001.
0.35
0.
3
0.25
b = 0.006
b = 0.016***
b = 0.036***0.2
0.15
A
ca
de
m
ic
P
ro
bl
em
s
0.1
0.05
0
0 1
Study Time
2
9th Grade
10th Grade
12th Grade
3
b = 0.006
b = 0.016***
b = 0.036***
9th Grade
10th Grade
12th Grade
Figure 1. Associations between daily study time and next day’s
academic problems (controlling for whether student had a test
the next day) in 9th, 10th, and 12th grades. Study time is
reported in hours.
***p < .001.
To Study or to Sleep? 137
Trade-Off Between Extra Study Time and Sleep
To examine whether extra time spent studying
cut into adolescents’ sleep on a daily basis, another
HLM was estimated. At Level 1, students’ daily
sleep time was predicted by their daily study time
and whether or not they had a test at school the fol-
lowing day):
sleep timetij ¼ p0ij þ p1ijðstudy timetijÞ
þ p2ijðhad a testtþ1ijÞþ etij
ð5Þ
Study time was centered within each adolescent
within each year. Potential changes in the daily-level
association across the years of high school were
tested using the same equations as those described
in Equations 2–4 above. This analysis also accounted
for the nesting of observations within participants
(Level 3), but these equations are not shown here
because there were no Level 3 predictors.
Results indicated that daily study and sleep time
were inversely associated in ninth grade (see row 3
of Table 3); days on which students reported longer
than normal study hours tended to be days on
which they reported fewer hours of sleep. This
association became increasingly negative across the
high school years (see row 4 of Table 3 and Fig-
ure 2). As before, these analyses controlled for stu-
dents’ daily reports of whether or not they had a
test or quiz at school, indicating that students’
trade-off between studying and sleeping occurs
regardless of whether or not they have a test at
school on the following day. Also, as before, indi-
vidual-level differences in these associations were
not examined because there was no significant vari-
ability in these associations across individuals.
Extra Study Time and Academic Problems: Mediated by
the Trade-Off With Sleep?
Finally, we examined whether the yearly increas-
ing trade-off between study time and sleep could
account for the surprisingly greater number of aca-
demic problems that followed days of extra study-
ing in the latter years of high school. In other words,
we examined whether the yearly changing associa-
tion between study time and sleep could account for
the yearly changing association between study time
and academic problems. To this end, we conducted
multilevel mediation analyses using the procedure
suggested by (Krull & MacKinnon, 2001). We esti-
mated a series of HLMs in which daily academic
problems were predicted by both the previous day’s
study time and the previous day’s sleep time:
academic problemstþ1ij
¼ p0ij þ p1ijðprior-day study timetijÞ
þ p2ijðprior-day sleep timetijÞ
þ p3ijðhad a testtþ1ijÞþ etij
ð6Þ
p0ij ¼ b00j þ b01jðyearijÞþ r0ij ð7Þ
p1ij ¼ b10j þ b11jðyearijÞ ð8Þ
p2ij ¼ b20j þ b21jðyearijÞ ð9Þ
p3ij ¼ b30j þ b31jðyearijÞ ð10Þ
Table 3
Association Between Daily Study Time and Sleep Time
Sleep
b (SE)
Intercept (average sleep time in 9th grade) 7.643 (0.046)***
Year (yearly change in average sleep time) )0.215 (0.022)***
Study time (association in 9th grade) )0.053 (0.018)**
Year (yearly change in association) )0.028 (0.011)*
Had a test (association in 9th grade) )0.033 (0.039)
Year (yearly change in association) )0.077 (0.024)**
Note. Study time was centered at each individual’s mean at each
year. Year was coded such that 9th grade = 0, 10th grade = 1,
and 12th grade = 3.
*p < .05. **p < .01. ***p < .001.
8
7.5
7
6.5
6
0 1 2
Study Time
Sl
ee
p
Ti
m
e
3
b = -0.053**
b = -0.081***
b = -0.136***
9th Grade
10th Grade
12th Grade
Figure 2. Daily associations between study time and sleep time
(controlling for whether student had a test) in 9th, 10th, and 12th
grades. Study time and sleep time are reported in hours.
*p < .01. ***p < .001.
138 Gillen-O’Neel, Huynh, and Fuligni
Study time and sleep time were centered within
each adolescent within each year. We then compared
the moderating effect of year on the association
between study time and academic problems before
and after controlling for sleep time (i.e., the magni-
tude and significance of b11j from Equation 3 vs. the
magnitude and significance of b11j from Equation 8).
As before, these models included students’ daily
reports of whether or not they had a test or quiz at
school as a control variable.
As shown in the first column and fourth row of
Table 2, without sleep time in the model, study time
seems to become increasingly associated with aca-
demic problems across the years of high school.
When sleep time is added to the model, however,
the yearly exacerbation of the association between
study time and academic problems is reduced by
90% and is no longer significant. These results sug-
gest that study time became increasingly associated
with more academic problems across high school
because longer study hours were increasingly associ-
ated with fewer hours of sleep, which in turn pre-
dicted greater academic problems the following day.
Discussion
Our results suggest that, across the years of high
school, the trade-off between daily study time and
sleep becomes increasingly associated with aca-
demic problems. In the latter years of high school,
days of extra studying tend to be followed by days
with more academic problems. In 9th grade, days of
extra studying have no association with the follow-
ing day’s understanding of class material or test
performance; in 10th grade, however, adolescents
report more such academic problems on days after
they spend more time studying than usual, and this
troublesome association becomes even stronger in
12th grade. The association between study time and
academic problems occurs regardless of whether or
not students have a test coming up and, therefore,
is not simply an artifact of studying for and taking
a difficult test.
Although we expected that nights of extra study-
ing might not be as effective as students suppose
(Pilcher & Walters, 1997), it was somewhat surpris-
ing that nights of extra studying would be associ-
ated with worse academic functioning the following
day. This surprising finding, however, made more
sense once we examined extra studying in the con-
text of adolescents’ sleep. As other studies have
found, our results indicate that extra time spent
studying cuts into adolescents’ sleep on a daily
basis (Adam et al., 2007). This trade-off between
studying and sleeping occurs in 9th grade and
becomes more dramatic in the latter years of high
school. Our mediation results suggest that the
reduced sleep that tends to occur on nights of extra
studying is what accounts for the increase in aca-
demic problems that occurs the next day.
It is important to underscore that our results do
not suggest that it is problematic for adolescents to
spend more time studying overall. Previous studies
suggest that when examining achievement differ-
ences between students, those who study more tend
to earn higher grades (Keith, 1982; Witkow, 2009),
and this same pattern was evident in our study. Our
analyses, however, go beyond averages and do not
compare the study times and achievement levels of
different students. Instead, our analyses focus on
daily and yearly variations within each student.
Regardless of how much a student generally studies
each day, our results suggest that if that student sac-
rifices sleep to study more than usual, he or she will
have more trouble understanding material taught in
class and be more likely to struggle on an assign-
ment or test the following day.
Our findings complement sleep research that has
demonstrated that students who, on average, sleep
for more hours tend to have more positive aca-
demic outcomes such as higher grades and better
school behaviors (Curcio et al., 2006; Wolfson &
Carskadon, 2003). Our study provides additional
evidence that beyond average amounts of sleep,
nightly variations in sleep are associated with
school functioning on a daily basis. Specifically,
students who sleep less than usual on a particular
night are more likely to experience academic prob-
lems on the following day, especially in the latter
years of high school. It is possible that this daily
effect is exacerbated by the fact that students are
generally sleep deprived. That is, perhaps if stu-
dents generally received adequate amounts of
sleep, they would be less sensitive to daily varia-
tions. Similar to other studies (e.g., National Sleep
Foundation, 2006; Wolfson & Carskadon, 1998), we
found that even in 9th grade, students tend to sleep
considerably less than the needed 9 hr per night,
and students’ sleep deprivations tends to worsen
over the course of high school. Even if adolescents
did receive adequate amounts of sleep, however,
we would not expect the effect of irregular sleep on
school functioning to disappear completely, as
other studies have demonstrated that even beyond
total amounts of sleep, irregular sleep schedules are
associated with lower academic performance (Wolf-
son & Carskadon, 1998).
To Study or to Sleep? 139
Given that our research suggests that it is partic-
ularly counterproductive to sacrifice sleep in ser-
vice of studying, academic success may depend on
finding strategies to avoid having to make such a
trade-off. One such strategy might be to maintain a
consistent study schedule across days. Rather than
letting due dates dictate the amount one dedicates
to homework and studying each day, students
could distribute their total study and homework
time evenly across all days of the week. In and of
itself, this is a generally effective study strategy—
experimental research has demonstrated that spac-
ing study time evenly across a number of days
results in better academic performance than study-
ing in one massed session, even if the total amount
of study time is the same (Kornell, 2009).
Despite efforts to maintain a consistent study
schedule, high school students may still occasion-
ally face days on which they need to spend sub-
stantially more time than usual on their school
work. On these nights, our research suggests that
students should make every effort not to let this
extra study time disrupt their normal sleep pat-
terns. One possibility is for students to use their
school time as efficiently as possible. Many high
school students have at least one period during
the school day that is relatively unstructured (e.g.,
homeroom or study hall). If students can use this
time to complete their additional work, they will be
less likely to need to sacrifice sleep. Another possi-
bility is for students to sacrifice time spent on other
activities. On average, adolescents spend about an
hour each day socializing with friends, about 1 hr
each day helping the family, and between 1 and
2 hr each day watching television (Fuligni & Hard-
way, 2006; Wight et al., 2009). Presumably, adoles-
cents are sacrificing sleep for study time to
maintain the time they spend on these and other
demands (e.g., paid work and extracurricular activ-
ities). Further research into the daily lives of adoles-
cents could examine precisely how adolescents’
various demands interact with study and sleep
time and, therefore, offer specific activity- and
time-management suggestions to adolescents and
their families.
Overall, our daily approach is a particular
strength of this study. First of all, collecting reports
from the same students over the course of many
days allowed us to conduct within-person analyses,
which control for individual differences that can
confound traditional between-person analyses. Sec-
ond, by examining multiple facets of adolescents’
lives simultaneously, our study provides a perspec-
tive that is closer to the actual decisions adolescents
make on a daily basis, a perspective that may be
especially important for work that hopes to inform
interventions aimed at improving students’
achievement. Our research suggests, for example,
that intervening to increase students’ study time
will be counterproductive if the additional study
time ends up interfering with sleep. Similarly, inter-
ventions aimed at helping students get more sleep
should account for the daily demands that students
face and the fact that studying seems to be one rea-
son why students are not getting as much sleep as
they need.
Importantly, we did not find evidence of individ-
ual differences in any of our findings. This suggests
that the daily dynamics of studying, sleep, and aca-
demic functioning are similar across individuals
regardless of demographic characteristics such as
gender or ethnic background.
Limitations and Future Directions
As already discussed, a main limitation of our
study is that we measured only two of the many
activities that demand adolescents’ time. Although
we know that studying can come at the expense of
sleeping, we do not know how other activities
interact with studying and sleeping, or how these
activities might be associated with academic func-
tioning. Further research could use similar daily
and yearly methodology to examine a wider array
of activities that occupy adolescents’ time over the
course of high school. Such research will provide a
more complete picture of adolescents’ days and
could identify additional daily choices that either
support or hinder adolescents’ achievement.
Similarly, although our daily measures of study-
ing and sleeping are a strength of this study, our
study is limited in that we only assessed the time
adolescents spent doing these activities. We did
not, for example, assess the quality of students’
studying or sleeping. Without knowing exactly
how students were spending their study time, it is
unclear how study strategy might interact with
study time and academic problems. It could be, for
example, that adolescents study more on days
when they are using a particularly ineffective study
strategy. If this were the case, helping students
develop more efficient study habits could reduce
the number of days when they need extra study
time. Sleep quality may also be an important vari-
able to consider. It is possible that studying more
than usual is associated with poorer quality of sleep
(in addition to reduced sleep time). Studying more
than usual may, for example, be associated with
140 Gillen-O’Neel, Huynh, and Fuligni
feelings of anxiety, and anxiety is certainly associ-
ated with a reduced sleep quality (Carskadon,
2002). If this were the case, poor sleep quality may
independently contribute to academic problems,
and helping students sleep soundly for the hours
that they do sleep may reduce the number of aca-
demic problems they experience the following day.
Finally, our study is limited in that we only uti-
lized adolescent self-reports. Although daily diary
reports generally avoid retrospective errors in
recall, they are still subjective reports and, there-
fore, are subject to reporting biases. For sleep in
particular, there are more objective ways of mea-
suring behavior. Conducting similar daily research
using a device such as an actigraph to monitor par-
ticipants’ motor movements throughout the day
could corroborate the validity of daily self-reports
of sleep and provide a wider variety of sleep mea-
sures than was included in this study (e.g., sleep
duration, timing, and quality).
Conclusion
Sacrificing sleep for more studying time is a com-
mon, yet counterproductive strategy for adolescents,
especially in the latter years of high school. Adoles-
cents devote less time to sleep as they age, and when
they sacrifice the precious little sleep they have for
extra studying, it has negative consequences for
their daily academic performance. Our results sug-
gest that the best studying strategy for adolescents
who must juggle the demands of high school is to
study consistently on school days. However, as ado-
lescents progress through high school, their time
becomes an increasingly precious commodity. On
the basis of the mediation results, we speculate that
if adolescents do need to study more than normal,
they should not sacrifice sleep, but rather some
other time-consuming activity. Parents and educa-
tors concerned about adolescents’ academic prob-
lems should emphasize the importance of sleep and
maintaining a regular studying schedule.
References
Adam, E. K., Snell, E. K., & Pendry, P. (2007). Sleep tim-
ing and quantity in ecological and family context: A
nationally representative time-diary study. Journal of
Family Psychology, 21, 4–19. doi:10.1037/0893-3200.
21.1.4
Bahrick, H. P., & Phelps, E. (1987). Retention of Spanish
vocabulary over 8 years. Journal of Experimental Psychol-
ogy: Learning, Memory, and Cognition, 13, 344–349.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical
linear models: Applications and data analysis methods.
Thousand Oaks, CA: Sage.
California Department of Education. (2006). School sum-
mary data. Retrieved January 27, 2006, from http://
dq.cde.ca.gov/dataquest/
Carskadon, M. A. (1990). Patterns of sleep and sleepiness
in adolescents. Pediatrician, 17, 5–12.
Carskadon, M. A. (2002). Adolescent sleep patterns: Biologi-
cal, social, and psychological influences (Vol. 297). New
York: Cambridge University Press.
Carskadon, M. A., Acebo, C., & Jenni, O. G. (2004). Regu-
lation of adolescent sleep: Implications for behavior.
Annals of the New York Academy of Sciences, 1021, 276–
291. doi:10.1196/annals.1308.032
Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss,
learning capacity and academic performance. Sleep Medi-
cine Reviews, 10, 323–337. doi:10.1016/j.smrv.2005.11.001
Dempster, F. N., & Farris, R. (1990). The spacing effect:
Research and practice. Journal of Research and Develop-
ment in Education, 23, 97–101.
Diekelmann, S., & Born, J. (2010). The memory function
of sleep. Nature Reviews Neuroscience, 11, 114–126.
doi:10.1038/nrn2762
Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M.,
Reuman, D., Flanagan, C., et al. (1993). Development
during adolescence: The impact of stage–environment
fit on young adolescents’ experiences in schools and in
families. American Psychologist, 48, 90–101. doi:10.1037/
0003-066x.48.2.90
Fukuda, K., & Ishihara, K. (2001). Age-related changes of
sleeping pattern during adolescence. Psychiatry and
Clinical Neurosciences, 55, 231–232.
Fuligni, A. J., & Hardway, C. (2006). Daily variation in
adolescents’ sleep, activities, and psychological well-
being. Journal of Research on Adolescence, 16, 353–378.
doi:10.1111/j.1532-7795.2006.00498.x
Fuligni, A. J., & Stevenson, H. W. (1995). Time use and
mathematics achievement among American, Chinese,
and Japanese high school students. Child Development,
66, 830–842.
Giannotti, F., Cortesi, F., Sebastiani, T., & Ottaviano, S.
(2002). Circadian preference, sleep and daytime
behaviour in adolescence. Journal of Sleep Research, 11,
191–199.
Keith, T. Z. (1982). Time spent on homework and high
school grades: A large-sample path analysis. Journal of
Educational Psychology, 74, 248–253.
Kornell, N. (2009). Optimising learning using flashcards:
Spacing is more effective than cramming. Applied Cog-
nitive Psychology, 23, 1297–1317.
Krull, J. L., & MacKinnon, D. P. (2001). Multilevel model-
ing of individual and group level mediated effects.
Multivariate Behavioral Research, 36, 249–277.
Lockley, S. W., Skene, D. J., & Arendt, J. (1999). Comparison
between subjective and actigraphic measurement of
sleep and sleep rhythms. Journal of Sleep Research, 8,
175–183.
To Study or to Sleep? 141
National Sleep Foundation. (2006). Sleep in America poll.
Retrieved February 18, 2011, from http://www.sleep-
foundation.org
Noland, H., Price, J. H., Dake, J., & Telljohann, S. K.
(2009). Adolescents’ sleep behaviors and perceptions of
sleep. Journal of School Health, 79, 224–230. doi:10.1111/
j.1746-1561.2009.00402.x
Pilcher, J. J., & Walters, A. S. (1997). How sleep depriva-
tion affects psychological variables related to college
students’ cognitive performance. Journal of American
College Health, 46, 121–126.
Shanahan, M. J., & Flaherty, B. P. (2001). Dynamic pat-
terns of time use in adolescence. Child Development, 72,
385–401.
Taras, H., & Potts-Datema, W. (2005). Sleep and student
performance at school. Journal of School Health, 75, 248–
254. doi:10.1111/j.1746-1561.2005.00033.x
Wight, V. R., Price, J., Bianchi, S. M., & Hunt, B. R. (2009).
The time use of teenagers. Social Science Research, 38,
792–809.
Witkow, M. R. (2009). Academic achievement and adoles-
cents’ daily time use in the social and academic
domains. Journal of Research on Adolescence, 19, 151–172.
doi:10.1111/j.1532-7795.2009.00587.x
Wolfson, A. R., & Carskadon, M. A. (1998). Sleep sched-
ules and daytime functioning in adolescents. Child
Development, 69, 875–887. doi:10.2307/1132351
Wolfson, A. R., & Carskadon, M. A. (2003). Understand-
ing adolescents’ sleep patterns and school performance:
A critical appraisal. Sleep Medicine Reviews, 7, 491–506.
Wolfson, A. R., Carskadon, M. A., Acebo, C., Seifer, R.,
Fallone, G., Labyak, S. E., et al. (2003). Evidence for the
validity of a sleep habits survey for adolescents. Sleep,
26, 213–216.
142 Gillen-O’Neel, Huynh, and Fuligni
