Factors Behind College Cheating: Immaturity, Lack of Commitment and Neutralizing Attitudes

College Cheating: Immaturity, Lack of Commitment, and the
Neutralizing Attitude
Author(s): Valerie J. Haines, George M. Diekhoff, Emily E.
LaBeff and Robert E. Clark
Source: Research in Higher Education, Vol. 25, No. 4 (1986),
pp. 342-354
Published by: Springer
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COLLEGE CHEATING:
Immaturity, Lack of Commitment,
and the Neutralizing Attitude
Valerie J. Haines, George M. Diekhoff, Emily E. LaBeff,
and Robert E. Clark
Through the use of a 49-item questionnaire administered to 380
university students, we
investigated student cheating on exams, quizzes, and homework
assignments. More
than half the students reported cheating during the academic
year on at least one of the
above. The purpose of this paper was to uncover fundamental
factors underlying cheat-
ing behavior. Through the use of correlational and factor
analysis, three primary factors
were identified: student immaturity, lack of commitment to
academics, and neutraliza-
tion. We offer interpretations of these factors and suggestions
for testing these and other
factors in future research.
Student dishonesty on college campuses throughout the nation
has been

widely recognized as epidemic ("Cheating in College," 1976;
Wellborn,
1980). Although cheating has been noted by faculty and
students alike, its
occurrence does not appear to be on the decline. In fact, there
seems to be
general agreement that cheating is endemic to education in the
secondary
schools as well as at the college level. Methods of cheating
often provide a
study in creativity ranging from the sophisticated distribution of
term
papers through so-called paper mills, to devising ways of
carrying informa-
tion into the classroom, to the not-so-sophisticated means of
looking at
someone else's paper during an exam. Since it is unlikely that
those asso-
ciated with academia for any length of time would deny the
presence of
student cheating, it is important to search for processes that
underlie this
behavior.
Correspondence to: Emily E. LaBeff, Division of Social and
Behavioral Sciences, Midwest-
ern State University, Wichita Falls, Texas 76308. Valerie J.
Haines, George M. Diekhoff, and
Robert E. Clark, Midwestern State University.
Research in Higher Education © 1986 Agathon Press, Inc. Vol.
25, No. 4
342

2014 20:05:43 PM
COLLEGE CHEATING 343
Research into college student cheating has been diverse. Based
on the
premise that a majority of educators would like to identify those
likely to
cheat, numerous studies have attempted to discern those
characteristics and
circumstances which "predispose" some students to engage in
this activity.
Some important determinants that have been examined include
the student's
sex, age, previous academic performance, class standing,
academic major,
fraternity-sorority membership, extracurricular involvement, as
well as the
student's level of test anxiety. Although some significant
correlations be-
tween these variables and cheating have been reported, each has
been found
to rely on circumstances that vary from situation to situation.
These moder-
ating factors include the arrangement of seating during exams,
as well as the
importance and difficulty of the exam (Baird, 1980; Barnett and
Dalton,
1981; Bronzaft et al., 1973; Fakouri, 1972; Harp and Taietz,
1966; Johnson
and Gormly, 1972; Leming, 1980; Newhouse, 1982; Singhal,
1982; Stannord

and Bowers, 1970). In addition to various demographic
variables, Eve and
Bromley (1981) reported cultural conflict and internal social
control to have
significant predictive ability with regard to college cheating.
Students who
were found to have high levels of cultural conflict were most
likely to cheat
on exams; those who demonstrated high levels of internalized
social control
cheated less.
Attention has also been directed toward the impact of
administrative
attitudes upon the occurrence of cheating on campus. According
to one
study (Singhal, 1982), most divisions within colleges vare not
paying enough
attention to the incidence of cheating, and when cheating is
detected, they
do not possess skills adequate to deal with the problehi.
Bonjean and
McGee's (1965) comparison of the honor system versus the
proctor system
revealed the former to be more effective in controlling cheating.
According
to their findings, students in the honor system were more likely
to possess a
clear understanding of the rules regarding class dishonesty than
were those
students in classes where the proctor system was used. Such
findings provide
possible explanations for the higher rate of honest behavior.
In contrast, further study of the effects of social control by
Tittle and

Rowe (1973) demonstrated that moral appeal had little or no
impact on
cheating while the delivery of a sanctioned threat resulted in a
significant
decrease in cheating activity. According to the authors, "fear of
a sanction is
a more important influence than moral appeal in generating
conformity to
the norm of classroom honesty" (Tittle and Rowe, 1973, p. 492).
In their
final analysis of the data, the authors noted that those students
with the
lowest grades were least affected by threat of sanction. Such
findings fit well
within the framework of general deterrence theory according to
which the
greater the utility of an act, the greater the severity of
punishment required
for deterrence.
2014 20:05:43 PM
344 HAINES ET AL.
Focusing on the identification of conditions under which select
causal
structures can influence cheating behavior, Liska (1978) found
neutraliza-
tion to be an important factor in college cheating.
Neutralization, first
defined by Sykes and Matza (1957), is similar to rationalization

which can be
used before, during, or after deviant behavior to deflect the
disapproval of
others and self. Liska employed various combinations of social
processes
(i.e., socialization, interpersonal social control, and social
selection) com-
bined with psychological processes (attitude impact on
behavior) and found
the concept of neutralization to be strongest in the absence of
social control
accentuations.
The present study was conducted with the following objectives
in mind:
(1) to describe the incidence of college cheating and further
document its
existence; (2) to examine the occurrence of cheating from
within the frame-
work of Sykes and Matza's (1957) neutralization theory; (3) to
identify
demographic as well as personal characteristics of students who
cheat; and
(4) to search for the fundamental factors underlying cheating
behavior. This
latter goal is the primary focus of this report.
METHODOLOGY
Data were gathered through the completion of a 49-item
questionnaire
administered during the spring of 1984 to 380 undergraduate
students at a
small state university in the Southwest. The student population
(N= 4,950)
was unevenly distributed throughout the university's programs,

with a dis-
proportionate number majoring in business administration.
While our pri-
mary concern was to use data collection techniques that would
maximize the
return rate, we also sought to secure a relatively representative
sample in
terms of major areas of study. Therefore, our questionnaire was
adminis-
tered only to those students enrolled in courses classified as
part of the
university's required core curriculum. At the time of the study, a
cursory
examination of enrollment sheets of the classes used, which
noted each
student's major, supported this strategy. However, subsequent
analyses indi-
cated that in our sample, freshmen and sophomores were
overrepresented
(84% of the sample versus 60% of the university population).
Females were
also slightly overrepresented (62% of the sample versus 55% of
the univer-
sity population).
There were obvious disadvantages associated with the use of
self-adminis-
tered questionnaires for data-gathering purposes. We were
forced to accept
student responses without the benefit of contest. In order to
maximize the
return rate, the questionnaire was administered during regularly
scheduled
class periods in which permission of the instructor had been
secured. Par-
ticipation was on a voluntary basis. In order to promote honesty

of re-
2014 20:05:43 PM
TABLE 1. Prevalence of Cheating
Type of Cheating Yes No
Cheated on major exams 23.7% (90) 76.3% (290)
Cheated on daily/weekly quizzes 22. 1 % (84) 77.9% (296)
Cheated on assignments 34.2% (130) 65.8% (250)
Overall cheating measure (on ex-
ams, quizzes, or assignments) 54.1% (206) 45.9% (174)
sponses, students were encouraged to be as open as possible
with a guaran-
tee of complete anonymity. They were instructed to limit their
responses
regarding whether or not they had cheated to that academic
year. This
included the entire fall semester of 1983 and half of the spring
semester of
1984.
The questionnaire required approximately 30 minutes to
complete and
forced-choice response categories were employed through most
of the instru-

ment. The questionnaire also contained items concerning
demographic
characteristics, the incidence of cheating in three forms (on
major exams,
quizzes, and class assignments), perceptions of and attitudes
toward cheat-
ing by other students, the effectiveness of several alternative
deterrents to
cheating, and an 11 -item neutralization scale.
Four pilot studies involving approximately 100 students were
conducted
during the initial planning stages of the project. Several
problem areas were
noted at that time, and appropriate changes were made in the
questionnaire.
RESULTS
Extent of Cheating
As mentioned, three measures of cheating behavior were used in
the
instrument: cheating on major exams, on quizzes, and on class
assignments.
Table 1 shows the prevalence of cheating by each measure as
well as the
overall cheating score which involved cheating in any of the
three forms.
Slightly less than one-fourth of the students reported cheating
on major
exams or quizzes, whereas just over one-third reported cheating
on class
assignments. Nevertheless, when counting the total number of
students who
admitted cheating in any form, more than one-half (54.1%) of

the students
had cheated. This overall cheating measure was used in all
subsequent analy-
ses. It should be noted that this percentage is quite similar to
the results
obtained in other recent surveys of college cheating (Baird,
1980; Liska,
1978; Singhal, 1982). Also, in our study, only 1.3% of the
students reported
having ever been caught cheating.
2014 20:05:43 PM
346 HAINES ET AL.
Cheating and Neutralization
In order to more fully understand the attitudinal processes
involved in
student cheating, we turned to the concept of neutralization of
deviance first
presented by Sykes and Matza in their important 1957 essay.
We wanted to
know whether or not neutralization was associated with cheating
behavior
and if students were, in essence, justifying their cheating
behavior so as to
provide protection "from self blame and the blame of others"
(Sykes and
Matza, 1957, p. 666).

Sykes and Matza discussed five specific types of neutralization:
denial of
responsibility, denial of the victim, denial of injury,
condemnation of the
condemners, and appeal to higher loyalties. In each case, the
individual
professes to support a particular societal norm or law but also
recognizes
special circumstances which allow or even require the
individual to violate
the norm or law. This neutralization process is presumed to free
the individ-
ual to deviate without considering himself or herself a deviant,
thus elimi-
nating or reducing the sense of guilt or wrongdoing. Each of
these five types
of neutralization were represented in 11 hypothetical situations
adapted
from Ball (1966). Responses of our sample to the items
provided an indica-
tion of the students' tendency to neutralize. The 11 hypothetical
statements
and student's Likert-type responses to each are summarized in
Table 2 for
cheaters and noncheaters.
An evaluation of the psychometric qualities of the
neutralization scale
showed very high internal consistency with all items showing
item-total
correlations greater than .64. The average inter-item correlation
was .54.
Split-half reliability, as measured by Cronbach's alpha, proved
to be very
high (a= .93). Shortening the scale by eliminating any of the
items would

have reduced the reliability of the scale. Consequently, full-
scale scores were
used as our measure of neutralization.
As shown in Table 2, cheaters showed higher levels of
neutralization (i.e.,
lower scores) on all 11 items of the neutralization scale. Total
neutralization
scores differed significantly between the two groups as well (/ =
6.90,
df= 377, /?< .001). Given the importance of neutralization
among cheaters,
we further examined our data in ways designed to clarify the
processes
associated with neutralization and cheating. Correlations
between neutral-
ization scores and student's ratings of the effectiveness of
various deterrents
to cheating were examined and found to be low, but statistically
significant,
and present a compelling pattern. As can be seen from Table 3,
those who
show high neutralization (i.e., low neutralization scores) are
most deterred
by the formal, institutional consequences of being caught
cheating (i.e.,
threat of receiving an F, being dropped from the course, or fear
of university
reprisal). They are least deterred by guilt over cheating or
disapproval of
2014 20:05:43 PM

TABLE 2. Techniques of Neutralization: Cheaters vs.
Noncheaters
Cheaters Noncheaters
Neutralizing Statements Mean SD Mean SD
1. The course material is too hard. No matter
how much he studies, he cannot under-
stand the material. 3.08 .62 3.44 .67
2. He is in danger of losing his scholarship
due to low grades. 3.09 .67 3.42 .68
3. He doesn't have time to study because he is
working to pay for school. 3.04 .66 3.36 .67
4. The instructor doesn't seem to care if he
learns the material. 2.74 .79 3.17 .76
5. The instructor acts like his/her course is
the only one he is taking. Too much mate-
rial is assigned. 2.68 .75 3.16 .74
6. His cheating isn't hurting anyone. 3.23 .65 3.47 .61
7. Everyone else in the room seems to be
cheating. 2.96 .77 3.32 .75
8. The people sitting around him made no
attempt to cover their papers and he could
see the answers. 3.13 .64 3.39 .66

9. His friend asked him to help him/her cheat
and Jack couldn't say no. 3.01 .70 3.45 .66
10. The instructor left the room to talk to
someone during the test. 2.97 .74 3.41 .69
11. The course is required for his degree, but
the information seems useless. He is only
interested in the grade. 2.98 .72 3.37 .69
Total Neutralization Scores 32.90 5.41 36.95 6.01
(t = 6.90, df= 377, /?<. 001)
friends, this guilt having been handled by neutralization. In
short, neutral-
izers seem to function at a relatively low level of moral
development
(Kohlberg, 1964), being concerned primarily with punishment
and the reac-
tions of authority figures.
Demographic Characteristics and Cheating
A comparison of the demographic makeup of cheaters and
noncheaters
2014 20:05:43 PM
348 HAINES ET AL.

TABLE 3. Correlations between Neutralization
Scores and Cheating Deterrents
Deterrents Correlations
Family a- =.02
a? = 380
/7= .38
Friends r=.15
n = 38O
p=.OO2
Guilt a- =.25
/7 = 38O
p=.00
Embarrassment a* =.03
a* = 380
p=.30
F for cheating r=.14
aj = 38O
p=.002
Instructor drop r=.13
a? = 380
p=.005
Fear of university a* =.13
a? = 380
p=.005
(see Table 4) showed that cheaters tended to be younger, to be
single, to have

lower grade-point averages, to be receiving financial support
from parents,
and to be more involved in extracurricular activities such as
intramural or
varsity sports and fraternities and sororities. If they worked at
all, it was
generally on a part-time basis.
Surprisingly, and in contrast to other recent research (Baird,
1980; Fa-
kouri, 1972; Johnson and Gormly, 1972), no significant
differences between
cheaters and noncheaters were found in relation to either sex or
academic
classification (i.e., year in school). It is possible, however, that
our sample
differed from those studied previously in that ours was heavily
weighted
with freshmen, sophomores, and females.
Age showed the most substantial correlation with cheating in
that the
younger students were more likely to report cheating in any of
the three
forms. It might be that age has become more significant today
as more
nontraditional students are returning to college. Following age,
involvement
in intramural sports, lower GPA, and being single showed the
strongest
correlations with cheating. The correlations for the other
variables, such as
source of financial support and varsity sport involvement, were

not substan-
tial, but they were statistically significant.
2014 20:05:43 PM
TABLE 4. Correlations between Demographic Characteristics
and Cheating
Cheaters Noncheaters
Variables Correlations (scored 1) (scored 0)
Age r=A0 M=20.3 M=25.6
/?<.001 (a? = 205) (/i =174)
Marital status r= - .33
p<.00
Single (scored 0) 88.8% 60.9%
(n =182) (a? =106)
Married (scored 1) 11.2% 39.1%
(a? = 23) (a? = 68)
Grade-point average r=-.23 A/=2.54 M=2.84
/7<.001 (a? =179) (/f = 135)
Source of financial
support a* =.17

/7<.OO5
Parents (scored 1) 37.6% 22.2%
(aj = 73) (aj = 34)
Other source
(scored 0) 62.4%
(/i =121) 77.8%
(« =119)
Varsity sports a* =.12
/?<.005
Involved (scored 1) 6.3% 1.1%
(a? =13) (n = 2)
Not involved
(scored 0) 93.7% 98.9%
(/? =192) (/i= 172)
Intramural sports r= .27
/7<.001
(a? = 55) (a? =10)
Not involved 73.2% 94.3%
(scored 0) (a? =150) (a? =164)
Fraternity/Sorority r = . 1 7
/7<.OO5
(a? = 40) (w =13)

Not involved 80.5% 92.5%
(scored 0) (a? =165) (a? =160)
Employment status r= - .22
/7<.001
Less than full-time 82.0% 62. 1 %
(scored 0) (a? =168) (a? =108)
Full-time 18.0% 37.9%
(scored 0) (a? = 37) (n = 66)
2014 20:05:43 PM
350 HAINESETAL.
TABLE 5. Stepwise Discriminant Analysis Comparing Cheaters
vs. Noncheaters
Significance
Variable Total Overall of Added
Step Entered % Variance Significance Predictor
1 Age 15.9 F(l,203) = 38.46
p<.00
2 Neutralization 22.1 F(2,202) = 29.79 F(l,376) = 29.93
p<.001 p<.0
3 Notice others cheating 25.4 F(3,201) = 24.47 F(l,375)= 16.59

p<.001 /?<.01
When considered together, these variables can be used as rough
indicators
of the maturity and commitment to academics on the part of the
students.
Tentatively, we can say that students who cheat tend to be
immature and to
show a lower level of commitment to academics in that their
GPAs are lower.
Additionally, they are more likely to be involved in nonwork,
extracurricular
activities.
An Overall Comparison of Cheaters and Noncheaters
A stepwise discriminant analysis (summarized in Table 5) was
used to
clarify the nature of the differences between cheaters and
noncheaters. Age
was selected on the first step. At step two, scores on the
neutralization scale
were entered and added significantly to the discrimination of
cheaters and
noncheaters (F(l,376) = 29.93, p<.0). The fact that
neutralization was se-
lected prior to any of the other demographic variables (except
age) suggests
that although cheating does occur more frequently in some
demographic
groups than in others (as identified earlier), it is primarily
because those
demographic groups are more likely to neutralize their cheating
behavior.
Only age is as reliably and consistently related to cheating as is
the neutraliz-

ing attitude. Neutralization, it seems, is fundamental to cheating
and can
best be characterized as a common denominator for cheaters.
Although additional discriminating variables added little to
discriminat-
ing power, one variable, added at the third step of the
discriminant analysis,
is worth noting. At step three, the variable addressing the
degree to which
respondents noticed other students cheating was entered and
added a small,
but statistically significant margin of additional discrimination.
This vari-
able consisted of a Likert-type item, scored 1 to 5, on which
cheaters indi-
cated noticing more cheating (M=2.71, SD=.$$) than did
noncheaters
(M= 2.14, SD= .75). Singly, this variable showed a correlation
with cheating
of -.33.
The finding that cheaters see more cheating by others than do
noncheaters
is not surprising. Part of the neutralizing attitude displayed by
cheaters
toward their cheating behavior involves just this kind of
justification:
2014 20:05:43 PM

TABLE 6. Principal Components Analysis Summary Table:
Varimax Rotated Factor
Loadings"
Variables FI FII Fill
Age .72
Grade-point average .67
Neutralization .69
Marital status .74
Employment status .41 - .42
Fraternity/sorority .66
Notice others cheating .48
Varsity sports .51
Intramural sports .71
Parental financial support - .75
Eigenvalues 2.83 1.12 1.07
Percentage of variance 28.3 11.2 10.7
"Only loadings of .4 or greater are shown.
"Those around me are cheating, therefore it is fair for me to
cheat in order to
compete effectively." Of course, in order to use this argument to
justify their
cheating behavior, cheaters may very well tend to perceive
higher levels of
cheating, either inaccurately, as a result of their projecting their
own motives
and actions onto others, or accurately, as a result of being
sensitized and
attuned to cheating behavior.

Factor Analysis of Variables Related to Cheating
The pattern of results presented thus far has led to the tentative
conclu-
sion that a limited number of fundamental factors underlie
cheating behav-
ior: immaturity, lack of commitment to academics, and a
neutralizing atti-
tude toward cheating. This conclusion was put to the test by
factor-analyzing
those variables found to be related to cheating behavior: age,
grade-point
average, neutralization scale scores, marital status (married vs.
single), em-
ployment status (full-time vs. less than full-time employment),
membership
in a fraternity or sorority, degree to which other students are
noticed cheat-
ing, involvement in varsity sports, involvement in intramural
sports, and
whether or not students were dependent upon parental financial
support.
The results of this factor analysis (a principal components
analysis with
varimax rotation) are summarized in Table 6. Three factors with
eigenvalues
of 1.0 or greater were extracted, accounting for 50.4% of the
total variance.
Factor I, accounting for 28.3% of the variance, was most
strongly repre-
sented by age, marital status, students' dependence upon
parental financial

2014 20:05:43 PM
352 HAINESETAL
support, and employment status. Students showing high scores
on Factor I
were older, married, not dependent upon parents, and were
employed full-
time. Factor I was thus interpreted as reflecting maturity.
Factor II, accounting for 11.2% of the variance, was most
strongly repre-
sented by involvement in intramural sports, membership in a
fraternity or
sorority, involvement in varsity sports, and employment status.
Those indi-
viduals scoring high on Factor II were heavily involved in
nonwork extra-
curricular (i.e., "play") activities that might distract from
attention to aca-
demics, e.g., sports and fraternities and sororities. Accordingly,
Factor II
was interpreted as reflecting students' level of commitment to
academics.
Factor III, accounting for 10.7% of the variance, was
represented most
strongly by neutralization scale scores, grade-point average, and
the degree
to which other students were perceived as cheating. Students
showing high
scores on Factor III tended not to neutralize (or cheat) because

their grades
were higher. Factor III was interpreted as mostly involving the
neutralizing
attitude.
DISCUSSION AND CONCLUSIONS
The primary purpose of this study was to identify basic factors
underlying
cheating in college. Given previous diverse research on
cheating, it was
important to look for fundamental forces in cheating as an end
in itself.
Three underlying factors were discovered: immaturity, lack of
commitment
to academics, and the neutralizing attitude.
Given that the cheater tends to be younger, single, and either
unemployed
or employed only part-time, and to be more involved in outside
("play")
activities, it can be suggested that he or she is more immature
than the
noncheater. This conclusion was also reflected by the cheater's
low level of
moral development exhibited by a refusal to be deterred from
cheating by
anything other than the forces of formal social control.
A second factor related to cheating is the cheater's lack of
investment in
his or her education. The students in this study who admitted
cheating were
less likely to have paid for their own tuition and books than
were non-
cheaters. Reliance on parents for financial support may lead

cheaters to
place less value on the formal aspects of an education than do
their counter-
parts who have made a greater personal financial investment.
It can be suggested that this factor plays a role in students'
perceived need
to cheat. Given cheaters' high level of participation in
extracurricular activi-
ties, it may be that they do not allow enough time to study and
perhaps give
studying a low priority. Also related to this factor is the
cheater's generally
lower GPA. Cheaters may feel more pressure to cheat in order
to maintain
adequate grades.
2014 20:05:43 PM
The third factor found to be related to cheating was
neutralization. Atten-
tion was focused on the application of Sykes and Matza's (1957)
techniques
of neutralization to cheating activities. The use of such
techniques conveys
the message that students recognize and accept cheating as an
undesirable
behavior; however, its occurrence can be excused in certain
instances. This

approach enables those who cheat to do so with a clear
conscience. The
evidence suggests that under certain circumstances, cheaters
neutralize so
effectively that they really do not think cheating is wrong,
either for them-
selves or for others.
Given the continuing presence of cheating in the university
setting, it is
necessary to further test the salience of these three factors in
more diverse
university environments. Since our sample was limited to a
small state uni-
versity, it is important to examine factors in cheating in a wide
range of
institutions including prestigious private colleges, large state
universities,
and religious schools. Additionally, cross-cultural studies of
cheating might
prove especially useful in identifying broader societal forces
underlying
cheating behavior.
It is important to address broader research questions suggested
by our
study. For example, factors at the college level that can increase
the maturity
of the students might be investigated. What kind of environment
can in-
crease the maturity of students? Factors contributing to lack of
commitment
to academics and perhaps to student alienation from the learning
process
should be examined. What social forces contribute to lack of
commit-

ment? Moreover, the processes in learning neutralizing attitudes
should be
studied and integrated with the variety of work in the study of
deviance.
How do students learn to neutralize and what would deter it?
We consider
these questions to be of considerable importance to institutions
of higher
education.
REFERENCES
Baird, J. S. (1980). Current trends in college cheating.
Psychology in the Schools
17: 512-522.
Ball, R. (1966). An empirical exploration of neutralization.
Criminologica 4: 22-23.
Barnett, D. C, and Dalton, J. C. (1981). Why college students
cheat. Journal of
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http://www.jstor.org/page/info/about/policies/terms.jspArticle
Contentsp. 342p. 343p. 344p. 345p. 346p. 347p. 348p. 349p.
350p. 351p. 352p. 353p. 354Issue Table of ContentsResearch in
Higher Education, Vol. 25, No. 4 (1986), pp. 307-396Volume
InformationFront MatterTenure, Retirement, and the Year 2000:
The Issues of Flexibility and Dollars [pp. 307-315]Preferred
Directions and Images for the Community College: A View
from Inside [pp. 316-327]Characteristics of Graduate Students
in Biglan Areas of Study [pp. 328-341]College Cheating:
Immaturity, Lack of Commitment, and the Neutralizing Attitude
[pp. 342-354]Supply and Demand of Doctorates in Economics
[pp. 355-364]Using Discriminant Analysis to Predict Faculty
Rank [pp. 365-376]Work and Life Away from Work: Predictors
of Faculty Satisfaction [pp. 377-394]Back Matter
Contemporary Educational Psychology 30 (2005) 96–116
www.elsevier.com/locate/cedpsych
Why study time does not predict grade
point average across college
students: Implications of deliberate practice
for academic performance
E. Ashby Plant*, K. Anders Ericsson, Len Hill, Kia Asberg
Department of Psychology, Florida State University,
Tallahassee, FL 32306-1270, USA
Available online 14 August 2004
Abstract

The current work draws upon the theoretical framework of
deliberate practice in order to
clarify why the amount of study by college students is a poor
predictor of academic perfor-
mance. A model was proposed where performance in college,
both cumulatively and for a cur-
rent semester, was jointly determined by previous knowledge
and skills as well as factors
indicating quality (e.g., study environment) and quantity of
study. The findings support the
proposed model and indicate that the amount of study only
emerged as a significant predictor
of cumulative GPA when the quality of study and previously
attained performance were taken
into consideration. The findings are discussed in terms of the
insights provided by applying the
framework of deliberate practice to academic performance in a
university setting.
� 2004 Elsevier Inc. All rights reserved.
Keywords: Grade point average; Study time; Academic
performance; Deliberate practice; Study habits
0361-476X/$ - see front matter � 2004 Elsevier Inc. All rights
reserved.
doi:10.1016/j.cedpsych.2004.06.001
*

Corresponding author. Fax: 1-850-644-7739.
E-mail address: [email protected] (E.A. Plant).
mailto:[email protected]
E.A. Plant et al. / Contemporary Educational Psychology 30
(2005) 96–116 97
1. Introduction
The total amount of time that students report studying has often
been examined
as a potential predictor of success in school. It might seem that
the more time that
students spend studying, the better grades they should receive.
Although students
should increase their personal knowledge and skills by
increasing the amount of time
that they spend on relevant study activities, the relationship
between the amount of
study and achievement across students is less clear. Indeed
researchers have consis-
tently found a weak or unreliable relationship between the
weekly amount of re-
ported study time and grade point average (GPA) for college
students (Allen,
Lerner, & Hinrichsen, 1972; Beer & Beer, 1992; Gortner
Lahmers & Zulauf, 2000;

Hinrichsen, 1972; Michaels & Miethe, 1989; Schuman, Walsh,
Olson, & Etheridge,
1985; Wagstaff & Mahmoudi, 1976).
1
The most extensive study conducted on the issue, by Schuman
et al. (1985) pro-
vides compelling evidence that ‘‘there is at best only a very
small relationship be-
tween amount of studying and grades’’ (p. 945). In one of their
studies, they
found a weak, yet reliable relationship between reported study
time and grades in
the corresponding semester, but this relationship disappeared
when students� SAT
scores were statistically controlled. Schuman et al. (1985)
argued that grades in col-
lege are primarily determined by aptitude measures, such as
SAT, and attendance at
lectures and classes.
Subsequent investigators largely accepted the findings of
Schuman et al. (1985)
but questioned the generalizability of the findings across
educational contexts (Mi-
chaels & Miethe, 1989) and student populations (Rau & Durand,
2000). In their

study, Michaels and Miethe (1989) found a small (r = .18, p <
.01) relationship be-
tween reported study and GPA, which remained after
controlling for a number of
background variables, such as high school rank, attendance, and
reported study hab-
its. They also found that studying ‘‘without listening to radio
and television (no
noise)’’ predicted higher GPA. Rau and Durand (2000) argued
that Schuman
et al.�s (1985) findings were the result of their sample of
undergraduates from the
University of Michigan, which they posited are not
representative of students in
most large state universities. For example, they found that the
students at University
of Michigan reported studying an average of 25h/week, whereas
Illinois State Uni-
versity (ISU) students reported only 8h/week (but see Schuman,
2001). Although
Rau and Durand (2000) found that the amount of study was
reliably related to
GPA (r = .23, p < .001) for an ISU sample, the real benefits
were only seen for stu-
dents studying over 14h/week (about 25% of the ISU students).

Rau and Durand
(2000) devised a variable of ‘‘academic ethic’’ to identify
students who were commit-
ted to studying, which also predicted GPA, after controlling for
high-school grades
and scholastic aptitude (ACT) scores.
1
For the current literature review, we chose to focus on research
that used official records of GPA as
opposed to self-reported GPA and that used samples of regular
college students and not pre-selected
special populations.
98 E.A. Plant et al. / Contemporary Educational Psychology 30
(2005) 96–116
1.1. Deliberate practice and performance
In trying to understand the small or unreliable relationship
between study time
and GPA, it may be helpful to consider the emerging literature
on deliberate prac-
tice. Research into deliberate practice indicates that the amount
of high quality prac-
tice accumulated during individuals� careers is closely related
to their attained
performance in a wide range of domains (e.g., Ericsson, 2002;
Ericsson & Lehmann,

1996). Studies of the acquisition of expert performance have
shown that extensive
experience is necessary for individuals to attain high levels of
reproducibly superior
performance in the domain of expertise (Ericsson & Lehmann,
1996; Simon &
Chase, 1973). However, all experiences are not equally helpful
and there are qualita-
tive differences between activities loosely referred to as
‘‘practice’’ in their ability to
improve performance.
There are clear limits on the benefits of experience. For
example, many people
know recreational golf and tennis players whose performance
has not improved in
spite of 20–30 years of active participation. The mere act of
regularly engaging
in an activity for years and even decades does not appear to lead
to improvements
in performance, once an acceptable level of performance has
been attained (Ericsson,
2002). For example, if someone misses a backhand volley
during a tennis game, there
may be a long time before the same person gets another chance
at that same type of

shot. When the chance finally comes, they are not prepared and
are likely to miss a
similar shot again. In contrast, a tennis coach can give tennis
players repeated oppor-
tunities to hit backhand volleys that are progressively more
challenging and eventu-
ally integrated into representative match play. However, unlike
recreational play,
such deliberate practice requires high levels of concentration
with few outside dis-
tractions and is not typically spontaneous but carefully
scheduled (Ericsson, 1996,
2002). A tennis player who takes advantage of this instruction
and then engages
in particular practice activities recommended by the teacher for
a couple of hours
in deeply focused manner (deliberate practice), may improve
specific aspects of his
or her game more than he or she otherwise might experience
after many years of rec-
reational play.
Ericsson, Krampe, and Tesch-Römer (1993) proposed that the
acquisition of ex-
pert performance was primarily the result of the cumulative
effect of engagement in

deliberate-practice activities where the explicit goal is to
improve particular aspects
of performance. These activities are typically designed by a
teacher or by the elite
performers themselves when they have reached a sufficiently
high level of mastery.
The specific goals of deliberate practice and the detailed nature
of training activities
will differ for a given person from practice session to practice
session as it will from
one person to another in a given domain and particularly across
domains. However,
the general goal of all forms of deliberate practice involves
improving some aspect of
performance in an effective manner and, thus, deliberate
practice has a number of
pre-requisites, including the capacity to sustain full
concentration, a distraction-free
environment, and access to necessary training resources. Hence
to engage in deliber-
ate practice the aspiring elite performers often need to travel to
a training facility and
to schedule the practice activity to assure the ability to sustain
concentration during

(2005) 96–116 99
the daily practice activity (Ericsson, 1996, 2002, 2003a).
Ericsson et al. (1993) and
Ericsson (1996, 2002, 2003a) demonstrated that the attained
level of an individual�s
performance is closely related to the reported amount of
deliberate practice, primar-
ily solitary practice focused on improvement, that he or she has
accumulated since
the introduction to a domain, such as chess (Charness, Krampe,
& Mayr, 1996),
sports (Ericsson, 2001, 2003a, 2003b; Helsen, Starkes, &
Hodges, 1998; Starkes, Dea-
kin, Allard, Hodges, & Hayes, 1996), and music (Ericsson et al.,
1993; Krampe &
Ericsson, 1996; Lehmann & Ericsson, 1996; Sloboda, 1996).
In studies of college education, similar evidence has been
accumulated for differ-
ential effectiveness of various learning activities. Inspired by
Craik and Tulving�s
(1975) classic work on depth of processing, Schmeck and Grove
(1979) found that
college students with above average GPAs differed from
students with below average

grades in their reports of cognitive processes mediating their
learning. The students
with higher GPAs were found to endorse more inventory items
about elaborative
encoding and deep analysis and synthesis, but were not found to
differ in their
endorsement of traditional study and learning methods from the
students with lower
GPAs. In fact, they found that students� endorsement of
traditional study was neg-
atively related to their academic assessment tests (ACT). More
recent research on
effective learning (for reviews see Pintrich, 2000; Puustininen
& Pulkkinen, 2001;
Zimmerman, 2000) has explored successful students� reports of
the regulation of
learning activities and the study environment within educational
settings. For exam-
ple, Zimmerman and Bandura (1994) showed that self-efficacy
(as rated by college
students) and grade expectations predicted grades in a writing
class. VanderStoep,
Pintrich, and Fagerlin (1996) found that college students with
low, medium, and high
course grades differed in their reported learning characteristics
for social and natural
science but not humanities courses. Specifically, VanderStoep

et al. (1996) showed
that high achievers in social and natural science had more
domain-specific knowl-
edge, more adaptive motivational beliefs, and better self-
regulation. More recently
Zimmerman (1998, 2002) has developed a general framework
for self-regulation in
studying. He demonstrated close parallels between effective
activities in studying in
academic settings and self-regulated practice in the
development of expert perfor-
mance in many domains of expertise (Ericsson, 1996, 2002,
2003a, 2003b).
The current paper seeks to identify observable indicators of
effective learning
activities in the complex domain of academic performance in a
university setting
by extending the theoretical frameworks of deliberate practice
and self-regulated
learning. We propose that distinctions between deliberate
practice and other types
of practice can be applied to studying and that this distinction
can, at least in part,
explain why measures combining all types of study activities in
the school system are

not valid predictors of grades. Furthermore, we propose a few
observable indicators
that would reveal active efforts by some of the students to plan
study activities in
environments that are conducive to deliberate practice and self-
regulated study activ-
ities in college. Of particular interest are learning activities
reflecting deliberate and
self-regulated practice that are related to increased performance
(GPA). However,
in addition to factors that are hypothesized to promote the
quality of study, there
are numerous other factors in the college environment that also
influence GPA
(2005) 96–116
and performance across a wide range of academic subjects (e.g.,
prior knowledge of
subject, skills, and cognitive abilities). Therefore, our approach
focuses on measuring
a wide range of factors important for academic performance, so
that we can statis-
tically control for these factors and eventually estimate the
relationship between

study time and academic performance.
1.2. Toward a model of factors that determine grades during a
semester in college
Common measures of performance in college are the cumulated
GPA or the GPA
for a given semester. These measures are averages of course
grades, which are likely
determined by two types of factors. The first type can be
measured prior to the start
of a targeted semester, such as the knowledge, abilities, and
skills that had been ac-
quired prior to the start of the semester. The second group of
factors consists of the
concurrent study and the learning and non-learning activities
that take place during
the semester. We consider each of these types of factors in turn.
1.2.1. Factors reflecting conditions prior to the start of a
semester
Previously acquired knowledge, skills, and stable abilities
relevant to a given
course will directly affect performance on tests and the final
examination. These fac-
tors will also have an indirect impact by influencing the amount
and type of new

learning that is necessary during the semester for a student to
reach a given level
of mastery. Based on a large body of research, the best
measures of basic cognitive
skills and abilities and prior learning are SAT scores, high-
school GPA, and prior
grades in college (e.g., Allen et al., 1972; Gortner Lahmers &
Zulauf, 2000; Hinrich-
sen, 1972; Schuman et al., 1985). Allen et al. (1972), for
example, found that high
school rank was a better predictor of GPA than study time or
test anxiety. Standard-
ized assessments of aptitude such as SAT and ACT scores are
also predictive of per-
formance in college (Gortner Lahmers & Zulauf, 2000;
Hinrichsen, 1972; Schuman
et al., 1985). One might argue that the single best variable
summarizing this informa-
tion would be the cumulative GPA for college at the time of the
start of the relevant
semester. However, this measure also reflects many stable
characteristics concerning
quality and quantity of past study behaviors that are likely to be
continued into the
current semester.

1.2.2. Factors reflecting effective study during a semester
If the goal is to predict GPA and cumulative GPA for students,
it is necessary to
focus on information that students are capable of reporting
accurately from memory
about the entire current semester. Although it would be
fascinating if students were
willing to report their detailed study processes for every hour of
study during the
semester, it would be virtually impossible to validate this
information, particularly
retrospectively. Consequently, we chose to focus on observable
characteristics of
activities that students actively initiated to influence not only
the amount of study
time but also the quality of study. Based on the deliberate-
practice framework, effec-
tive learning requires high levels of concentration and focus on
the study activities
(Ericsson, 1996, 2002; Ericsson et al., 1993). As a result,
studying should be more
(2005) 96–116 101

effective if it takes place in environments that allow full
concentration (Zimmerman,
1998, 2002). Whereas some students may walk over to the
library to study alone, oth-
ers may study with friends and in settings with many potential
distracters. However,
studying is more likely to reach a quality consistent with
deliberate practice and self-
regulated academic learning if students schedule studying
activities at suitable times
and in locations where they would be unlikely to be interrupted
and distracted.
Consistent with this argument, when researchers have taken
steps to assess dis-
tractions or interruptions to studying, they are typically
successful in predicting aca-
demic performance. For example, Michaels and Miethe�s
(1989) found that studying
with the radio and TV was associated with a lower GPA.
Hinrichsen (1972) found
that the amount of effective study time (i.e., the number of
uninterrupted minutes
spent studying) predicted GPA. In addition, Allen et al. (1972)
found that the num-
ber of interruptions that students reported during studying was
negatively correlated

with GPA. These findings suggest that students interested in
excelling in school
might be well served by choosing study environments with a
low probability of dis-
traction (e.g., studying alone in the library). We argue that such
study environments
are more likely to foster the kind of concentration and focus
necessary for effective
learning (i.e., deliberate practice and self-regulated learning).
Based on research on expert musicians and other elite
performers, we know that
engagement in deliberate practice is not generally spontaneous
but that future expert
performers habitually practice at regularly scheduled times
(Ericsson, 1996, 2002).
The factors that control engagement in deliberate practice thus
differ from the un-
planned and spontaneous engagement in more enjoyable and
effortless activities,
such as leisure activities with friends (Ericsson et al., 1993).
The need for sustained
concentration, appropriate environment, and sufficiently long
uninterrupted time
intervals for deliberate practice requires long-term time
budgeting and active prior-

itization. Therefore, given the competing demands for time in
college, deliberate
practice among college students would require active planning
of their time. Simi-
larly, self-regulated, effective learning is argued to require
careful forethought and
planning (Zimmerman, 1998, 2002). Consistent with these
propositions, Britton
and Tesser (1991) argued that because of the multiple demands
on students� time,
careful planning of time is critical to success. They believe that
good organization
and goal setting (i.e., planning activities a week or more in
advance) created a more
focused approach to studying and more efficient monitoring of
goal accomplish-
ment. Such focus and monitoring are critical to deliberate
practice. Consistent with
their theorizing, they found that self-management practices such
as prioritizing tasks
were predictive of college students� GPAs even when
controlling for their SAT scores
(also see Gortner Lahmers & Zulauf, 2000).
In order for students to engage in the high quality of study
necessary for deliber-

ate practice, it is also important that students expend the effort
to come to the classes
and attend a large percentage of them. It is in the classroom
where students receive
instruction regarding what information and skills need to be
studied and practiced
for high levels of performance. Therefore, it is expected that a
high level of atten-
dance is required for optimal quality of studying. In addition,
other demands or
draws for students� time tend to influence the use of available
time and, likely, energy
(2005) 96–116
for studying. For example, students who work for pay for a
large number of hours
each week will have fewer hours available for studying and less
freedom to choose
when to study. As a result, instead of selecting study time based
on motivation
and level of energy, people working many hours for pay may be
left with fewer op-
tions for when to study (e.g., late at night, between classes),
which may lead to less

effective and less focused studying. Similarly, students who
choose to spend extensive
time partying may also limit the available time for studying as
well as the quality of
their study time.
1.3. The current study
The current study examines those factors likely to indicate the
high quality of
study among college students, endemic to deliberate practice
and self-regulated
learning, in hopes that it will help to clarify the relationship
between study time
and GPA. Specifically, the current study examined a range of
factors reflecting con-
ditions prior to the current academic semester (i.e., high-school
GPA, SAT scores) as
well as factors from the current semester (i.e., study time, study
environment, and
planning) and attempted to predict college performance both
cumulatively and for
a current semester.
First, we assessed the relationship between estimated study time
and cumulative
GPA. We then controlled for previous performance in high

school, college, and
on standardized aptitude tests before examining the effects of
factors from the cur-
rent semester, including those related to quality of study on
college GPA. Once pre-
viously acquired knowledge, skills, and abilities are statistically
controlled, we
predict that factors related to quality and quantity of study
would emerge as predic-
tors of college GPA. Therefore, in the current study,
participants were asked about a
range of their activities in order to gain a detailed picture of the
characteristics as
well as quantity of their study behavior. Across the factors
assessed in the current
study, we focused on objective and verifiable information, such
as official university
records (e.g., GPA, SAT scores). We selected quantifiable
assessments that are ver-
ifiable in principle and minimally subjective. For example, the
time spent studying in
the library, attendance to classes, participation in parties, and
outside employment
can be validated in future studies by direct observation and
interviews of close

friends and roommates. We also collected information about
studying and other
activities in diaries. Similar methods have been used to validate
concurrent and ret-
rospective estimates of deliberate practice (Côté, Ericsson, &
Beamer, 2004; Ericsson
et al., 1993; Krampe & Ericsson, 1996). By examining a large
range of factors simul-
taneously, the current work allows us to identify those factors
that provide an inde-
pendent contribution to grade point average.
We anticipated that students, who reported studying behaviors
that reflect
important aspects of deliberate practice (i.e., focused,
uninterrupted, and carefully
planned) (Ericsson, 1996, 2002, 2003a; Ericsson et al., 1993)
and characteristics the-
oretically related to self-regulated learning (Zimmerman, 1998,
2002), would excel.
Specifically, based on the findings regarding deliberate practice
and the review of
the literature on academic performance and self-regulated
learning, we anticipated

(2005) 96–116 103
that students who studied in a quiet environment with fewer
distractions and who
carefully organized their study time would achieve higher
performance. Further,
we expected that students who attended a large percentage of
classes and had fewer
outside competing demands for their time and energy, such as
working for pay or
frequently attending parties, would have higher GPAs.
2
Finally, when other factors
that may influence the quality of study time (e.g., study
environment, planning) are
taken into account, we predicted that the amount of reported
study time would
emerge as a predictor of academic performance.
2. Method
2.1. Participants
Participants were 88 volunteer, undergraduate college students
(49% male) from
Florida State University in Tallahassee, Florida. Participants
were required to have
completed at least 1 year or 24 credit hours at the university

(mean credit
hours = 58.52, SD = 27.39) to insure that there were enough
credit hours to produce
a meaningful GPA. Participants were drawn from classes in the
departments of Psy-
chology and Education as well as from sports teams at the
university. The mean age
of the participants was 19.82 years (SD = 1.19). All participants
signed informed
consent documents and release forms for their official
university records.
2.2. Procedure
Participation took place in group sessions (typically 15–20
students) in classrooms
at the university. Participants were given a packet of materials
including a Time
Allocation and Academic Performance questionnaire, seven time
log forms, and se-
ven stamped and addressed envelopes. Participants were given
an overall explana-
tion of the study and the procedure to be followed for
completing the time logs.
Participants then completed the questionnaire, which took
approximately 45min.
Participants were asked to complete the time logs on a day-to-

day basis over the next
week and mail the completed forms to the investigators daily.
Most participants fol-
lowed the instructions for remitting the completed forms, but
some participants re-
turned multiple completed forms at the same time. The overall
purpose of the
procedure was to maintain an awareness of their daily activities
so that they could
be accurately reported.
2
Although these measures capture objective characteristics that
make deliberate practice more likely,
they do not directly measure the quality of study. We chose to
focus on observable, objective behaviors
that we believe to be associated with high quality deliberate
practice as opposed to more direct assessments
of self-reported quality of study in order to avoid potential
subjective biases in the direct quality ratings.
(2005) 96–116
2.3. Materials
2.3.1. Official university records

The University official records were used to acquire
information regarding the
participants� grade point averages from high school and college
level courses ac-
cepted by the university prior to the current semester on a four-
point scale (e.g.,
A = 4.0). In addition, participants� SAT/ACT scores were
collected. To create a sin-
gle standardized test score, students ACT scores were
transformed into SAT scores
using the University�s equivalency formula. In addition, the
GPA for the fall semes-
ter during which the study was conducted was obtained from the
official university
records after the end of the semester.
2.3.2. Time allocation and academic performance questionnaire
The questionnaire packet was designed to elicit information
from the participants
regarding their academic performance at the university and the
factors that may
influence their academic performance. The questionnaire
assessed background infor-
mation, academic history, university academic performance,
time allocation, and
study methodologies.

From the major categories listed above, questions bearing
directly on the current
investigation were selected for analysis. Participants reported
the percentage of basic
core classes (i.e., English, mathematics, and major courses) that
they had attended.
They were also asked to report the percentage of their most
difficult class and their
second most difficult class that they attended. These
percentages were averaged to
create the class attendance variable (a = .73).
The time allocation section asked participants to report the
number of hours a
week they spent in a variety of activities. Relevant for the
current investigation, par-
ticipants were asked to report the number of hours a week they
spent working for
pay (hours of work) as well as the number of hours a week they
spent at parties
or clubs (hours partying). In addition, planning practices were
obtained by examin-
ing how participants reported that they most often planned their
time. Participants
selected their method of planning from a list that included a
computer planning pro-
gram, a commercial planner, a calendar, a daily to-do list, and

keeping it in their
head. These responses were coded to create a planning variable.
Participants who re-
ported that they used long-term planning that included some
advanced planning
(e.g., a computer program, commercial planner, or calendar)
were coded as having
long-term planning (53%). If they used a daily list or kept their
plan in their head,
they were coded as not using long-term planning (47%).
The study methodologies section asked participants about their
study habits
including how much they studied, where they studied, and
whether they studied
alone. Participants reported the average number of hours they
studied per week
for their courses using two different approaches. First, they
were asked to report
the number of hours per week they studied for each of their
classes. They were next
asked the number of hours per week that they studied in a
variety of locations (e.g., a
home, library, etc.). The total number of hours that they
reported studying across

each of these measures was summed. These two measures of
study time were strongly
(2005) 96–116 105
correlated with each other (r = .71), and, therefore, the two
totals were averaged to
create a single measure of total study time.
To determine whether participants typically studied in a quiet,
solitary environ-
ment, we examined the percentage of the time that they reported
studying at the li-
brary versus at home and the percentage of time that they
reported studying alone
versus with other people present. Based on the concept of
deliberate practice people
should study most effectively if they study alone in a quiet
environment with few dis-
tractions. Therefore, the two percentages were summed to create
an index of the de-
gree to which they typically studied in a quiet environment with
few distractions
(study environment) with higher numbers indicating a better
environment.
2.3.3. Daily time logs

After completing the questionnaire, participants were requested
to complete a dai-
ly time log for seven consecutive days. Space was provided to
note the participant�s
activities (e.g., studying, sleeping, hanging out with friends) in
15-min segments
throughout a 24-h day. Participants were instructed to note
‘‘Personal’’ in the time
slots for those activities that they felt uncomfortable reporting.
In addition, partic-
ipants reported whether the week covered by the daily time logs
was a typical or
atypical week (for either academic or non-academic reasons).
The number of hours
that participants reported studying across the daily time logs
was tallied as an addi-
tional assessment of study time. Unfortunately, the time logs
were only completed by
60% of the participants and could not be universally compared
to the questionnaire
data.
3. Results
As a first step in understanding the factors that influence
performance in college,
we examined the zero-order correlations between the different
assessments of col-

lege GPA (i.e., cumulative, fall semester) and the variables that
we anticipated
would predict college GPA. The full set of correlations between
the measures
can be found in Table 1. In general, the relationships between
the different assess-
ments of GPA and the predictors were quite similar across the
measures of GPA.
Whereas neither of the assessments of GPA was associated with
the amount of
time students studied, they were both positively associated with
high-school
GPA (and SAT scores for cumulative GPA). In addition,
consistent with expecta-
tions, attending classes and having an organized approach to
planning were asso-
ciated with a higher cumulative GPA. Attending classes was
also associated with a
higher fall semester GPA. For fall semester GPA, studying in a
quiet environment
was related to a higher GPA. Further, across the assessments of
GPA, working
long hours at a job and spending more hours partying or at clubs
were associated
with a lower GPA.

It is also worth noting that the amount of time that students
spent studying was
negatively related to their SAT scores. This finding is
consistent with the idea that
students who have superior prior knowledge and skills coming
into the college could
Table 1
Intercorrelations between measures
2 3 4 5 6 7 8 9 10
1. GPA fall 2000 .55
*
.02 .25
*
.17 .27
*
.17 .27
* �.24* �.22*
2. Cumulative GPA — .11 .33
*
.24
*

.28
*
.26
*
.17 �.30* �.28*
3. Study time — �.05 �.26* .04 .20 �.21* .14 .11
4. High-school GPA — .39
*
.13 .01 �.01 �.17 �.19
5. SAT scores — �.07 .01 �.11 �.05 �.10
6. Attendance — .12 .03 �.06 �.31*
7. Planning — �.01 .10 .06
8. Study environment — �.05 �.03
9. Hours of work — �.03
10. Hours partying —
Note. N ranges from 83 to 88 depending on missing data.
*
p < .05.
(2005) 96–116
attain a given GPA with less study time than those with weaker
prior knowledge and
skills. Also, students who studied in a quiet environment with
few distractions
tended to study for less time than those who studied in a less
ideal environment.
Not surprisingly, students who spent more hours at parties and

clubs tended to at-
tend a smaller percentage of their classes. Finally, high-school
GPA and SAT were
reliably correlated.
3.1. Examination of cumulative GPA
Having established that the zero-order correlations were
consistent with predic-
tions, we were interested in examining which of the potential
predictors were inde-
pendently associated with college GPA. To this end, a
hierarchical regression
analysis was conducted on participants� measures of GPA. As
the more general
measure of GPA, we first examined cumulative GPA up to the
fall semester during
which we collected the participants� responses to the
questionnaire. In the first step
of the regression, the average study time per week based on the
questionnaire re-
sponses was entered into the equation to determine the impact
of study time in the
absence of the other potential predictors. Next, high-school
GPA and SAT scores
were entered into the regression as indicators of prior
knowledge and skills. For

the third step, other variables that were anticipated to influence
academic perfor-
mance (i.e., taking advantage of instruction and study quality)
were entered. These
variables included class attendance, planning, study
environment, and hours of
work per week. For the final step of the regression, high-school
GPA and SAT
scores were removed from the equation. This step allowed us to
identify both
the variance independently accounted for by prior knowledge
and skills and the
effect of the other predictors when the variance due to these
variables was not re-
moved from cumulative GPA.
The findings from the analyses can be found in Table 2. The
results from the first
step of the regression indicated that study time alone was not a
significant predictor
of cumulative GPA, F(1,81) = 1.01, p = .32 (b = .11). When
high-school GPA and
Table 2
Hierarchical regression analyses across measures of GPA

Cumulative
GPA
Fall GPA Fall GPA
controlling for
cumulative GPA
R
2 b R2 b R2 b
Step 1: Total Model R
2
.01 <.01 <.01
Study time .11 <.01 <.01
2
.15
*
.06 .31
*
Study Time .15 .04 �.05
High-school GPA .28
*
.20 .04

SAT scores .16 .09 <.01
Cumulative GPA up to fall — — .54
*
Step 2: Partial Correlations
For variables not in equation pr pr pr
Attendance .29
*
.25
*
.12
Planning .24
*
.17 .05
Study environment .27
*
.31
*
.21
Hours of work �.29* �.22* �.09
Hours partying �.26* �.23* �.11
2

.41
*
.29
*
.37
*
Study time .24
*
.14 .05
High-school GPA .15 .08 .02
SAT scores .24
*
.18 .10
Cumulative GPA up to fall — — .38
*
Attendance .18 .17 .10
Planning .21
*
.15 .07
Study environment .24
*
.30
*

.21
*
Hours of work �.28* �.22* �.11
Hours partying �.18 �.16 �.10
Step 4: Total Model .32
*
.24
*
.24
*
Study time .18 .10 .10
Attendance .15 .15 .15
Planning .24
*
.17 .17
Study environment .18 .26
*
.26
*
Hours of work �.30* �.23* �.23*
Hours partying �.25* �.21 �.21
Note. N = 83.
*

p < .05.
(2005) 96–116 107
SAT scores were included in the equation, the model accounted
for 15% of the var-
iance in GPA and the addition of high-school GPA and SAT
scores constituted a
significant change in the model�s overall F score,
Fchange(2,79) = 6.33, p < .004. How-
ever, examination of the independent influence of each of the
predictors revealed that
high-school GPA was the only significant predictor of
cumulative GPA, such that a
higher level of GPA in high school was associated with a higher
cumulative college
GPA, F(1,79) = 6.25, p < .02 (b = .28). An examination of the
partial correlations of
(2005) 96–116
the variables not included in the regression equation at the
second step showed that
all of these variables (i.e., attendance, planning, study
environment, hours of work,
and hours partying) would predict reliable variance in GPA
once the effects of SAT

achievement and high-school GPA were statistically controlled.
At the third step of the regression, the overall model accounted
for 41% of the
variance in cumulative GPA and the addition of the variables in
the third step re-
sulted in a significant change in the model�s overall F score,
Fchange(5,74) = 6.57,
p < .001. Examination of the independent influence of each of
the variables revealed
that when all of the predictors were included in the regression,
study time emerged as
a significant predictor of GPA, such that more study time was
associated with a
higher GPA, F(1,74) = 5.94, p < .02 (b = .24). In contrast, high-
school GPA no long-
er uniquely predicted college GPA, F(1,74) = 2.22, p = .14, (b =
.15). Further, SAT
scores provided unique prediction of GPA with higher SAT
scores associated with
a higher cumulative GPA, F(1,74) = 5.32, p < .03 (b = .24). In
addition, several of
the variables added to the regression at this step were
significant unique predictors
of cumulative GPA. Specifically, an organized approach to
planning was positively
associated with GPA, F(1,74) = 5.38, p < .03 (b = .21). As
anticipated, studying in a
quiet, solitary environment was associated with a high GPA,
F(1,74) = 6.28, p < .02

(b = .24). The more hours a student worked per week, the lower
his or her cumula-
tive GPA, F(1,74) = 9.04, p < .005 (b = �.28). Although
attendance and hours par-
tying approached significance, when the other factors were
included in the
regression, they did not reach significance.
When excluding high-school GPA and SAT scores, the
regression equation ac-
counted for 32% of the variance in GPA and the removal of
these variables consti-
tuted a significant decrease in significance of the overall model,
Fchange(2,78) = 6.01,
p < .005. At this step of the regression, each of the other
variables remained a signif-
icant predictor of cumulative GPA with the exception of study
time, which dropped
below significance.
We were interested in why the amount of study time was only a
significant pre-
dictor of GPA when all of the other variables were included in
the regression
equation. Specifically, we wanted to determine which of the
variables in our model
influenced the effect of study time on GPA. The findings from
the previous anal-

ysis indicated that high-school GPA and SAT scores influenced
the effect of study
time on cumulative GPA (i.e., study time was only a significant
predictor when
these variables were in the equation). In addition, because study
time only
emerged in the third step of the equation, it appeared that at
least one of the vari-
ables that was entered in the third step (i.e., attendance,
planning, study environ-
ment, and hours of work per week) influenced the effect of
study time on GPA.
Examination of the correlations between study time and the
variables entered in
the third step of the regression revealed that study environment
was negatively re-
lated to study time, r = �.21, p < .05. It appears that students
who study in a
quiet, solitary environment tend to study for less time than
those who study in
more disruptive environments. We suspected that the change in
the influence of
study time on GPA was due to the relationship between study
time and study
environment.

(2005) 96–116 109
To examine this possibility, we conducted a series of analyses
to explore whether
study environment suppressed the influence of study time on
cumulative GPA. When
study environment was not included in the regression but all of
the other predictors
were included, study time was not a significant predictor of
cumulative GPA,
F(1,75) = 2.91, p = .09 (b = 0.17). However, when study
environment was included
in the regression equation, study time emerged as a significant
predictor,
F(1,74) = 5.94, p < .02 (b = 0.24), such that more study time
was associated with
higher GPAs. A modified Sobel test indicated that the shift in
the effect of study time
across these regressions was significant, Sobel z = 2.23, p < .03.
3.2. Examination of GPA for the fall semester
Having examined the factors that predict cumulative GPA
before the fall semes-
ter, we next turned to the factors that predict the GPA for the
fall semester during

which the data were collected. The same type of hierarchical
regression analysis
was conducted on participants� fall semester GPA. As shown in
Table 2 the results
are essentially parallel to those obtained in the previous
analyses of cumulative
GPA.
The results from the first step of the regression indicated that
study time alone was
not a significant predictor of fall GPA, F(1,81) < 1, p = .97 (b <
.01). When high-
school GPA and SAT scores were included in the equation, the
model accounted
for 6% of the variance in fall GPA, but the addition of high-
school GPA and
SAT scores did not constitute a significant change in the
model�s overall F score,
Fchange(2,84) = 2.44, p = .06. An examination of the partial
correlations with the
variables not in the equation showed the same pattern as in the
previous analysis
of cumulative GPA. However, the partial correlation for
planning failed to reach
the level of significance.
At the third step of the regression, the addition of the new
variables led to a sig-

nificant change in the model�s overall F score, Fchange(5,74) =
4.78, p > .002. The pat-
tern of results was similar to the analysis of the cumulative
GPA up to the fall, but
seven of the eight regression coefficients were smaller in
magnitude. Only two of the
variables provided unique accounts of the variability in grades
for the fall semester.
Studying in a quiet, solitary environment was positively
associated with fall semester
GPA, F(1,74) = 8.19, p < .006 (b = .30). Furthermore, working
was associated with
a reduced level of fall GPA, F(1,74) = 4.68, p < .04 (b = �.22).
When excluding high-school GPA and SAT scores, the
regression equation ac-
counted for 24% of the variance in GPA. The removal of these
variables was, how-
ever, not associated with a reliable decrease in significance of
the overall model,
Fchange(2,78) = 2.29, p = .11.
3.3. Predicting fall GPA controlling for GPA from earlier
semesters
The similar patterns of relationships for cumulative GPA up to
fall and fall semes-
ter GPA led us to perform the same hierarchical regression
analysis of fall GPA

while controlling for the cumulative GPA for previous
semesters.
(2005) 96–116
The results from the first step of the regression are identical to
those reported ear-
lier. When high-school GPA, cumulative college GPA, and SAT
scores were in-
cluded in the equation in the second step (see Table 2), the
model accounted for
31% of the variance in fall GPA and their addition constituted a
significant change
in the model�s overall F score, Fchange(3,78) = 11.70, p <
.001. However, examination
of the independent influence of each of the predictors revealed
that cumulative col-
lege GPA up to the fall semester was the only significant
predictor of fall GPA, such
that a higher level of GPA in college up to the fall semester was
associated with a
higher fall GPA, F(1,78) = 28.53, p < .001 (b = .54).
The addition of the other variables in the third step did not
result in a significant
change in the model�s overall F score, Fchange(4,73) = 1.43, p

= .22. However, the
overall model was highly significant, F(9,73) = 4.81, p < .001,
and it accounted for
37% of the variance in fall semester GPA. It is interesting to
note that when the vari-
ables were added in the third step of the regression, cumulative
college GPA re-
mained a highly significant, but reduced, predictor of fall GPA,
F(1,73) = 9.77,
p < .002, (b = .38). Further, examination of the independent
influence of the vari-
ables added in the third step revealed that the only variable
reliably associated with
fall semester GPA was studying in a quiet, solitary
environment, F(1,73) = 4.13,
p < .05 (b = .21).
When the high-school GPA, cumulative college GPA, and SAT
scores were ex-
cluded in step 4, the regression equation accounted for 24% of
the variance in fall
GPA. The removal of these variables constituted a significant
decrease in significance
of the overall model, Fchange(3,79) = 4.09, p < .002, indicating
that they had a signif-
icant independent influence on the fall semester GPA.
3.4. Diary analyses

Given that approximately a third of the participants did not
complete the diary
portion of the study, it was difficult to draw conclusions based
on the responses to
the diary. Further, over half of the participants who completed
the diary reported
that the week covered by the diary was unusual either for
academic reasons (e.g.,
they had several exams, n = 12) or non-academic reasons (e.g.,
travel off campus,
the homecoming game, n = 17). However, we were interested in
whether the reported
study time in the diary over the week covered by the diary was
consistent with the
average study time reported in the questionnaire. Supporting the
validity of the
study time reported in the questionnaire, a tally of the time
spent studying over
the course of the week covered in the diary was significantly
correlated with the study
time from the questionnaire, r(53) = .61, p < .001. As would be
expected, this rela-
tionship was particularly strong for those students who reported
that the week cov-
ered by the diary was typical, r(22) = .74, p < .001, but did not
reach significance for

the participants who reported the week was not typical for
academic or non-aca-
demic reasons, both r�s < .38, p�s > .24.
For those participants who completed the diary and reported
that the previous
week had been typical, we examined whether their study time
reported in the diary
was related to their cumulative and fall semester GPAs above
and beyond
(2005) 96–116 111
high-school GPA and SAT scores. Regression analysis indicated
that the number of
hours that participants reported studying on the diary was
significantly related to
their fall semester GPA, F(1,18) = 8.11, p < .02 (b = .57).3
4. General discussion
The current work drew upon the theoretical frameworks of
deliberate practice
and self-regulated academic learning in order to examine why
the amount of study
by college students has been found to have no, or a negligible,
relationship to aca-

demic performance in a university setting. Previous research on
the acquisition of ex-
pert performance has shown that the level of expertise in a
domain is closely related
to the amount of high quality, focused practice, termed
deliberate practice, that indi-
viduals have accumulated during many years of committed
training (Ericsson, 1996,
2002, 2003a; Ericsson et al., 1993). In applying this approach to
performance in col-
lege, we sought to determine which characteristics of studying
would help to identify
people likely to be engaging in the type of high quality study,
which would qualify as
deliberate practice. We proposed a model where performance in
college (GPA) was
jointly determined by previously acquired knowledge, skills,
and abilities (high-
school GPA and SAT) as well as factors regulating the available
time and resources
for consistent well-planned studying and class attendance.
Based on the tenets of
deliberate practice and self-regulated learning, those who
engage in deliberate study-
ing take active steps to ensure their practice time will be of high
quality and encour-

age the improvement of performance.
The results from the current study were generally consistent
with predictions and
previous findings. First, performance attained prior to college
reliably predicted
cumulative GPA and GPA in one semester, consistent with
many previous investiga-
tors (e.g., Allen et al., 1972; Elliot, McGregor, & Gable, 1999;
Gortner Lahmers &
Zulauf, 2000; Hinrichsen, 1972; Schuman et al., 1985).
Specifically, high-school GPA
and SAT scores were both positively related to the cumulative
university GPA, and
SAT scores accounted for variability independent of all other
variables. In addition,
GPA in previous semesters of college appeared to capture the
relevant variability
associated with performance prior to entry in college when
predicting GPA for a sin-
gle semester.
When the influence of skills and abilities attained in high
school was statistically
controlled, many factors associated with current study behavior
revealed reliable

relationships with cumulative GPA and fall semester GPA. Of
particular relevance
to the theoretical framework of deliberate practice, students
who indicated that they
studied alone in an environment unlikely to contain distracters,
tended to perform
better both in the current semester and cumulatively. It is worth
noting that study
environment was a significant predictor of performance even
after accounting for
3
It should be noted that when we compared participants who
completed the diary to those who did not
complete the diary, the only significant difference between the
groups was that the participants who
completed the diary were more likely to report an organized
approach to studying, t(86) = �3.04, p < .004.
(2005) 96–116
previous performance. These findings are consistent with the
importance of concen-
trated, deliberate practice for predicting high levels of
performance (Ericsson, 1996,
2002; Ericsson et al., 1993) and self-regulated academic
learning (Zimmerman, 1998,

2002). Further, when considering cumulative GPA, the overall
amount of study time
only emerged as a significant predictor of performance when the
quality of the study
environment and scholastic aptitude at entry to college (SAT)
were included in the
regression equation. Thus, it appears that the quantity of study
time may only
emerge as a reliable factor that determines performance when
the quality of study
time and the student�s SAT scores are also taken into
consideration. In fact, the
amount of study time was negatively related to both the study
environment and
the SAT scores with no reliable evidence for a correlation
between study environ-
ment and SAT scores.
This pattern of results suggests that students with higher SAT
scores, most likely
reflecting a higher level of previously attained relevant study
skills and domain-spe-
cific knowledge, can attain the same or better grades with less
study time. Indepen-
dent of that effect, those who study alone in a quiet
environment may study more

effectively and, therefore, may attain a comparable performance
with less overall
study time than those who study in a more disruptive
environment. This finding is
consistent with previous studies of deliberate practice, where
many activities within
a domain, such as playing games of golf and playing music with
friends are far less
effective in improving performance than solitary deliberate
practice (Ericsson, 1996;
Ericsson & Lehmann, 1996). In fact, mere experience in a
domain, such as playing
chess games, does not reliably improve chess performance once
the effects of solitary
practice are accounted for (Charness et al., 1996).
The literature on deliberate practice and self-regulated learning
by skilled and ex-
pert performers shows that engagement in deliberate practice
and study is typically
carefully scheduled (Ericsson, 1996, 2002; Zimmerman, 1998,
2002). Consistent with
these findings our study found that the degree to which students
used long-term
planning was related to their cumulative GPA. In addition, this
was the case even

when high-school GPA and SAT scores were included in the
analyses (also see Brit-
ton & Tesser, 1991). The evidence suggests that careful
organization and goal setting
created a focused approach to studying and effective monitoring
of goal accomplish-
ment, supporting deliberate-practice principles.
Our analysis also replicated the influence of other factors
previously documented
to influence GPA. For example, the percentage of classes
attended was correlated
with participants� current and cumulative GPA. That is,
students who attended a
higher percentage of their classes tended to achieve higher
GPAs, which is consistent
with the findings of Schuman et al. (1985). These findings are
also consistent with the
model of deliberate practice. Attending classes would be
important for engagement
in deliberate practice, since it is in the classroom where
students receive instruction
regarding what information and skills need to be studied and
practiced for high lev-
els of performance. In addition, many instructors design their
tests based on the

material presented during lectures. However, in the regression
analyses, attendance
was only a reliable predictor of GPA prior to the entry of other
factors in the regres-
sion models. The inverse relationship between attendance and
hours partying may
(2005) 96–116 113
have accounted for the reduced independent influence of
attendance on GPA. Be-
cause students who spent more time partying were less likely to
attend their classes,
these two variables may have been tapping into the same
variance in performance
and, thus, when both were included in the regressions predicting
cumulative and fall
semester GPA, their independent influence was reduced. A
recent study manipulated
attendance experimentally in a course and found suggestive
improvements in grades
and mastery of the material, even material not covered in the
lectures (Shimoff &
Catania, 2001). The number of hours students worked per week
for pay was also re-

lated to their cumulative and current semester GPAs. That is,
students who worked
more hours per week had lower GPAs.
In sum, our study identified several characteristics of students�
behavior in college
that were correlated with their cumulative GPA and fall-
semester GPA, even when
the past performance (high-school GPA) and level of scholastic
achievement (SAT)
at their entry to the college were statistically controlled. Only
one of these variables,
namely study environment, had a direct relationship with the
fall-semester GPA that
was not explained by the accumulated GPA in college. Our
interpretation of this pat-
tern of results is that college students have established habits
for studying in college,
perhaps established in part in high school, that influence their
tendency to attend clas-
ses, their tendency to use long-term planning techniques, the
amount of time they spend
partying, and their involvement in part-time work. These habits
will influence past
grades and the cumulative GPA will provide an aggregate
reflection of these influences
in a stable manner. If there were changes in these habits during

the fall semester, the
associated changes were most likely too small to allow our
study to detect them.
Our current findings are also highly consistent with self-
regulated learning
approaches to academic performance (Pintrich, 2000;
Puustininen & Pulkkinen,
2001; Schunk & Zimmerman, 1994; Zimmerman, 1998, 2000,
2002). However, these
approaches tend to focus primarily on the motivational and
cognitive factors that
increase the likelihood of active and effective learning as
opposed to identifying
the characteristics of study and learning activities where
increased duration of
engagement leads to improved performance. Our focus on
deliberate practice led
us to describe many different factors related to academic
performance (GPA) and
to identify relations between characteristics and durations of
study activities and per-
formance. By focusing on observed engagement in these study
activities, we can
avoid the issues of the motivational and habitual factors that
lead students to engage

in them. However, a full understanding of academic
achievement will likely require
careful consideration of both the activities that increase the
productivity and efficacy
of study time (i.e., deliberate practice) as well as the social,
cognitive, and motiva-
tional factors that lead certain students to engage in these
effective study activities.
By combining the deliberate-practice framework and the
theoretical approaches of
self-regulated learning, future work may gain deeper insight
into these issues.
4.1. Limitations and future directions
Our estimated relationship between study time and GPA
measures most likely re-
flects a lower bound and would increase with better estimates
for study time. Our
(2005) 96–116
measure of study behavior using daily diaries showed that for
the sub-group report-
ing that the diary week was normal and representative, there
was a high relationship

(r = .75) between questionnaire reports of study and the hours
of study reflected in
the diaries. For this group we found reliable correlations
between diary-reported
study and fall-semester GPA. These findings suggest that the
relationship between
study and grades, especially in the associated semester, might
be stronger when stu-
dents keep diary reports of their actual study time for the whole
semester rather than
estimate the average study time for a questionnaire. Michaels
and Miethe (1989)
found the relationship between estimated study time and GPA to
be much lower
for students who primarily cram for exams (r = .10, p > .05)
compared to students
who have a sustained weekly study schedule (r = .23, p < .01).
More generally, we would expect that the relationship between
quality of study time
and grades would be much stronger when their relationship was
examined for a specific
course within a major. Ideally, one should measure prior
knowledge and abilities rel-
evant for a specific course at the beginning of classes and then
use parallel tests to mea-

sure improvements during the course. Withinthe context of a
particular course it would
also be easier to assess the specific type of studying and
practice that would be the most
appropriate for improving specific skills and expanding and
refining the desired knowl-
edge. Research on self-regulated learning and deliberate
practice would be even easier
to conduct on specific learning goals within the context of a
specific lecture topic or
homework assignment. Consistent with these ideas, many of the
recent studies of
self-regulated learning in college students have focused on
shorter-term activities with
particular learning tasks that can be monitored under controlled
conditions (Peverly,
Brobst, Graham, & Shaw, 2003; Zimmerman & Kitsantas,
2002).
In addition, it is important to note that GPA is only one
potential measure of aca-
demic performance in college. Further, as an outcome measure,
GPA has clear lim-
itations regarding what it can tell us about the academic
experience, and it likely
misses many important aspects of the educational process (e.g.,

mastery, interest).
However, GPA is an easily quantifiable and domain-general
measure that captures
many general mechanisms and factors involved in learning.
From a practical point
of view, GPA is one of the few accepted measures of
performance in college that
is used for applications to graduate school and for job
applications. As a result,
GPA in a given semester and cumulatively have meaningful
real-life implications
for students� experiences and life outcomes. However, it is
important for examina-
tions of learning to explore a range of outcome measures
assessing different aspects
of learning. In future work it will be important to explore the
current framework for
some of these other assessments.
In conclusion, we believe that our review of the large body of
research on the rela-
tionship between the study behavior in college and cumulative
GPA, in light of char-
acteristics of deliberate practice, reveals important similarities
as well as differences.
Even closer parallels are likely to emerge when we examine
more specific learning

activities in college, where students� performance is virtually
continuously evaluated
with informative personalized feedback and where detailed
characteristics of the
learning activity can be described. For these learning activities,
the insights regarding
the effectiveness of deliberate practice for expert performers
should be transferable to
(2005) 96–116 115
colleges and graduate schools, and they should offer a rich and
convenient opportu-
nity to test and discover new knowledge about more effective
means to improve the
trained performance. This work will also help students and
teachers understand the
pre-requisite need for extensive practice, even for the most
‘‘talented,’’ to master new
aspects of complex skills, and acquire extensive new
knowledge.
References
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Elliot, A. J., McGregor, H. A., & Gable, S. (1999).
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