Does Homework Improve Academic Achieve Ment A Synthesis of Research Cooper Et Al
Does Homework Improve Academic Achieve Ment A Synthesis of Research Cooper Et Al
Does Homework Improve Academic Achieve Ment A Synthesis of Research Cooper Et Al
In this article, research conducted in the United States since 1987 on the effects
o f homework is summarized Studies are grouped intofour research designs. The
authorsfound that all studies, regardless of type, had designflaws. However,
both within and across design types, there was generally consistent evidencefor
a positive influence o f homework on achievement. Studies that reported sim
ple homework-achievement correlations revealed evidence that a stronger
correlation existed (a) in Grades 7-12 than in K-6 and (b) when students rather
than parents reported time on homework. No strong evidence wasfound for an
association between the homework-achievement link and the outcome measure
(grades as opposed to standardized tests) or the subject matter (reading as
opposed to math). On the basis o f these results and others, the authors suggest
future research.
L
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Cooper et al.
(Lee & Pruitt, 1979). Finally, homework can require students to integrate separately
learned skills and concepts (Lee & Pruitt, 1979). This might be accomplished using
book reports, science projects, or creative writing.
Homework has other purposes in addition to enhancing instruction. It can be used
to (a) establish communication between parent and child (Acock & Demo, 1994;
Balli, Demo, & Wedman, 1998; Epstein, Simon, & Salinas, 1997^}onzalez, Andrade,
Civil, & Moll, 2001; Scott-Jones, 1995; Van Voorhis, 2003); (b) fulfill directives from
school administrators (Hoover-Dempsey, Bassler, & Burow, 1995); and (c) punish
students (Epstein & Van Voorhis, 2001; Xu & Como, 1998). To this list might be
added the public relations objective of simply informing parents about what is going
on in school (Coleman, Hoffer, & Kilgore, 1982; Como, 1996; Rutter, Maughan,
Mortimore, & Ouston, 1979).
Homework assignments rarely reflect a single purpose. Rather, most assignments
serve several different purposes; some relate to instruction, whereas others may meet
the purposes of the teacher, the school administration, or the school district.
The degree o f choice afforded a student refers to whether the homework assign
ment is compulsory or voluntary. Related to the degree of choice, completion dead
lines can vary from short term, meant to be completed overnight or for the next class
meeting, to long term, with students given days or weeks to complete the task. The
degree o f individualization refers to whether the teacher tailors assignments to meet
the needs of each student or whether a single assignment is presented to groups of
students or to the class as a whole. Finally, homework assignments can vary accord
ing to the social context in which they are carried out. Some assignments are meant
for the student to complete independent of other people. Assisted homework explic
itly calls for the involvement of another person, a parent or perhaps a sibling or friend.
Still other assignments involve groups of students working cooperatively to produce
a single product.
Overview
The Importance o f Homework and Homework Research
Homework is an important part of most school-aged childrens daily routine.
According to the National Assessment of Educational Progress (Campbell et al.,
1996), over two-thirds of all 9-year-olds and three-quarters of all 13- and 17-year-
olds reported doing some homework every day. Sixteen percent of 9-year-olds
reported doing more than 1 hour of homework each day, and this figure jumped to
37% for 13-year-olds and 39% for 17-year-olds. More recent surveys support the
extensive use of homework, although the amount of homework that students report
varies from study to study, depending perhaps on how the question is asked. For
example, Gill and Schlossman (2003) reported recent declines in time spent on
homework. However, among the youngest students, age 6 to 8 , homework appears
to have increased between 1981 (52 minutes weekly) and 1997 (128 minutes weekly;
Hofferth & Sandberg, 2000).
Homework likely has a significant impact on students educational trajectories.
Most educators believe that homework can be an important supplement to in-school
academic activities (Henderson, 1996). However, it is also clear from the surveys
mentioned earlier that not all teachers assign homework and/or not all students com
plete the homework they are assigned. This suggests that whatever impact homework
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Does Homework Improve Academic Achievement?
might have on achievement varies from student to student, depending on how much
each student is assigned or completes.
Homework is often a source of friction between home and school. Accounts of con
flicts between parents and educators appear often in the popular press (e.g., Ratnesar,
1999; Coutts, 2004; Kralovec & Buell, 2000; Loveless, 2003). Parents protest
that assignments are too long or too short, too hard or too easy^or too ambiguous
(Baumgartner, Bryan, Donahue, & Nelson, 1993; Kralovec & Buell, 2000; Warton,
1998). Teachers complain about a lack of support from parents, a lack of training in
how to construct good assignments, and a lack of time to prepare effective assign
ments (Farkas, Johnson, & Duffet, 1999). Students protest about the time that home
work takes away from leisure activities (Coutts, 2004; Kralovec & Buell, 2000).
Many students consider homework the chief source of stress in their lives (Kouzma
& Kennedy, 2002).
To date, the role of research in forming homework policies and practices has been
minimal. This is because the influences on homework are complex, and no simple,
general finding applicable to all students is possible. In addition, research is plentiful
enough that a few studies can always be found to buttress whatever position is desired,
while the counter-evidence is ignored. Thus advocates for or against homework
often cite isolated studies either to support or to refute its value.
It is critical that homework policies and practices have as their foundation the best
evidence available. Policies and practices that are consistent with a trustworthy
synthesis of research will (a) help students to obtain the optimum education benefit
from homework, and (b) help parents to find ways to integrate homework into a
healthy and well-rounded family life. It is our intention in this article to collect as
much of the research as possible on the effects of homework, both positive and
negative, conducted since 1987. We will apply narrative and quantitative techniques
to integrate the results of studies (see Cooper, 1998; Cooper & Hedges, 1994). While
research rarely, if ever, covers the gamut of issues and circumstances confronted
by educators, we hope that the results of this research synthesis will be used both
to guide future research on homework and to assist in the development of policies
and practices consistent with the empirical evidence.
A Brief History o f Homework in the United States
Public attitudes toward homework have been cyclical (Gill & Schlossman, 1996,
2004). Prior to the 20th century, homework was believed to be an important means
for disciplining childrens minds (Reese, 1995). By the 1940s, a reaction against
homework had set in (Nash, 1930; Otto, 1941). Developing problem-solving abilities,
as opposed to learning through drill, became a central task of education (Lindsay,
1928; Thayer, 1928). Also, the life-adjustment movement viewed home study as an
intrusion on other at-home activities (Patri, 1925; San Diego City Schools Research
Department, 1936).
The trend toward less homework was reversed in the late 1950s after the Russians
launched the Sputnik satellite (Gill & Schlossman, 2000; Goldstein, 1960; Epps,
1966). Americans became concerned that a lack of rigor in the educational system
was leaving children unprepared to face a complex technological future and to
compete against our ideological adversaries. Homework was viewed as a means of
accelerating the pace of knowledge acquisition. But in the mid-1960s the cycle
again reversed itself (Jones & Colvin, 1964). Homework came to be seen as a
. 3
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Cooper et al.
symptom of excessive pressure on students. Contemporary learning theories again
questioned the value of homework and raised its possible detrimental consequences
for mental health.
By the mid-1980s, views of homework had again shifted toward a more positive
assessment (National Commission on Excellence in Education, 1983). In the wake
of declining achievement test scores and increased conggm about Americans
ability to compete in a global marketplace, homework underwent its third renais
sance in 50 years. However, as the century turned, and against the backdrop of con
tinued parental support for homework (Public Agenda, 2000), a predicable backlash
set in, led by beleaguered parents concerned about the stresses on their children
(Winerip, 1999).
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Does Homework Improve Academic Achievement?
homework and achievement was nearly r = 0 ; for students in middle grades it was
r = .07; and for high school students it was r = .25.
The Need fo r a New Synthesis o f the Homework Literature
There are three reasons for conducting a new synthesis of the homework literature:
(a) to update the evidence on past conclusions about the effect^of homework and
determine if the conclusions from research need modification; (b) to determine
whether some of the questions left unanswered by the earlier syntheses can now
be answered; and (c) to apply new research synthesis techniques.
In the years since the completion of Coopers (1989) meta-analysis, a substantial
new body o f evidence has been added to the homework literature. For example,
a search of ERIC, PsycINFO, Sociological Abstracts, and Dissertation Abstracts
between January 1987 (when the search for the earlier synthesis ended) and Decem
ber 2003 indicated that over 4,000 documents with homework as a keyword had been
added to these reference databases. When we delimited this search to documents
that the reference engine cataloged as empirical, nearly 900 documents remained.
Yet we know of no comprehensive attempt to synthesize this new literature. There
fore, a reassessment of the evidence seems timely, both to determine if the earlier
conclusions need to be modified and to benefit from the added precision that the
new evidence can bring to the current assessment.
Coopers meta-analysis revealed a consistent influence of grade level on the
homework-achievement relationship. However, it produced ambiguous results
regarding the possible differential impact of homework on different subject matters
and on different measures of achievement. Specifically, research using different
comparison groups (i.e., no homework, supervised study, correlations involving
different reported amounts of homework) produced different orderings or magnitudes
of homeworks relation to achievement for different subject matters and achievement
measures. Also, Cooper (1989) found uniformly nonsignificant relationships between
the sex of the student and the magnitude of the homework-achievement relationship.
However, some recent theoretical perspectives (Covington, 1998; Deslandes &
Cloutier, 2002; Harris, Nixon, & Rudduck, 1993; Jackson, 2003) suggest that girls
generally hold more positive attitudes than boys toward homework and expend
greater effort on it. Emerging evidence from some homework studies (Harris et al.,
1993; Hong & Milgram, 1999; Younger & Warrington, 1996) lends empirical sup
port to these perspectives.
While these theories and results do not directly predict a stronger relationship
between homework and achievement for girls than for boys (that is, they predict a
main effect of higher levels of achievement among girls than among boys but do
not indicate why differences in homework attitude and effort within the sexes would
be more closely tied to achievement for one sex than the other, an interaction effect),
they do suggest that this remains an important issue. Therefore, exploring these
moderating relationships will be a focus of the present synthesis.
Also, the Cooper (1989) synthesis paid only passing attention to the ability of
the cumulated evidence to establish a causal relationship between homework and
achievement. Clearly, the 50 studies that took naturalistic, cross-sectional measures
of the amount of time students spent on homework and correlated these with measures
of achievement cannot be used to establish causality. About half of the studies that
introduced homework as an exogenous intervention and then compared achievement
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Cooper etal.
for students who did homework with that of students who did not, or who had in
school supervised study, employed random assignment of students to conditions.
The other half sometimes did and sometimes did not employ a priori matching or
post hoc statistical equating to enhance the similarity of homework and no-homework
groups. When homework was compared with no-homework, Cooper reported that
studies that used random assignment produced positive effectjhomework similar
to nonrandom assignment studies. However, when compared with in-school super
vised study, random-assignment designs revealed no difference between the home
work and in-school study students. We will test to determine whether these findings
still hold for the new evidence.
Also, since the earlier synthesis appeared, numerous studies have employed
structural equation modeling to test the fit of complex models of the relationship
between various factors and student achievement. Homework has been used as a
factor in many of these models. The earlier synthesis did not include these designs,
but this synthesis will.
Methodologically, the past two decades have introduced new techniques and
refinements in the practice of research synthesis. These include, among others, two
important advances. First, there is now a greater understanding of meta-analytic error
models involving the use of fixed and random-error assumptions that add precision to
statements about the generality of findings. Second, new tests have been developed
to estimate the impact of data censoring on research synthesis findings. These give us
a better sense of the robustness of findings against plausible missing data assumptions.
We will use these in the synthesis that follows.
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TABLE 1
Potential effects of homework that might serve as outcomes for research
Potential positive effects
Immediate achievement and learning
Better retention of factual knowledge
Increased understanding "
Better critical thinking, concept formation, information processing
Curriculum enrichment
Long-term academic benefits
More learning during leisure time
Improved attitude toward school
Better study habits and skills
Nonacademic benefits
Greater self-direction
Greater self-discipline
Better time organization
More inquisitiveness
More independent problem-solving
Parental and family benefits
Greater parental appreciation of and involvement in schooling
Parental demonstrations of interest in childs academic progress
Student awareness of connection between home and school
Potential negative effects
Satiation
Loss of interest in academic material
Physical and emotional fatigue
Denial of access to leisure time and community activities
Parental interference
Pressure to complete homework and perform well
Confusion of instructional techniques
Cheating .
Copying from other students
Help beyond tutoring
Increased differences between high and low achievers
Note. Adapted from Cooper (1989). Copyright 2005 by American Psychological Association.
Reprinted with permission.
direction and self-discipline (Como, 1994; Zimmerman, Bonner, & Kovach, 1996),
better time organization, more inquisitiveness, and more independent problem solv
ing. These skills and attributes apply to the nonacademic spheres of life as well as
the academic.
Finally, homework may have positive effects on parents and families (Hoover-
Dempsey et al., 2001). Teachers can use homework to increase parents appreciation
of and involvement in schooling (Balli, 1998; Balli, Wedman, & Demo, 1997; Epstein
& Dauber, 1991; Van Voorhis, 2003). Parents can demonstrate an interest in the
academic progress of their children (Epstein & Van Voorhis, 2001; Balli, Demo,
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Cooper et al.
& Wedman, 1998). Students become aware of the connection between home and
school.
Some negative effects attributed to homework contradict the suggested positive
effects. For instance, opponents of homework have argued that it can have a negative
influence on attitudes toward school (Chen, & Stevenson, 1989), by satiating stu
dents on academic pursuits. They claim any activity remairrw^jyarding for only so
long, and children may become overexposed to academic tasks (Bryan, Nelson, &
Mathru, 1995). Related to the satiation argument is the notion that homework leads
to general physical and emotional fatigue. Homework can also deny children access
to leisure time and community activities (Warton, 2001; Coutts, 2004). Proponents
of leisure activities point out that homework is not the only circumstance under
which after-school learning takes place. Many leisure activities teach important
academic and life skills.
Involving parents in the schooling process can have negative consequences
(Epstein, 1988; Levin, Levy-Shiff, Appelbaum-Peled, Katz, Komar, & Meiran, 1997;
Cooper, Lindsay, & Nye, 2000). Parents pressure students to complete homework
assignments or to do them with unrealistic rigor. Also, parents may create confusion
if they are unfamiliar with the material that is sent home for study or if their approach
to teaching differs from that used in school. Parental involvementindeed the
involvement of anyone else in homeworkcan sometimes go beyond simple tutor
ing or assistance. This raises the possibility that homework might promote cheating
or excessive reliance on others for help with assignments.
Finally, some opponents of homework have argued that home study has increased
differences between high- and low-achieving students, especially when the achieve
ment difference is associated with economic differences (Scott-Jones, 1984; Odum,
1994; McDermott, Goldman, & Varenne, 1984). They suggest that high achievers
from well-to-do homes will have greater parental support for home study, including
more appropriate parental assistance. Also, these students are more likely to have
access to places conducive to their learning style in which to do assignments and
better resources to help them complete a ss ig n m e n ts successfully.
With few exceptions, the positive and negative consequences of homework can
occur together. For instance, homework can improve study habits at the same time
that it denies access to leisure-time activities. Some types of assignments can pro
duce positive effects, whereas other assignments produce negative ones. In fact, in
light of the host of ways that homework assignments can be construed and carried
out, complex patterns of effects ought to be expected.
The present synthesis will search for any and all of the above possible effects
of homework. However, it is unrealistic to expect that any but a few of these will
actually appear in the research literature. We expected the large preponderance of
measures to involve achievement test scores, school grades, and unit grades. A few
measures of students attitudes toward school and subject matters might also appear.
Other measures of homeworks effect were expected to be few and far between. One
reason for this is because many of the other potential effects are subtle. Therefore,
their impact might take a long time to accrue, and few researchers have the resources
to mount and sustain long-term longitudinal research. Another reason for the lack
of subtle measures of homeworks effect is that the homework variable is often one
of many influences on achievement being examined in a study. It is achievement
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r
U 9
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] Does Homework Improve Academic Achievement?
| go up through die highest point on the measured scales, more than 2 hours. In the
} present synthesis, we included studies examining time on homework because of their
relevance to homeworks general effectiveness; therefore, we also looked for studies
! that might replicate or extend this finding.
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Cooper et al.
in the reference and citation databases. First, we contacted the dean, associate dean,
or chair of 77 colleges, schools, or departments of education at research-intensive
institutions of higher education and requested that they ask their faculty to share with
us any research they had conducted that related to the practice of assigning homework.
Second, we sent similar letters to 21 researchers who, as revealed by our reference
database search, had been the first author on two or morenftieles on homework
and academic achievement between 1987 and the end of 2003. Finally, we sent
similar letters to the directors of research or evaluation in more than a hundred
school districts, obtained from the membership list of the National Association of
Test Directors.
Two researchers in our team then examined each title, abstract, or document. If
either of the two felt that the document might contain data relevant to the relation
ship between homework and an achievement-related outcome, we obtained the full
document (in the case of judgments made on the titles or abstracts).
Finally, the reference sections of relevant documents were examined to determine
if any cited works had titles that also might be relevant to the topic.
Criteria fo r Including Studies
For a study to be included in the research synthesis, several criteria had to be met.
Most obviously, the study had to have estimated in some way the relationship between
a measure of homework activity on the part of a student and a measure of achieve
ment or an achievement-related outcome.
Two sampling restrictions were placed on included studies. Each study had to
assess students in kindergarten through 12th grade. We excluded studies conducted
on preschool-aged children or on postsecondary students. It was felt that the purpose
and causal structure underlying the homework-achievement relationship would be
very different for these populations. For similar reasons, we included only studies
conducted in the United States.
Finally, the report had to contain enough information to permit the calculation
of an estimate of the homework-achievement relationship.
Information Retrieved From Evaluations
Numerous characteristics of each study were included in the database. These
characteristics encompassed six broad distinctions among studies: (a) the research
report; (b) the research design; (c) the homework variable; (d) the sample of students;
(e) the measure of achievement, and (f) the estimate of the relationship between
homework and achievement.
Report Characteristics
Each database entry began with the name of the author of the study. Then the
year of the study was recorded, followed by the type of research report. Each research
report was categorized as a journal article, book chapter, book, dissertation, Masters
thesis, private report, government report (state or federal), school or district report,
or other type of report.
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Cooper et al.
underachieving/below grade level, possessing a learning disability, overachieving/
above grade level).
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Coder Reliability
Two coders extracted information from all reports selected for inclusion. Dis
crepancies were first noted and discussed by the coders, and if agreement was not
reached the first author was consulted. Because all studies were independently coded
twice and all disagreements resolved by a third independent coder, we did not cal
culate a reliability for this process (which would have entailed 4 Qiining three more
coders and having them code at least a subset of studies).
Methods o f Data Integration
Before conducting any statistical integration of the effect sizes, we first counted the
number of positive and negative effects. For studies with effect size information,
we calculated the median and range of estimated relationships. Also, we examined
the distribution of sample sizes and effect sizes to determine if any studies con
tained statistical outliers. Grubbss (1950) test, also called the maximum normed
residual test, was applied (see also Barnett & Lewis, 1994). This test identifies
outliers in univariate distributions and does so one observation at a time. If outliers
were identified, (using p < .05, two-tailed, as the significance level) these values
would be set at the value of their next nearest neighbor.
Both published and unpublished studies were included in the synthesis. However,
there is still the possibility that we did not obtain all studies that have investigated
the relationship between homework and achievement. Therefore, we used Duval
and Tweedies (2000 a, 2000 b) trim-and-fill procedure to test whether the distribution
of effect sizes used in the analyses were consistent with variation in effect sizes that
would be predicted if the estimates were normally distributed. If the distribution
of observed effect sizes was skewed, indicating a possible bias created either by the
study retrieval procedures or by data censoring on the part of authors, the trim-and-
fill method provides a way to estimate the values from missing studies that need to
be present to approximate a normal distribution. Then, it imputes these missing
values, permitting an examination of an estimate of the impact of data censoring
on the observed distribution of effect sizes.
Calculating Average Effect Sizes 1
We used both weighted and unweighted procedures to calculate average effect
sizes across all comparisons. In the unweighted procedure, each effect size was
given equal weight in calculating the average value. In the weighted procedure, each
independent effect size was first multiplied by the inverse of its variance. The sum
of these products was then divided by the sum of the inverses. Generally speaking,
weighted effect sizes are preferred because they give the most precise estimates of
the underlying population values (see Shadish & Haddock, 1994). The unweighted
effect sizes are also reported because in instances in which these are very different
from the weighted estimates, this can give an indication that the magnitude of the
effect size and sample size are correlated, sometimes suggesting that publication
bias might be a concern. Also, 95% confidence intervals were calculated for weighted
average effects. If the confidence interval did not contain zero, then the null hypoth
esis of no homework effect can be rejected.
Identifying Independent Hypothesis Tests
One problem that arises in calculating effect sizes involves deciding what con
stitutes an independent estimate of effect. Here, we used a shifting unit of analysis
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Cooper et al.
approach (Cooper, 1998). In this procedure, each effect size associated with a study
is first coded as if it were an independent estimate of the relationship. For example,
if a single sample of students permitted comparisons of homeworks effect on both
math and reading scores, two separate effect sizes were calculated. However, for
estimating the overall effect of homework, these two effect sizes were averaged prior
to entry into the analysis, so that the sample only contribulgdone effect size. To
calculate the overall weighted mean and confidence interval, this one effect size would
be weighted by the inverse of its variance (based primarily on sample size, which
should be about equal for the two component effect sizes). However, in an analy
sis that examined the effect of homework on math and reading scores separately,
this sample would contribute one effect size to each estimate of a category mean
effect size.
The shifting unit of analysis approach retains as many data as possible from each
study while holding to a minimum any violations of the assumption that data points
are independent. Also, because effect sizes are weighted by sample size in the cal
culation of averages, a study with many independent samples containing just a few
students will not have a larger impact on average effect size values than a study with
only a single, or only a few, large independent samples.
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Does Homework Improve Academic Achievement?
if an analysis reveals that a moderator variable is significant under fixed-error assump
tions but not under random-error assumptions, this result suggests a limit on the
generalizability of inferences about the moderator variable.
All statistical analyses were conducted using the Comprehensive Meta-Analysis
statistical software package (Borenstein, Hedges, Higgins, & Rothstein, 2005).
Results
Studies With Exogenous Introductions o f Homework
The literature search located six studies that employed a procedure in which the
homework and no-homework conditions were imposed on students explicitly for
the purpose of studying homeworks effects. None of these studies was published.
Some of the important characteristics and outcomes of each study are presented in
Table 3.
Apparently, only one study used random assignment of students to conditions.
McGrath (1992) looked at the effect of homework on the achievement of 94 high
school seniors in three English classes studying the play Macbeth. At one point in
the research report, the author states that half of the students elected to receive no
homework and half elected to receive homework (p. 27). However, at another
point, the report states that each student was assigned to a condition by the alpha
betic listing of his/her last name (p. 29). Thus it might be (optimistically) assumed
that the students in each of the three classes were haphazardly assigned to homework
and no-homework conditions. In the analyses, the student was used as the unit. The
experiment lasted 3 weeks and involved 12 homework assignments. Students doing
homework did significantly better on a posttest achievement measure, d = .39.
A study by Foyle (1990) assigned four whole 5th-grade classrooms (not indi
vidual students) to conditions at random, one to a practice homework condition, one
to a preparation homework condition, and two to a no-homework control condition.
Clearly, assigning only one classroom to each condition, even when done at random,
cannot remove confounded classroom differences from the effect of homework.
For example, all four classrooms used a cooperative learning approach to teaching
social studies, but one classroom (assigned to the practice homework condition) used
a different cooperative learning approach from the other three classes. Also, the
student, rather than the classroom, was used as the unit for statistical analysis, cre
ating the concern that within-class dependencies among students were ignored.
Analysis revealed that students differed significantly on a social studies pretest and
on a standard measure of intelligence, but it was not reported whether there were
preexisting classroom differences on these measures. Students doing homework
outperformed no-homework students on unadjusted posttest scores, d = .90, and
on posttest scores adjusted for pretest and intelligence differences, d = .99.
Foyle (1984) conducted a similar study on six high school classes in American
history. Here, the experimenter reported that the assignment of treatment and control
groups was under the experimenters control (p. 90) and two intact classrooms
were each assigned randomly to practice homework, preparation homework, and
no-homework conditions. However, the student was again used as the unit of analy
sis. Analyses of covariance that controlled for pretest scores, aptitude differences,
and the students sex revealed that students doing homework had higher posttest
achievement scores than students who did not. The covariance analysis and post hoc
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Does Homework Improve Academic Achievement?
tests revealed a significant positive effect of homework, but an effect size could not
be calculated from the adjusted data (because the reported F-test contained two
degrees of freedom in the numerator and means and standard deviations were not
provided). The approximate, unadjusted homework effect was d = .46.
Finstad (1987) studied the effect of homework on mathematics achievement for
39 second-grade students in two intact classrooms. One unit, on-place values to 100,
was used, but neither the frequency nor the duration of assignments was reported.
One classroom was assigned to do homework and the other not. It was not reported
how the classroom assignments were carried out, but it was reported that there were
no pretest differences between the classes. Data were analyzed on the student level
without adjustment. The students in the classroom doing homework performed sig
nificantly better on a posttest measure, d = .97.
Meloy (1987) studied the effects of homework on the English skills (sentence
components, writing) of third and fourth graders. Eight intact classrooms took part
in the study and classes were matched on a shortened version of the Iowa Test of
Basic Skills (ITBS) language subtest before entire classes were randomly assigned
to homework and no-homework conditions. However, examination of pretest dif
ferences on the ITBS language subscale revealed that the students assigned to do
homework scored significantly higher than students in no-homework classes. Thus
a pretest-posttest design was used to control for the initial group differences, but
pretests were used as a within-students factor rather than as a covariate (meaning
a significant homework effect would appear as an interaction with time of testing).
Also, students who scored above a threshold score on the pretest were excluded from
the posttest analysis. Thus only 106 of an original sample consisting of 186 students
were used in the analyses, and excluded students were not distributed equally across
homework and no-homework conditions. Grade levels were analyzed separately,
and classrooms were a factor in the analyses. The class-within-condition effect was
not significant, so, again, the student was used as the unit of analysis. Homework
was assigned daily for 40 instructional days. This study also monitored the home
work completion rates in classrooms and set up reinforcement plans, different for
each class, to improve completion rates. The effecfs of homework were gauged by
using a researcher-modified version of the ITBS language subtest and a unit mas
tery test from the textbook. The complex reporting of statistical analyses made it
impossible to retrieve simple effect estimates frorii the data. However, the author
reported that the condition-by-time interactions indicated that homework had a sig
nificant negative effect on ITBS scores for third graders and a significant positive
effect on fourth graders unit test scores.
Finally, Townsend (1995) examined the effects of homework on the acquisition
of vocabulary knowledge and understanding among 40 third-grade students in two
classes, both taught by the experimenter. Treatment was given to classes as a whole
and it was not stated how each class was assigned to the homework or no-homework
condition. The student was used as the unit of analysis. A teacher-prepared posttest
measure of vocabulary knowledge suggested that the homework group performed
better, d = .71.
In sum, the six studies that employed exogenous manipulations all revealed
a positive effect of homework on unit tests. One study (Meloy, 1987) revealed a
negative effect on a standardized test modified by the experimenter. Four of the six
studies employed random assignment, but in three cases assignment to conditions was
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Cooper et al.
carried out at the classroom level, using a small number of classrooms, and analyses
were conducted using the student as the unit of analysis. In the only instance in which
random assignment appears to have occurred within classes (McGrath, 1992), students
also were used as the unit of analysis. Also, random assignment appears to have
failed to produce equivalent groups in one study (Meloy, 1987).
While the introduction of homework as an exogenous intervention is a positive
feature of these studies, other methodological considerations make it difficult to draw
strong causal inferences from their results. Still the results are encouraging because
of the consistency of findings. The measurable effects of homework on unit tests
varied between d = .39 and d = .97. Also, the three studies that successfully used
random assignment, fixed weighted d = .53 (95% Cl = .291.19), random weighted
d = .54 (9 5 % Cl = ,26/.82), produced effect sizes that were smaller than those of
two studies that used other techniques to produce equivalent groups and for which
effect sizes could be calculated, fixed weighted d = .83 (95% Cl = .37/1.30), ran
dom weighted d = .83 (95% Cl = .37/1.30); but the difference in mean ^-indexes
between these two sets of studies was not significant, fixed Q( 1) = 1.26, ns, ran
dom Q(l) = 1.12, ns. Collapsing across the two study designs and using fixed-error
assumptions, the weighted mean d-index across the five studies from which effect
sizes could be obtained was d = .60 and was significantly different from zero (95%
Cl = .38/.82). Using a random-error model, the weighted average rf-index was also
.60 (95% Cl = ,38/.82).
To take into account the within-class dependencies that were not addressed in the
reported data analyses, we recalculated the mean effect sizes and confidence inter
vals by using an assumed intraclass correlation of .35 to estimate effective sample
sizes. In this analysis, the weighted mean d-index was .63, using both fixed and
random-error assumptions, and both were statistically different from zero (95% Cl =
.03/1.23, for both). The mean d-index would not have been significant if an intraclass
correlation of .4 was assumed. Additionally, the tests of the distribution of ^-indexes
revealed that we could not reject the hypothesis that the effects were estimating the
same underlying population value when students, were used as the unit of analysis,
QjueA5) = 4.09, ns, Qrandom(5) = 4.00, ns, or when effective sample sizes were used
as the unit, < 2/^5) = .54, ns, Q w m(5) = .54, ns.
And finally, the trim-and-fill analyses were conducted looking for asymmetry using
both fixed and random-error models to impute the mean d-index (see Borenstein
et al., 2005). Neither of the analyses produced results different from those described
above. There was evidence that two effect sizes might have been missing. Imputing
them would lower the mean rf-index to d = .48 (95% Cl = .22/.74) using both fixed
and random-error assumptions.
The small number of studies and their variety of methods and contexts preclude
their use in any formal analyses investigating possible influences on the magnitude of
the homework effect, beyond comparing studies that used random assignment versus
other means to create equivalent groups. The studies varied not only in research design
but also in subject matter, grade level, duration, amount of homework, and the degree
of alignment of the outcome measure with the content of assignments. Replications
of any important feature that might influence the homework effect are generally
confounded with other important features, and no visible pattern connecting effect
sizes to study features is evident.
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Studies Using Cross-Sectional Data and Control o f Third Variables
Studies Using the National Education Longitudinal Study (1988,1990, or 1992)
The literature search located nine reports that contained multivariate analyses
of data collected as part of the National Education Longitudinal Study of 1988 (NELS)
or in one of the NELS follow-ups on the same students in 1990, 1992, 1994, or
2000. These studies are described in Table 4. The NELS \9a>conducted by the
National Center for Educational Statistics and involved a nationally representative
two-stage stratified probability sample. The final student sample in the first wave
included 24,599 eighth-grade students. Each student completed achievement tests
in mathematics, reading, science, and social studies in 1988,1990, and 1992, as well
as a 45-minute questionnaire that included questions about school, school grades,
personal background, and school context. Various waves of the NELS also included
surveys of teachers, school administrators, and parents. Student transcripts were
collected at the end of their high school careers. Questions on homework were
completed by both students and teachers, and they were asked about the total min
utes of homework completed or assigned in different subject areas.
Several of the studies using the NELS data sampled students from the NELS
itself for the purpose of examining questions regarding restricted populations. For
example, Peng and Wright (1994) were interested in studying differences in relation
ships between predictors of achievement across ethnic groups, with a focus on Asian
Americans. Davis and Jordan (1996) focused on African American males, while
Roberts (2000) restricted the subsample to students attending urban schools only.
Examined as a group, the studies using NELS data use a wide variety of outcome
measure configurations and different sets of predictor variables, in addition to home
work. Still, every regression coefficient associated with homework was positive,
and all but one were statistically different from zero. The exception occurred in the
study of African American males on a composite measure of class grades (Davis
& Jordan, 1996).
The study revealing the smallest beta-weight was a dissertation by Hill (2003).
This report presents an unclear description of how the subsample drawn from the
NELS was defined. The text reports that students were omitted from the sample if they
attended public schools, live in suburban areas, are neither Black nor Hispanic;
and whose teachers are male, not certified in [the subject of the outcome variable],
have neither an undergraduate degree in education or in [the subject of the outcome
variable], and have neither a graduate degree in education or [the subject of the out
come variable] (pp. 45, 86 , 120). However, the tables in the report suggest that
White students were included in the samples. The regression models suggest that
students with teachers who had degrees in subjects other than the outcome variable
also were included. Thus it is difficult to determine whether sampling restrictions
might be the cause of the small regression coefficients associated with homework.
The dissertation by Lam (1996) deserves separate mention. In this study using
data from 12th graders, the amount of homework students reported doing was entered
into the regression equation as four dummy variables. This permitted an examination
of possible curvilinear effects of homework. As Table 4 reveals, students who reported
doing homework always had higher achievement scores than students who did not
do homework (coded as the dummy variable). However, the strongest relationship
between homework and achievement was found among students who reported doing
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Cooper et al.
7 to 12 hours of homework per week, followed by students who reported doing
13-20 hours per week. Students who reported doing more than 20 hours of home
work per week revealed a relationship with achievement test scores nearly equal to
those reporting between 1-6 hours of homework per week. While this result is sug
gestive of a curvilinear relationship between homework and achievement, we must
bear in mind that Lam restricted the sample of students to Asi^n Americans and
Caucasian Americans.
In sum, if we omit (a) the Hill (2003) study (which produced beta-weights of .01
and .02 ), as well as (b) those studies that reported unstandardized regression weights,
or (c) those for which coefficients could not be determined, then the reported beta-
weights for the relation between homework and standardized achievement test scores
range from .05 to .28. For composite achievement scores the range is from .05 to
.21; for math, it is .09 t o . 16; for reading,. 12 to .28; for science, .09 to .23; and for
social studies,. 11 t o . 18. Thus the ranges of estimated regression coefficients appear
quite similar across the subject areas. However, we would caution against drawing
any conclusions regarding the mediating role of subject matter on the homework-
achievement relationship from these data, because the number and type of predictors
in each model are confounded with subject matter. It should also be kept in mind that
these estimates refer to high school students only.
Studies Using Data Other Than the National Education Longitudinal Study
and Performing Multivariate Analyses
Table 5 provides information on 12 additional studies that performed multi
variate analysis on cross-sectional data in order to examine the relationship between
homework and achievement, with other variables controlled. Two of the studies
used the High School and Beyond database (Cool & Keith, 1991; Fehrmann, Keith,
& Reimers, 1987). The High School and Beyond database drew its 1980 base-year
sample of sophomores and seniors from high schools throughout the United States.
Probability sampling was used with overrepresentation of special populations.
Follow-up surveys were conducted in 1982 and 1^84. Brookhart (1997) used the
Longitudinal Study of American Youth database, containing a national probability
sample of approximately 6,000 seventh and tenth graders stratified by geographic
area and degree of urban development. The rest of the studies used data collected by
the researchers for the specific purpose of studying variables related to achievement.
Two studies conducted by Smith (1990, 1992), using overlapping data sets of
seventh, ninth, and eleventh graders, found some negative relationships between
homework and achievement. One of these findings (in Smith, 1992) revealed a small
but statistically significant negative relationship between the amount of time spent
on homework and language achievement, {3=.06. However, this study also revealed
a significant positive interaction between year in school and time spent on home
work. The interaction was not interpreted. This was the only significant negative
result obtained in any of the cross-sectional, multivariate studies.
The remaining studies that used secondary school students all revealed posi
tive and generally significant relationships. The three studies that used elementary
school students (Cooper et al., 1998; Olson, 1988; Wynn, 1996) all revealed posi
tive relationships between the homework measure and achievement (in Cooper et al.,
P = .22 for teacher-reported overall grades; in Olsen, P = .10 for math and P = .11
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Does Homework Improve Academic Achievement?
for reading; and in Wynn, P = .04 for grade point average). Thus, in addition to
using varying predictor variables in the regression models, these studies also included
a variety of outcome measures, including not only standardized tests but also
teacher-assigned grades. In one instance, (Hendrix, Sederberg, & Miller, 1990) the
outcome measure was not achievement but rather an indicator of school commitment/
alienation constructed by the researcher that measured the importance of successful
performance on school tasks, effort, and relevance of school work for students lives.
Thus we would again caution against drawing conclusions about mediating and
moderating variables from these studies. It seems safest simply to note that the pos
itive relationship between homework and achievement across the set of studies was
generally robust across sample types, models, and outcome measures.
Structural Equation Modeling Studies Using Data From the High School
and Beyond (1980,1982,1984) Longitudinal Studies
Table 7 provides information on four studies that tested structural equation
models using data from the High School and Beyond database. All coefficients but
one are positive and statistically significant. Keith and Benson (1992) found anon-
significant negative coefficient for a subsample of Native Americans, P = -.09. The
authors caution against strong interpretation of this finding because (a) the sample
size was small (n = 147), and (b) Native American students who attended Bureau
of Indian Affairs schools were not sampled. Still, it is generally the case that co
efficients for the homework-achievement relationship estimated using High School
and Beyond data are smaller than those estimate<|using NELS data.
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Does Homework Improve Academic Achievement?
parent, and a measure of academic achievement. These studies are listed in Table 8 .
The 32 studies reported 69 separate correlations based on 35 separate samples of
students. Cooper et al. (1998) reported 8 correlations, separating out effects for ele
mentary and secondary students (two independent samples) on both class grades and
standardized tests with time on homework reported by either students or parents.
Drazen (1992) reported 12 correlations, for reading, math, and multiple subjects for
three national surveys (three independent samples). Bents-HHTand colleagues (1988)
reported 8 correlations, for language arts, math, reading, and multiple subjects both
for class grades and for a standardized test of achievement. Epstein (1988), Olson
(1988), and Walker (2002) each reported 2 effect sizes, 1 for math and 1 for reading.
Fehrmann et al. (1992), Wynn (1996), and Keith and Benson (1992) each reported
2 correlations, 1 involving class grades and 1 involving achievement test results.
Hendrix et al. (1990) reported 3 correlations, 1 for multiple subjects, 1 for verbal
ability, and 1 for nonverbal ability. Mau & Lynn (2000) reported 3 correlations, 1 for
math, 1 for reading, and 1 for science. Singh et al. (2002) reported 2 correlations
for math and 1 for science.
The 32 studies appeared between the years 1987 and 2004. The sample sizes
ranged from 55 to approximately 58,000 with a median size of 1,584. The mean
sample size was 8,598 with a standard deviation of 12,856, suggesting a nonnormal
distribution. The Grubbs test revealed a significant outlier, p < .05. This sample was
the largest in the data set, reported by Drazen (1992) for six correlations obtained
from the 1980 High School and Beyond longitudinal study. As a result, we replaced
these six sample sizes with the next largest sample size in the data set, 28,051. The
mean sample size for the adjusted data set was 7,742 with a standard deviation
of 10,192.
Only three studies specifically mentioned that students were drawn from regular
education classrooms, and one of these studies included learning-disabled students
as well (Deslandes, 1999). The remaining studies did not report information on the
students achievement or ability level. Seventeen studies did not report information
on the socioeconomic status of students, 11 reported that the samples SES was
mixed, 3 described the sample as middle SE, and 1 as lower SES. Seventeen
studies did not report the sex make-up of the sample, while 14 reports said the sam
ple was comprised of both sexes. Only one study reported correlations separately
for males and females. Because of a lack of reporting or variation across categories,
no analyses were conducted on these variables.1
Of the 69 correlations, 50 were in a positive direction and 19 in a negative direc
tion. The mean unweighted correlation across the 35 samples (averaging multiple
correlations within each sample) was r = . 14, the median was r = . 17, and the cor
relations ranged from -.2 5 to .65.
The weighted average correlation was r= .24 using a fixed-error model with a 95%
confidence interval (95% Cl) from .24 to .25. The weighted average correlation
was r = .16 using a random-error model with a 95% confidence interval from .13
to .19. Clearly, then, the hypothesis that the relationship between homework and
achievement is r = 0 can be rejected under either error model. There were no sig
nificant outliers among the correlations, so all were retained for further analysis.
The trim-and-fill analyses were conducted in several different ways. We performed
the analyses looking for asymmetry, using both fixed and random-error models
to impute the mean correlation and creating graphs using both fixed and random
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Does Homework Improve Academic Achievement?
models (see Borenstein et al., 2005) while searching for possible missing correlations
on the left side of the distribution (those that would reduce the size of the positive
correlation). None of the analyses produced results different from those described
above. When we used a random-error model, there was evidence that three effect
sizes might have been missing and that imputing them would lower the mean fixed-
effect correlation to r = .23 (95% C l = .221.23). The randenterror results of this
analysis were r = .14 (95% Cl = .11/.17).2
Next, we carried out a moderator analysis examining the association between the
magnitude of correlations and the publication status of the study report. Seventeen
of the samples had been published and their results were compared with those of the
18 samples that had appeared as dissertations, ERIC documents, or unpublished
research reports. Under the fixed-error model, correlations from journal articles,
r = .25, were significantly higher than those from unpublished sources, r = .23,
<2(1) = 20.71, p < .0001. Under the random-error model, correlations from journal
articles, r = 18, were not statistically different from those from unpublished sources,
r = . 15, <2( 1) = 0.91, ns. In both instances, the absolute size of the difference was
quite small.
Moderator Analyses
Table 9 presents the results of analyses examining whether the magnitude of the
correlation between time spent on homework and achievement was moderated by
the type of achievement measure. Two studies using unstandardized tests scores,
one using a composite of standardized tests and class grades, and one not reporting
the type of achievement outcome were omitted from this analysis because there were
too few studies in each of these outcome-type categories. Thus the moderator
analysis compared results involving class grades with results involving standardized
achievement tests.
Under fixed-error assumptions, the correlation between time spent on homework
and class grades, r = .27 (95% Cl = .261.21), was significantly higher than that
involving standardized achievement test scores, f = .24 (95% Cl = .24/.25), 2(1) =
26.26, p < .0001. Under random-error assumptions, the correlation between time
spent on homework and class grades, r = . 19 (95% Cl = .11/.27), was not significantly
different from that involving standardized achievement test scores, r = .16 (95%
Cl = .14/. 19), 2(1) = 0.35, ns. In both instances, the absolute difference between
the correlations was quite small.
Table 9 also presents the results of analyses examining whether the magnitude
of the correlation between time spent on homework and achievement was moder
ated by the grade level of the students. Correlations were grouped into those
involving elementary school students, Grades K - 6 , and secondary school students,
Grades 7-12. One study (Tonglet, 2000) was omitted from the analysis because it
included students in Grades 5 and 8 and the correlation for the two grades could
not be separated. One correlation from Cooper et al. (1998) was omitted from the
analysis because it included students in Grades 6-12. Tonglet (2000) and Cooper
et al. (1998) reported sampling students from Grades 5 and 8 , and 6-12, respectively.
The correlation between time spent on homework and class grades was +.47 for
Tonglet. The correlation was +.21 for Cooper et al., who also reported a correlation
o f+.07 between time spent on homework and standardized achievement test scores.
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Does Homework Improve Academic Achievement?
Figure 1 presents a stem-and-leaf display of the 33 correlations associated with this
analysis.
Under fixed-error assumptions, the correlation between time spent on homework
and achievement was significantiy higher for secondary school students, r =.25 (95%
Cl = .25/25), than for elementary school students, r = -.0 4 (95% Cl = -0 6 /-.0 2 ),
Q( 1) = 710.68, p < .0001. Under random-error assumptions, the correlation
between time spent on homework and achievement was also significantly higher for
secondary school students, r = .20 (95% C l = AH .22), than for elementary school
students, r = .05 (95% Cl = -.03/. 13), <2(1) = 10.43,/? < .002. As indicated by the
confidence intervals, using the random-error model, the mean correlation between
time spent on homework and achievement was not significantly different from zero
for elementary school students.
Table 9 also presents the results of analyses examining whether the homework-
achievement correlation was moderated by the subject matter of the homework
assignment. One study involving science, 1 involving foreign language, and 1 involv
ing verbal and nonverbal ability were omitted from the analysis because there were
too few studies in each of these outcome-type categories. Thus the moderator
analysis compared only studies involving language arts, reading, mathematics, and
achievement across multiple subject domains.
First, we compared correlations involving language arts with correlations involv
ing reading. Using fixed-error assumptions, the three correlations involving language
arts revealed a nonsignificant average weighted correlation of r =-.01 (C l= -04/.02),
while the eight reading outcomes produced a significant positive correlation of r = .21
(Cl = .20/.21). These average correlations were significantly different from one
another, Q( 1) = 202.94, p < .0001. Using random-error assumptions, the average
language arts correlation was nonsignificant, r = .01 (Cl = -.10/.13), while reading
produced a significant positive correlation, r = .12 (Cl = .07/. 18). These average
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Cooper et al.
correlations approached being significantly different from one another, Q (l)=2.71,
p <. 10. Because of these results, we chose not to combine the language arts and read
ing data sets but instead to use only reading correlations in the subsequent analyses
examining subject matter as a moderator.
The average weighted correlations between time on homework and reading,
math, and multiple subjects were significantly differeatjrom one another under
fixed-error assumptions, Q(2) = 164.62, but not under random-error assumptions,
2(2) = 2.46, ns. We then proceeded to conduct two planned comparisons, one com
paring reading outcomes with math outcomes and one comparing both math and
reading outcomes with outcomes involving measures of multiple subjects.
Under fixed-error assumptions, the correlation between time spent on homework
and achievement was significantly higher for math, r = .24 (95% Cl = .24/.25) than
for reading, r= .21 (95% CI=.20/.21), Q(1) = 99.92, p < .0001. Under random-error
assumptions, the correlation between time spent on homework and achievement
was not significantly different for math, r = . 18 (95% Cl = . 13/23), than for reading,
r = .12 (95% Cl = .07/.18), <2(1) = 2.46, ns. In both instances, the absolute differ
ence between the correlations was quite small.
Under fixed-error assumptions, the correlation between time spent on home
work and achievement was significantly higher for multiple subjects, r =.25 (9 5 %
Cl = .25/.25) than for either reading or math alone, r =.23 (95% Cl = .227.23), Q(l) =
64.70, p < .0001 . Under random-error assumptions, the correlationbetween rima spent
on homework and achievement was not significantly different for multiple subjects,
r =. 16 (95% C l= . 12/.20), in comparison with that for reading or math alone, r= .1 6
(95%CI = .12/.19), G(l) = 0.004, ns. Again, in both instances, the absolute differen t
between the correlations was quite small.
Finally, Table 9 presents the results of analyses examining whether the homework
and achievement correlation was moderated by who provided data on the amount
of time spent on homework. All studies included information about whether it was
the student or a parent who was the respondent.
Under fixed-error assumptions, the correlation between time spent on homework
and achievement was significantly higher w |e n students made the report, r= .25
(95% Cl = ,25/.25) than when parents reported, r = -.0 3 (95% Cl = -0 5 /-.0 1 ),
<2(1)=631.70,/> < .0001. Under random-error assumptions, the correlation between
time spent on homework and achievement was still significantly stronger for students,
r= .19 (95% C I= .167.21), than for parents, r = - .0 2 (95% CI=-.10/.07), Q(1)=20.06,
P < .0001. Using the random-error models, the correlations involving parent reports
were not significantly different from zero.
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Does Homework Improve Academic Achievement ?
The findings produced a pattern of results regarding the direction and significance
for the moderators effect that was consistent with the main effects in 13 of the
14 subgroup analyses. That is, both the direction of the comparison between cor
relations and the significance of the difference between correlations (using both
fixed and random models) was the same when we compared the subgroup analyses
to the main effect analyses in all instances but one. The exception was that when
we used a random-error model to compare the relationship between homework and
class grades for four correlations at the elementary school level, r = .09 (95% Cl =
.10/.28), and six correlations at the secondary level, r = .21 (95% Cl = .12/.30),
the difference was not significantly different from zero, <2(1) = 1.18, ns. The direction
of the difference between the mean correlations was the same as that in the main
effect analyses. .
Finally, we looked to see whether the respondent providing information about
homework (the student or a parent) was confounded with any of the other three
moderator variables. We found that 3 times parents provided information on home
work in correlations involving class grades and 4 times when correlations involved
achievement tests. Similarly, 3 times parents provided information when homework
was associated with math, 2 times when associated with reading, and 3 times with
multiple subjects.
However, all parent reports on the amount of homework were provided tor stu
dents who were in Grades K6.3 Therefore it was possible that the significant dif
ference suggesting that the homework-achievement relationship was smaller for
elementary school than secondary school students might not hold if students were
respondents. To test this hypothesis, we re-ran the grade level analyses using only
students as respondents.
Under fixed-error assumptions, the correlation between time spent on home
work and achievement was significantly higher for secondary school students, r = .25
(95 % Cl = .251.25), than for elementary school students, r = .06 (95% C l=-.00/. 11),
(2(1)=47.48, p < .0001. Under random-error assumptions, the correlation between
time spent on homework and achievement was not significantly higher for secondary
school students, r = .19 (95% C l = .17/.22), than for elementary school students,
r= .22 (95% C I= .00/.42), <2 ( 1) = 0-57, ns. *
In light of these results, it is not surprising that we also found differences between
student and parent reports at the elementary school level. Under fixed-error assump
tions, the correlation between time spent on homework and achievement was sig
nificantly higher when elementary school students made the report, r = .06 (95%
C l = .00/. 11), than when parents of elementary school students made the report,
r = -.0 6 (95% Cl = - 08/-.04), <2(1) = 14.40, p < .001. Under random-error assump
tions, the correlation between time spent on homework and achievement was still
significantly stronger for elementary school student reports, r = .22 (95% Cl =
-.00/.42), than for parents, r = -.05 (95% Cl = -.11/.01), <2(1) = 5.40, p < .03. It
appears that, for elementary school students, parents report a small negative relation
ship between the amount of time their child spends on homework and their achieve
ment, while the students themselves report a positive relationship.
Studies Correlating Time on Homework and Non-Achievement Measures
We found 5 studies that presented correlations between the amount of time
students spent doing homework and student attitudes. Characteristics of these
studies can be found in Table 10. Using a fixed-error model, the unweighted mean
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Does Homework Improve Academic Achievement?
correlation was r = . 12. The weighted mean correlation was r = . 13 (95% C l= . 11/. 14),
which was significantly different from zero. Using a random-effect error model,
the weighted mean correlation was r = .13 (95% Cl = -.01/.26), not significantly
different from zero.
Two studies looked at time on homework and student conduct problems. These
studies are also presented in Table 10. Epstein (1988) fouiwUajiear zero, r = .01,
correlation between elementary-school parent reports of the time their child spent
on homework and their conduct in school. However, Vazsonyi and Pickering (2003)
found a significant negative relationship between how much time high school students
reported spending on homework and their scores on the Normative Deviance Scale.
Further, the relationship held for both Caucasian students, r = .28, and African
American students, r = .24, separately.
Discussion
Summary o f Studies on the Causal Relationship
Between Homework and Achievement
Studies that have attempted to establish a causal link between homework and
academic achievement have done so using several different research designs:
(a) randomly assigning classrooms or students within classrooms to homework and
no-homework conditions; (b) assigning homework to classrooms in a nonrandom
manner but attempting statistical control of rival hypotheses; (c) using naturalistic
measurement to assess both the amount of homework students do and their achieve
ment, but attempting statistical control of rival hypotheses; and (d) testing structural
equation models using naturalistic data.
The studies that randomly assigned classrooms or students within classrooms to
homework and no-homework conditions were all flawed in some way that com
promised their ability to draw strong causal inference. Thus we await studies that
individually permit strong conclusions establishing the productive impact of home
work on achievement. Still, the findings from the three studies that used random
assignment did not differ in their mean effect si^fe from the two studies that used
other techniques to produce equivalent groups.
Further, the findings from manipulated-homework study designs were quite
consistent and encouraging, if not conclusive. They revealed a positive relation
ship between homework and achievement that was robust against conservative
re-analyses, including those using adjusted sample sizes and imputing possible
missing data. The standardized mean difference on unit tests between students who
did and did not do homework varied from d = .39 to d = .97. The weighted mean
d-index was .60 under both fixed and random-error assumptions and was significantly
different from zero when the student was used as the unit of analysis. When we sub
stituted the effective sample size as the unit of analysis by adjusting for within-class
dependency, the weighted mean d-index was .63 and was statistically significant,
up to an assumed intraclass correlation of .35. Further, we could not reject the
hypothesis that all the effect sizes from these studies were testing the same under
lying population value. This was true whether fixed- or random-error assumptions
were used.
Similarly, the range of estimated regression coefficients derived from studies using
multiple regression, path analysis, or structural equation modeling were nearly all
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Cooper et al.
positive and significant. The regression coefficients appeared quite similar across
subject areas. However, as with the studies described above, we would caution against
drawing any conclusions regarding the mediating role of other variables on the
homework-achievement relationship from this rather limited data set. The number
and type of predictors in each model was complex, varied considerably from model
to model, and potentially were confounded with one another Stress studies. Also, the
estimates using naturalistic data and controlling for other variables were calculated
primarily by using high school student samples.
While each set of studies is flawed, in general the studies tend not to share the
same flaws. Across the set, a wide variety of students have provided data, and
the effects of homework have been tested in multiple subject areas. The studies
have controlled for or tested many plausible rival hypotheses in various combinations.
Homework has been embedded within diverse structural models. With only rare
exceptions, the relationship between the amount of homework students do and their
achievement outcomes was found to be positive and statistically significant. There
fore, we think it would not be imprudent, based on the evidence in hand, to conclude
that doing homework causes improved academic achievement. Of course, this
assertion should not inhibit future efforts to establish more firmly this productive
relationship.
The same diversity of research designs that permits optimism regarding a causal
connection also makes the pinpointing of moderators of the homework-achievement
relationship very problematic. Each study differs from other studies on multiple
dimensions, and few studies are contained in each combination of multiple design
features. This makes it difficult, if not impossible, to disentangle moderator effects
by testing for plausible confounds when a moderating variable is found. Therefore,
it seems unwise to use the limited data from these designs to draw inferences about
what variables might be associated with the magnitude of the homework-achievement
relationship. In order to get a first approximation of what these variables might be, we
turn instead to an examination of a larger body of research that simply estimated the
correlation between time spent on homework and achievement, without attempting
to establish a causal direction for the relationship
Summary o f Homework-Achievement Correlations and Moderator Analyses
We found 69 correlations between homework and achievement reported in 32 doc
uments. Fifty correlations were in a positive direction and 19 in a negative direction.
The mean weighted correlation was r = .24 using a fixed-error model, and r = . 16
using a random-error model, and both were significantly different from zero.
Moderator Analyses
It is important to keep in mind two cautions when interpreting the results of mod
erator analyses using correlation coefficients. First, synthesis-generated evidence
should not be misinterpreted as supporting statements about causality (see Cooper,
1998). When groups of effect sizes are compared within a research synthesis, regard
less of whether they come from simple correlational analyses or controlled experi
ments using random assignment, the synthesis can only establish an association
between a moderator variable and the outcomes of studies, not a causal connection.
For example, it might be found that a set of studies reporting a larger-than-average
effect of homework was also conducted at upper-income schools. However, it might
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Does Homework Improve Academic Achievement?
also be the case (known or unknown to the synthesist) that these studies tended to
use unusually long homework assignments. The synthesist cannot discern which
characteristic of the studies, if either, produced the larger effect. Thus, when differ
ent study characteristics are found to be associated with the effects of an intervention
or the size of a correlation, the synthesist should recommend that future research
examine these factors using a more systematically controlled design so that its causal
impact can be appraised.
The second caution relates specifically to moderator analyses that use correlations.
In the current synthesis, we are interested in the causal impact of homework on
achievement. We are not interested in whether achievement also might effect time on
homework (such that, for example, receiving higher grades causes students to work
harder on assignments). However, we know that the size of the correlation between
homework and achievement might reflect the size not only of (a) the homework-
causes-achievement relationship but also of (b) the achievement-causes-homework
relationship and (c) any spurious relationship between the two. Thus, unlike mod
erator analyses that use effect sizes from experiments, moderator analyses that use
correlations must acknowledge the possibility that any uncovered relationships might
be reflecting moderation of any of these three potential influences on the correlation
(or that relationships involving moderators of interest are being suppressed by other
relations captured by the correlation). Again, this suggests that moderator analyses
in research syntheses should be interpreted with caution and used to guide future,
more definitive, research.
Because of a lack of reporting or a lack of variation in some of the moderators
we hoped to test, only four variables were used in quantitative analyses. Two of these,
the type of outcome measure and the subject matter of the homework, revealed that
time on homework was positively associated with both class grades and standard
ized test scores, and with reading-only, math-only, and multiple-subject outcomes.
Under fixed-error assumptions, the association with homework was stronger for
grades than for standardized tests, for math than for reading, and for multiple-subject
outcomes than for reading and math combined. However, neither difference in asso
ciation was significant under random-error assumptions, and in all instances the
difference was quite small, never exceeding a difference between correlations of .06.
Thus, beyond suggesting that the homework-achievement association was robust
across these subsets of data, we would caution against drawing a conclusion that
these moderators were important practical influences on the strength of the relation.
This is especially true for subject areas because many subjects (e.g., language arts,
writing, science, social studies) were not tested frequently enough to be included
in the analysis.
The two other moderator variables, (a) the grade level of the student and (b) whether
the student or parent reported about homework, present a different picture. For grade
level, there was strong evidence that homework and achievement were positively
related for secondary school students. A significant, though small, negative rela
tionship was found for elementary school students, using fixed-error assumptions,
but a nonsignificant positive relationship was found using random-error assumptions.
Moreover, with both error models, the difference between the mean correlations
involving elementary versus secondary students was significant.
For differences among respondents, analyses using both error models suggested
that student reports about homework were significantly positively related to achieve-
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Cooper et al.
ment, while parent reports produced a significant, near-zero correlation using a
fixed-error model. Correlations involving the two types of respondents differed
significantly. Finally, because all parent reports came from parents of elementary
school students, a re-analysis of the grade-level effect was conducted excluding
parent reports. This analysis still showed a higher correlation for secondary than for
elementary school students under fixed-error assumptiomUyit no difference under
random-error assumptions. Not surprisingly, we also found that the correlation
between time spent on homework and achievement was significantly higher when
elementary school students made the report than when parents of elementary school
students made the report.4
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Does Homework Improve Academic Achievement?
be undertaken, we would also recommend that these studies include students from
a variety of grade levels and that grade level be used as a moderating variable.
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T
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Does Homework Improve Academic Achievement?
nature of the assignment and student individual differences. Also, the Lam (1996)
study was limited to 12th-grade Chinese Americans and Caucasian Americans.
Still, this new piece of evidence does, suggest, as common sense would dictate,
that the positive effects of homework are not linear across all amounts. Even for
these oldest students, too much homework may diminish its effectiveness, or even
become counterproductive.
Limitations o f Generalizability
Our analyses looking for publication bias and data censoring revealed litde evi
dence to suggest that the strategies we used to locate studies for the synthesis were
in some way a biased representation of all studies that might exist. That being said, it
is also the case that certain clear limitations to the generalizability of the synthesis
findings need to be noted.
First, as noted above, the positive causal effect of homework on achievement
has been tested and found only on measures of an immediate outcome, the unit test.
Therefore, it is not possible to make claims about homeworks causal effects on
longer-term measures of achievement, such as class grades and standardized tests,
or other achievement-related outcomes. However, the studies using naturally occur
ring measures of time on homework found strong evidence of a link to longer-term
achievement measures. We suspect that this distinction in the types of measures
used in experimental and naturalistic studies of homework will persist. This is
because the large-scale manipulation of homework across multiple subject areas
and long durations within the same samples of studentsthe type of experiment
likely needed to produce homework effects on grades and standardized testswill
require considerable resources and the cooperation of educators and parents willing
to participate.
With regard to subject matter, both studies that introduced homework as an
exogenous intervention and studies that used statistical controls suggest that home
work will have positive effects on achievement involving both quantitative and
verbal material. However, our database contained too few correlations involving
other subjects, such as science and social studies, to include them in the meta
analysis. Therefore, while there is evidence that the effect of subject matter on the
homework-achievement relationship is small, it should be viewed as suggestive
rather than conclusive. :
Finally, a perusal of Tables 3 through 8 suggests that few studies exist examining
the effectiveness of homework in the early elementary school grades. This may be an
especially important omission because of the apparent increase in the amount of
homework being assigned to students in these grades (Hofferth & Sandberg, 2000).
Also, nearly all the literature that we uncovered looked at the effect of homework on
students who might be labeled average, or examined broad samples of students
but did not look for moderating effects of student characteristics.
Future Research
Throughout this discussion, we have pointed to fruitful avenues for future research.
As is often the case, an assessment of what we know places in bold relief what we
dont. Researchers are encouraged to find in our report any of the numerous areas
where research is thin or nonexistent. These areas include studies that introduce
homework as an exogenous intervention, randomly assign students or classrooms to
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Cooper et al.
conditions, and then analyze data at the same unit of analysis as the manipulation.
There are several barriers to implementing such designs. First, of course, are the
barriers to random assignment in applied settings (see Shadish, Cook, & Campbell,
2002, pp. 287-288), not the least of which would be the ethics of withholding from
some students an intervention (homework) with presumed benefits. Second, if
treatments are implemented at the classroom level and analyzed accordingly, the
statistical power to detect effects will be quite low unless large-scale studies can
be mounted that involve numerous classrooms. If students within classrooms are
assigned to conditions, the researcher faces issues of treatment diffusion and/or
demoralization and compensation effects that can contaminate conditions, because
the intervention and control groups interact and know each others experimental
assignment.
Still, given the state of evidence, it seems there is much less to be gained from
carrying out homework studies as usual than from new attempts to pinpoint esti
mates of causal relationships. That being said, we would encourage, as well, the use
of mixed research models that incorporate qualitative analysesto examine the
homework process, moderators, and mediators of its effects, along with its intended
and unintended consequencesin experimental designs. Such studies provide a
rich tableau and complementary sources of knowledge for guiding yet another gen
eration of research, policy, and practice. The long-term and cumulative effects of
homework remain a largely unmapped terrain. Therefore, nonexperimental, longitu
dinal studies that follow cohorts of students and perform fine-grained analyses of
developing homework behaviors would be a new and rich source of information.
In addition, the gaps in our knowledge suggest that future studies, whether exper
imental, qualitative, or longitudinal, should include variations in numerous poten
tial factors in homework effects. Most important, we think these variations should
include:
1. Students in multiple grades, especially the early elementary grades;
2. Students with other varying characteristics, especially varying ability levels,
SES, and sex; ^
3. Variations in the subject matter of homework assignments, including subjects
other than reading and math;
4. Measures of the non-achievement-related effects of homework that have been
proposed in the literature; and
5. Variations in the amount of homework assigned, so that optimum amounts of
homework can be examined.
We might envision all of these design variations being realized within a single
research project, leading to multiple replications, but it is more likely that numerous
small projects will gather data on one or a few areas. Thus we more realistically
call for programs o f research that begin by establishing general principles (some
of which can be gleaned from this synthesis) and then systematically vary fac
tors 1 through 5, above, not in the same study but through a series of interrelated
studies (Shadish et al., 2002). The advantages of this approach are that studies can
be implemented with good control of treatment, thus enhancing their power to
detect effects. And, of course, individual studies will be less expensive to conduct.
A disadvantage of this approach is that it limits the ability to examine interactions
between factors. For example, if the grade level of the student is examined in one
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I
Conclusion
We hope that this report has demonstrated the value of research synthesis for
testing the plausibility of causal relationships even when le^tkan-optim al research
designs and analyses are available in the literature. Most important, we hope that the
findings provide the beginnings of an empirical foundation on which educators can
base homework policies and practices and researchers can build the next generation
of homework research.
Notes
This research was supported by a grant from the U.S. Department of Education, Office
of Educational Research and Improvement (R305T030002), to the first author. The
opinions expressed herein are those of the authors and not necessarily of the Department
of Education. The authors wish to thank Heather Meggers-Wright and Jeffrey C. Valen
tine for their assistance with conducting the research synthesis, and Angela Clinton for
assistance with manuscript preparation. Correspondence regarding this article should
be sent to Harris Cooper (see contact information at end of article).
'We could find no study that looked at the students SES as a moderator of the home
work-achievement link. Only two studies examined the sex of the student as a modera
tor of the homework-achievement link. Among the studies that manipulated homework,
Foyle (1984) presented results of an Analysis of Covariance that included the sex of the
student in interaction with homework condition. The interaction was not significant, and
the cross-break of means was not reported. Among the studies reporting simple correla
tions, Mau and Lynn (2000) reported six comparisons of male and female correlations
between homework and achievement in Grades 10 and 12 for math, reading, and science.
All six comparisons revealed significantly higher correlations for females than for males.
2Looking for missing correlations to the right (increasing the size of the positive effect)
suggested more evidence that correlations higher than those in the retrieved reports might
have been missing from the data set. The fixed-error tnodel suggested that 11 correlations
might be missing and that if they were imputed, fixed graph r = .25 (95% Cl = .251.26).
The random model imputed no additional correlations,. Also, the trim-and-fill analysis was
conducted separately for studies that used class grades or standardized achievement
tests as outcome variables. In all cases, the analysis suggested that the findings reported
in this article were robust with regard to data censoring.
3The Cooper et al. (1998) correlation involving parents had to be dropped from this
analysis because it included both elementary and secondary students.
4This type of subgroup analysis is a way of disentangling the effects of confounded
moderating variables. It is an example of the type of analysis that would have been ben
eficial to carry out as well on the studies that employed exogenous introductions of
homework. However, the limited number of such studies meant that some combinations
of categories within moderators would have few or no studies in them.
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Authors
HARRIS COOPER is a Professor of Psychology and Director of the Program in Education,
Box 90739, Duke University, Durham, NC 27708-0739; e-mail cooperh@duke.edu. His
research interests include how academic activities outside the school day (such as home
work, after school programs, and summer school) affect the achievement of children and
adolescents; he also studies techniques for improving research synthesis methodology
and how to make research synthesis more useful for practitioners and policymakers.
JORGIANNE CIVEY ROBINSON is a PhD candidate in Social Psychology in the Department
of Psychology, Box 90085, Duke University, Durham, NC 27708-0085; e-mail jorgianne.
robinson@duke.edu. Her research interests include homework and the impact of variations
in assignments on homework effectiveness.
ERIKA A. PATALL is a graduate student in Social Psychology in the Department of Psy
chology, Box 90085, Duke University, Durham, NC 27708-0085; e-mail erika.patall@
duke.edu. She is also a fellow in the Spencer Foundation Interdisciplinary Program in
Education Science. Her research interests include parent involvement in homework and
the role of choice in intrinsic motivation.
62
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