Study Habits, Skills, and Attitudes: The Third Pillar Supporting Collegiate Academic Performance

PERSPECTIVES ON PSYCHOLOGICAL SCIENCE
Study Habits, Skills, and

Attitudes
The Third Pillar Supporting Collegiate Academic
the examined criteria. Overall, study habit and
Performance skill measures improve prediction of academic
Marcus Crede´1 and Nathan R. performance more than any other noncognitive
individual difference variable examined to
dateandshouldberegardedasthethirdpillarofacad
Kuncel2 1 2 emic success.
University at Albany, SUNY, and University of

Minnesota Our investment in higher education is enormous.
We are painfully reminded of this whenever
seemingly qualified students fail in college or drop
out from graduate school. What explains
ABSTRACT—
Studyhabit,skill,andattitudeinventoriesand
constructs were found to rival standardized tests
Address correspondence to Marcus Crede´, Department of Psychology,
and previous grades as predictors of academic University at Albany, SUNY, Social Sciences 369, 1400 Washington
performance, yielding substantial incremental Avenue, Albany, NY 12222; e-mail: mcrede@albany.edu.
validity in predicting academic performance. This these performance discrepancies? To protect this
meta-analysis (N 5 72,431, k 5 344) examines the investment, researchers have focused on
construct validity and predictive validity of understanding the academic success and failure of
10studyskill constructsforcollege students.We students and have examined a wide array of
found that study skill inventories and constructs student characteristics as determinants of
are largely independent of both high school academic performance. These individual
grades and scores on standardized admissions
difference factors can be coarsely subdivided into
tests but moderately related to various
intellective (cognitive) and nonintellective
personality constructs; these results are
(noncognitive) factors. Psychology and education
inconsistent with previous theories. Study
have a good grasp on the intellective factors that
motivation and study skills exhibit the strongest
relationships with both grade point average and encompass most of the variables typically
grades in individual classes. Academic specific considered in the admissions process, such as
anxiety was found to be an important negative scores on cognitively loaded admissions tests.
predictor of performance. In addition, significant Recent metaanalytic evidence has shown that a
variation in the validity of specific inventories is consideration of these intellective factors is
shown. Scores on traditional study habit and valuable given the substantial predictive validities
attitude inventories are the most predictive of of students’ prior grades and the ubiquitous
performance, whereas scores on inventories predictive power of admissions tests at both the
based on the popular depth-of-processing college and graduate school levels across a range
perspective are shown to be least predictive of of outcome variables (e.g., Bridgeman, McCamley-
Volume 3—Number 6 425

Study Habits Meta-Analysis
Jenkins, & Ervin, 2000; Halpin, Halpin, & Schaer, Another promising group of highly academically
1981; Kuncel, Crede´, & Thomas, 2007; Kuncel & focused factors relate specifically to the studying
Hezlett, 2007; Kuncel, Hezlett, & Ones, 2001, and learning behaviors of students. The empirical
2004; Noble, 1991). Despite the impressive and theoretical literature relating to these
predictive validities of intellective factors with constructs is very large and very fragmented,
regard to academic achievement, researchers described by a wide variety of proposed
have turned their attention to nonintellective constructs, and operationalized by an array of
factors for two broad reasons. inventories. Proposed constructs include study
First, all stakeholders are interested in making skills (e.g., Aaron & Skakun, 1999), study habits
better admissions decisions. Students, faculty, (e.g., Murray & Wren, 2003), study attitudes (e.g.,
universities, and society have a vested interest in W.S. Zimmerman, Parks, Gray, & Michael, 1977),
successful students. This has lead to an ongoing study motivation (e.g., Melancon, 2002), meta-
search for additional variables that may improve cognitive skills (e.g., Zeegers, 2001), study anxiety
the quality of admissions decisions and improve (e.g., Miller & Michael, 1972), and depth of
our understanding of academic performance. processing (e.g., C.W. Hall, 2001). Frequently used
Second, the reliance on intellective factors has inventories of these constructs are comparably
produced adverse impact in the admission process numerous and include the Survey of Study Habits
(Sackett, Schmitt, Ellingson, & Kabin, 2001), due to and Attitudes (SSHA; W.F. Brown & Holtzman,
the substantial group differences that have been 1967), Learning and Study Skills Inventory (LASSI;
observed in scores on both cognitive admissions Weinstein, & Palmer, 2002), Inventory of Learning
tests and prior grades (e.g., Zwick, 2004). A Processes (Schmeck, Geisler-Brenstein, & Cercy,
consideration of nonintellective factors in the 1991), and the Study Process Questionnaire
admis- (Biggs, 1987).
However,
Copyright r 2008 Association ‘‘promising’’
for Psychological Science does not mean
sions process may have the applied benefit of ‘‘proven.’’ Assessment and training are not free,
reducing adverse impact while simultaneously and study habits, skills, and attitudes (SHSAs)
increasing the accuracy of admissions decisions. would need more than strong correlations with
The evidence regarding the validity of scores on subsequent performance to be powerful
inventories of nonintellective factors, however, predictors—they would also need to add
has often been underwhelming. Observed considerable unique information to the existing
relationships between personality and academic measures to warrant their use. Despite the
achievement have typically been low (e.g., Ridgell considerable research attention focused on these
& Lounsbury, 2004; Thomas, Kuncel, & Crede´, various constructs, these issues have not been
2007; Zagar, Arbit, & Wengel, 1982). Not resolved, and the precise nature of the constructs’
surprisingly, only specific aspects of temperament relationship to academic performance is not well
are relevant in academic situations, as is the case understood. The combination of construct
in work settings (e.g., conscientiousness, Duff, proliferation and mixed findings in the literature
Boyle, Dunleavy, & Ferguson, 2004; Lievens, has lead to this state. The development of a
Coetsier, De Fruyt, & De Maeseneer, 2002), and taxonomy combined with a meta-analytic review
these relationships are markedly smaller than will likely provide clarity and condense the
those obtained from tests and prior grades. extensive but fragmented empirical literature and
Recent work has shown more promising levels of the variety of theoretical and empirical
validity for scores on biographical inventories and approaches. We anticipate both practical and
situational judgment tests (Oswald, Schmitt, Kim, theoretical benefits.
Ramsay, & Gillespie, 2004), as well as for a variety At a practical level, we anticipate benefits for
of psycho-social variables including academic the admissions process, college counseling
motivation, achievement motivation, and programs, and for the measurement of SHSAs. We
academic self-efficacy (Robbins et al., 2004). do not believe that self-reports of study behaviors
However, some of these factors are also likely to as currently measured are likely to be particularly
be associated with opportunity and social class, useful in an admissions context given the
and some are not readily modifiable through susceptibility of such inventories to faking and
intervention.
426 V
Marcus Crede´ and Nathan R. Kuncel
socially desirable responding.1 Rather, we candidates for training. A final practical benefit is
anticipate that ratings of SHSA constructs would that a meta-analytic review will also likely
be more useful when provided by high school establish whether or not scores on different
counselors, principals, or teachers, particularly if inventories of SHSA constructs are differentially
these ratings are made using psychometrically valid with regard to college performance. This
sound rating forms. would allow researchers and practitioners in the
In addition, capturing accurate SHSA SHSA domain to make informed decisions
information about college applicants in a low- regarding the appropriateness of different
stakes development context would allow inventories and may also provide motivation for
admissions officers to better identify students the refinement of existing inventories.
who would be able to succeed in college and A better understanding of how SHSA constructs
would allow college counselors to better relate to academic performance will also facilitate
anticipate the academic difficulties of at-risk a better theoretical understanding of how various
students (e.g., students with sound admissions individual difference factors are related to
test scores but poor study habits). Meta-analytic academic performance. The predictive power of
results illustrating meaningful relationships scores on cognitive instruments such as the SAT or
between SHSA constructs and academic GRE have a good deal of utility in the selection
performance may act as a spur for the process, but these scores on their own do not
development and use of such rating forms. It is provide us with a full understanding of why
also important to note that a meta-analytic review success or failure occurs (e.g., McCall, 1994;
will directly benefit training and counseling Romine & Crowell, 1981). SHSA constructs can
programs that focus on providing students with provide us with a better understanding of these
better SHSAs by highlighting constructs and phenomena, especially if we consider that, by
processes that are most strongly related to some accounts, many freshmen college students
performance in college. Programs that focus on do not possess the repertoire of study skills and
the acquisition of specific study skills are likely to study habits necessary to effectively cope with the
be particularly useful in light of the consistent academic requirement of colleges (e.g., Bishop,
finding that the amount of studying (time spent Bauer, & Becker, 1998; Hechinger, 1982; Sanoff,
studying) is largely unrelated to academic 2006) or to prepare effectively for high stakes
performance (e.g., Mael, Morath, & McLellan, testing situations (e.g., Loken, Radlinksi, Crespi,
1997; Schuman, Walsh, Olson, & Etheridge, 1985). Millet, & Cushing, 2004).
Improving study-skill training interventions Specifically, measures of cognitive ability
appears to be particularly important given meta- provide an indication of whether a student has
analytic evidence that the impact of study-skill the ability to learn and understand complex
training interventions on both reported study material, but they do not indicate whether the
practices and performance is strongly moderated student has acquired the patterns of studying
by the type of study skill training (Hattie, Biggs, & behavior that are necessary to process, integrate,
Purdie, 1996). and recall such material. Different SHSA
Identifying SHSA constructs that are most constructs can provide information about whether
strongly related to academic performance should this is a matter of attitudes toward studying (e.g.,
assist in both identifying ineffective training ‘‘I feel that it is not worth the time, money, and
methods and in identifying what the focus of effort that one must spend to get a college
training programs should be. Those that are most education’’), actual study behaviors (e.g., ‘‘I stop
strongly related to academic performance may, all periodically while reading and mentally go over or
else being equal, be of the highest utility as review what was said’’), or the cognitive processes
engaged in by students while studying (e.g., ‘‘I
1 However, the current state of research on detection and make connections among the different ideas or
control of faking on personality and related tests is as
promising as it has ever been with the recent development of
topics I am studying in my courses’’). A better
a variety of promising methods (Bagby et al., 1997; Eid & understanding of the potentially different
Zickar, 2007; Kuncel & Borneman, 2007). Proven versions of relationships among SHSA constructs and
these methods may be operational soon. What will remain to
be seen is if they can resist the inevitable flood of coaching academic performance can be attained by
methods that follow high-stakes testing.

considering the dimensionality of SHSAs and how delineation of the construct space has occurred
these constructs fit into existing theoretical over time. Many inventories have sought
frameworks of performance in general and of especially to distinguish between study skills,
college performance in particular. study habits, and study attitudes. The SSHA
(Brown & Holtzman, 1955, 1956) and the LASSI
THEORIESANDMEASURESOFSTUDYBEHAVIORS (C.E. Weinstein & Palmer, 2002) are two examples
of this distinction and are the most widely used
The Dimensionality of SHSAs inventories of SHSAs. Brown and Holtzman
The research literature on SHSAs dates back over proposed a hierarchical structure for the SSHA
65 years (e.g., Hartson, Johnson, & Manson, 1942; comprised of four variables—delay avoidance,
L. Jones & Ruch, 1928; Locke, 1940), but work methods, educational acceptance, and
substantial disagreement remains as to the teacher approval—that are combined into two
dimensionality and structure of SHSAs. This lack of higher level scores of study habits (delay
agreement appears to be largely a function of the avoidance and work methods) and study attitudes
differing ways in which operationalizations of (educational acceptance and teacher approval).
SHSAs have been developed. Although some These two are in turn, aggregated to obtain a
researchers have adopted a strictly empirical general study orientation score. The LASSI
approach whereby items that optimally assesses even more SHSA dimensions, being
distinguish between over- and underachievers are comprised of 10 subscales: anxiety, attitude,
factor analyzed to generate constructs (e.g., W.F. concentration, information processing,
Brown & Holtzman, 1955), others have based motivation, selecting main ideas, self-testing,
inventories on theoretical considerations (e.g., study aids, test strategies, and time management.
Entwistle, Thompson, & Wilson, 1974) or on Each of the 10 subscales is, in turn, related to one
qualitative analyses of the verbalized strategies of three strategic learning components that
used by students when studying (e.g., Marton, reflect the distinction between study skills, study
Hounsell, & Entwistle, 1984; Pressley & attitudes, and study habits: skill (information
Afflerbach, 1995). In all, scales and research in this processing, selecting main ideas, and test
domain tend to focus on one of three broad strategies), will (anxiety, attitude, and motivation),
areas: SHSAs themselves, the depth at which and self-regulation (concentration, self-testing,
information is processed while studying, and the study aids, and time management).
metacognitive awareness of the studying student.
Information Processing Approaches

Study Skills, Study Habits, and Study Attitudes Although inventories such as the SSHA and LASSI
As typically used in the broader literature, study distinguish among specific study competencies as
skills refers to the student’s knowledge of well as among habits, attitudes, and skills, other
appropriate study strategies and methods and the educational researchers have focused on the
ability to manage time and other resources to depth at which students process the information
meet the demands of the academic tasks. Study that is being studied. This approach is based on
habits typically denotes the degree to which the the information processing model of memory,
student engages in regular acts of studying that which proposes that individuals remember
are characterized by appropriate studying material more accurately if the material is
routines (e.g., reviews of material) occurring in an processed at a deep level rather than at a surface
environment that is conducive to studying. Finally, level (e.g., Craik & Lockhardt, 1972; Marton &
study attitudes is usually used to refer to a Sa¨ljo¨, 1976). Deep processing involves relating
student’s positive attitude toward the specific act new material to the existing knowledge structure,
of studying and the student’s acceptance and whereas a surface approach focuses primarily on
approval of the broader goals of a college rote memorization leading to a reproduction of
education. new material without integration with existing
Early inventories of SHSAs (e.g., Hartson et al., information.
1942; Locke, 1940; Michael & Reeder, 1952) were Biggs and colleagues and Entwistle and
largely unidimensional in nature, but a finer colleagues (e.g., Biggs, 1979; Biggs, Kember, &
428 V
Leung, 2001; Entwistle, Hanley, & Hounsell, 1979; efficacy and effective time management skills, and
Entwistle & Ramsden, 1983; Entwistle & are goal directed and self-motivated (Ley &
Waterson, 1988) expand on this information Young, 1998).
processing framework by including three In aggregate, the literature suggests that SHSAs
processing approaches and associated are multidimensional in nature (Gettinger &
motivational determinants: (a) the deep Seibert, 2002). Across all of the measures
approach, which is driven by one’s internal examined in this study, 10 commonly examined
motivation and commitment to learning; (b) the constructs or dimensions are evident. Collectively,
surface approach, which is driven by one’s the literature suggests that effective studying
external motivation; and (c) the strategic requires not only that the student possess
approach, which is driven by one’s motivation to knowledge of appropriate studying techniques
attain high grades without regard to learning of and practices (study skills), but also sustained and
any type. This general theoretical framework and deliberate effort (study motivation), self-
the associated desirability of a deep approach to regulation, ability to concentrate, selfmonitoring
studying has been widely acknowledged in the (study habits), and a sense of responsibility for
literature (e.g., Diseth & Martinsen, 2003; and value in one’s own learning (study attitude).
Entwistle & Waterson, 1988; Marton, 1976; In addition to these four constructs and the three
Schmeck & Grove, 1979; Schmeck, Ribich, & level-of-processing constructs (deep approach,
Ramanaiah, 1977; Schmeck & Spofford, 1982; surface approach, achieving approach), some
Watkins, 1983). researchers also focused on metacognitive skills
(discussed earlier) and study anxiety, which was
assessed in some of the more commonly used
Metacognitive Skill Approaches inventories (e.g., LASSI). Study anxiety as used by
A third set of researchers has noted the lack of a the examined inventories, refers to feelings of
relationship between cognitive ability and the use tension and anxiety based on perceptions of low
of specific study behaviors (e.g., Snow & Lohman, competence that accompany the act of studying.
1984). These researchers argue that as cognitive Like many early inventories, some more recently
ability increases, students have an increasing developed measures also report only an overall
array of available strategies to choose from and SHSA score, and we therefore included an
an increased ability to adapt their study strategy aggregate SHSA construct in our analysis. Each of
to the demands of the particular situation. This these 10 constructs is described in detail in Table
ability to adapt study behaviors to the demand 1.
characteristics of the particular learning tasks has It is important to note that our description of
been termed metacognition and self-regulation these 10 constructs serves primarily to highlight
ability (e.g., Biggs, 1985; Gettinger & Seibert, the most frequently encountered constructs in the
2002; Ley & Young, 1998). Flavell defines broad SHSA literature. The degree to which these
metacognitive processes as ‘‘one’s knowledge constructs are a complete and parsimonious
concerning one’s own cognitive processes and representation of the overall construct space
products . . . [and] the active monitoring and cannot be answered satisfactorily at this time.
consequential regulation of those processes in Very few studies have assessed students’ scores
relation to the cognitive objects or data on which across multiple inventories and/or constructs
they bear’’ (Flavell, 1976, p. 232). Students high in making it impossible to construct a meta-analytic
metacognitive and selfregulatory abilities are matrix of construct intercorrelations. Indeed, we
thought to be characterized by active involvement are aware of only a single (unpublished) study
in their own learning process; continuous (Cole, 1988) that utilized both of the most
planning; and the careful monitoring of the task frequently cited SHSA inventories (the SSHA and
that they are required to complete, their own LASSI) and provided their intercorrelations. The
study behaviors, and the match between task and evidence that is available does, however, suggest
study behavior (B.J. Zimmerman, 1986). In that some construct redundancy is likely to exist.
addition, self-regulated learners seek assistance For example, normative data of the LASSI (C.E.
from peers and teachers, possess high self- Weinstein & Palmer, 2002) shows substantial

overlap between various subscales, with specific study behavior, but it is overly limiting for
disattenuated correlations being as high as r5 .94.some of the attitudinal measures that are not
The SHSA literature is likely to benefit from a likely to be proximal determinants of academic
more detailed examination of the discriminant behaviors and success.
validity of these constructs than is possible in a The mediational approach that we favor argues
review of the published literature. that SHSA measures quantify groups of academic
specific attitudes and behavioral tendencies that
are more proximal in their relationship to learning
SHSAs and Academic Performance than are individual differences like personality,
The relationship between the various SHSA attitudes, and interests. The relationships
dimensions and subsequent academic between personality, attitude, and interests and
performance has been considered from three academic performance are indirect
perspectives: direct effects, mediational effects, Integrated Sustainability
Initiative. aReverse scored.
and interactive effects. The first and most
straightforward perspective conceptualizes SHSAs
as direct measures of study-specific behaviors
and mediated through their influence on SHSAs.
that cause academic success. This framework is
For example, study anxiety would be a more
applicable to some SHSA measures that ask about
proximal determinant of performance than would
TABLE1
Construct Description With Representative Measures
Category Description Representative measures of construct
Study skills Ability to manage time and allocate other resources in Time management (LASSI), selecting main ideas (LASSI),
accordance with the demands of the academic tasks, fact retention (ILP), critical thinking (MSLQ), Tyler-
ability to organize, summarize, and integrate material. Kimber
Test of Study Skills, Boyington Study Skills Test, Study
Methods and Systems (SAMS)
Study habits Sound study routines, including, but not restricted to, Study Habits Inventory, study habits (SSHA), rehearsal
frequency of studying sessions, review of material, (MSLQ), cognitive monitoring (Study Activity Survey),
selftesting, rehearsal of learned material, and studying in a Study
conducive environment. Habits Test, reviewing subject matters (Study Behavior
Questionnaire)
Study attitudes A positive attitude toward education in general and Study attitudes (SSHA), attitude (LASSI), academic
studying in particular. interest/ love of learning (SAMS), alienation toward
authority
(SAMS),a Student Attitudes Survey, giving priority to
studies
(College Adjustment and Study Skills Inventory)
Study anxiety Anxiety attached to either the act of studying or taking of Anxiety (LASSI), test anxiety (MSLQ), study anxiety
tests. (SAMS)
Study motivation Combination of both intrinsic and extrinsic motivation to Motivation (LASSI), motivation (Self-Regulation
engage in studying rather than other nonacademic Questionnaire), academic drive, conformity (SAMS),
activities. working without prodding, motivation (Study Methods
Scale)
Deep processing Attempt to understand material and integrate it with one’s Deep approach (SPQ), deep processing (ILP), Deep
existing knowledge structure to construct a global picture Processing Scale, generation of constructed information
characterized by intrinsic motivation. (Study Activity Survey)
Surface Reliance on role learning and memorization that allows Surface approach (SPQ), surface approach (ASSISI),
processing reproduction of learned material, characterized by duplicative processing (Study Activity Survey), fact
extrinsic task motivation. retention (ILP), reproducing orientation (Approaches to
Studying Inventory)
Strategic Focus is on achieving good grades through any means Strategic approach (SPQ), manipulation (SAMS),
processing necessary, characterized by a systematic study routine strategic approach (ASSISI)
that may be surface or deep in nature.
Metacognitive Awareness of studying process, monitoring of studying Self-regulation (MSLQ), meta-cognitive strategy
skills effectiveness, ability to adapt studying technique to suit (selfregulation questionnaire), Executive Process
situational demands. Questionnaire
Aggregate Broad measures of good study habits, study skills, study Study orientation (SSHA), Study Methods Scale, LASSI
measures attitudes, and motivation. total score, SAMS total score, College Adjustment and
430 Study Skills Inventory, Study Habits and AttitudesV
Inventory
Note. LASSI 5 Learning and Study Skills Inventory; ILP 5 Inventory of Learning Processes; MSLQ 5 Motivated Strategies for Learning Questionnaire; SAMS 5 Study
Attitudes and Methods Survey; SSHA 5 Survey of Study Habits and Attitudes; SPQ 5 Study Process Questionnaire; ASSISI 5 Strategic Systems-Based
trait anxiety. In addition, some of the effects of direct and indirect determinants. Effective
cognitive ability would be predicted to be performance in a student leadership role would
mediated through study skills but not study have a different set of determinants than would
motivation. Finally, characteristics like typical avoiding drug and alcohol abuse. This is salient
intellectual engagement would be mediated because recent empirical work has highlighted
through some types of study attitudes. that academic performance is multidimensional
The framework we present here is an extension and that predictors have differential relationships
of both a general theory of work performance depending on the dimension of performance. For
articulated by Campbell and colleagues (e.g., example Oswald et al. (2004) reviewed the
Campbell, 1990) and the application of this educational objectives and mission statements of
general theory to the academic performance 35 colleges and universities and identified a
domain (e.g., Kuncel, Hezlett, & Ones, 2001, number of academic performance dimensions,
2004). The application of the Campbell model to including leadership, interpersonal skills, and
the academic domain, and our extension of it to adaptability. Similarly, Kuncel and Hezlett (2007)
include SHSA constructs, is presented in Figure 1. demonstrated that all graduate admissions tests
This model proposes that performance on a task were positive predictors of an array of
is a function of three direct proximal performance measure (e.g., research productivity,
determinants: declarative knowledge (knowledge grades, faculty evaluations, degree attainment)
of facts and procedures), procedural knowledge but found that the magnitude of the relationship
(the skill to do what is required in a situation), and varied considerably depending on the nature of
motivation (the willingness to engage in and the performance domain.
sustain a high level of effort in completing the Second, we make an important distinction
task). The model is also characterized by a series between two stages of academic performance.
of indirect and more distal determinants: The first stage includes the behindthe-scenes
cognitive ability; interests and personality; and behaviors involving studying, time management,
education, training, and experience. The effects of and avoidance of behaviors that are
these distal determinants on performance are counterproductive for classroom success. This
fully mediated by the three direct determinants. stage of performance determines the amount of
In other words, effective performance on a knowledge and skill acquired. Successful
dimension of student performance is directly a performance at this stage involves effectively
function of task-relevant knowledge and skill and engaging in behaviors related to knowledge and
the immediate willingness to engage in a high skill acquisition, such as studying, communicating
Fig. 1. Proposed model of academic performance determinants.

level of effort that is sustained over time. The with peers, choosing to read at the library to avoid
influence of all other individual differences are distractions, and so on. In the second stage, the
mediated through knowledge,General Cognitive
skill, and these accumulated knowledgeDeclarative
and skill is assessed on
Ability Knowledge
specific motivational behaviors. For example, a exams, during presentations, and in written
high interest in mathematics is associated with a papers. Performance at this stage involves the
Study Skills
high grade in a mathematics examination, but this actual evaluation (taking the exams, giving the
Academic
Prior Training & Procedural
effect would be mediated by its influence on the presentation, etc).
Experience Performance at Performance
the second
Knowledge Dimension
acquisition of mathematical knowledge and skillStudy stage
Habits determines grades and is the most
and a willingness to use those skills to solve Study Attitudes
a observable aspect of student performance for
problem. faculty.
Interests & Motivation
Two additional aspectsPersonality
of this model are It is important to note that we have presented
important to note. First, there are separate sets of SHSA constructs as causal influences on academic
direct and indirect determinants depending on performance. We acknowledge that such a causal
the dimension of performance in question. For sequence is, of course, impossible to establish in a
example, paper writing and studying for an exam meta-analytic review given the correlational
would have different but overlapping sets of nature of most of the published data. At the same

time, a number of theoretical and empirical practices leading to higher academic performance
considerations suggest that a causal framework is that, in turn, act to reinforce those same good
not unreasonable. First, students must act to study practices, or poor academic performance
acquire knowledge (study, practice, integrate, acting as a motivator to change poor study
retain) before it can be translated into practices. Finally, we also note that our individual
performance on a test or exam, and extensive difference model does not specify
data from the experimental literature situational/contextual influences that almost
(summarized by Hattie et al., 1996) has shown certainly affect a student’s level of academic
that study skill training interventions can impact performance. The social environment (e.g., social
both study skill levels and academic performance. support, social integration) is an example of one
Second, a significant proportion of the SHSA domain that is likely to have effects above and
literature has made use of predictive, rather than beyond those of the individual difference
concurrent, research designs whereby SHSA data variables that are included in our model.
is gathered at one time point and academic
performance data is gathered at a later time point
(e.g., Ahmann & Glock, 1957; W.F. Brown & SHSAs as Moderators
Holtzman, 1956, 1967; Culler & Holahan, 1980; An alternate perspective (e.g., Hau & Salili, 1996)
Davenport, 1988; Holtzman, Brown, & Farquhar, on the role of SHSAs in determining academic
1954; Stockey, 1986). These studies have shown performance that has found some empirical
strong relationships between SHSA scores and support (e.g., De Sena, 1964; Lum, 1960; R.C.
future academic performance. Third, a causal Myers, 1950; Waters, 1964) is that SHSAs act as
mechanism is consistent with numerous other moderators of the relationship between cognitive
theoretical models of academic performance (e.g., ability and academic performance. From this
Chartrand, 1990; Rossi & Montgomery, 1994; perspective, effective performance in college
Tinto, 1975), including models that specifically requires not only high cognitive ability, but also
position SHSA constructs as mediators of the sound SHSAs. In the absence of good study skills
relationship between intellective and and study habits, even students with high
nonintellective factors on the one hand and of cognitive ability will do poorly, whereas good
academic performance on the other hand (e.g., study skills and study habits allow students with
Biggs, 1978; Elliot, McGregor, & Gable, 1999; high cognitive ability to perform well above
Horn, Bruning, Schraw, Curry, & Katkanant, 1993; students with low or medium cognitive ability
McKenzie & Gow, 2004). levels. In other words, the relationship between
Our model is, of course, an attempt to cognitive ability and academic performance is
represent a highly complex phenomenon (student likely to be strongly positive among students with
studying behavior and learning over a 15-week high levels of SHSAs, whereas it is likely to be
semester) in relatively parsimonious terms. As much weaker (although still positive) among
such, we have excluded numerous influences, students with low levels of SHSAs. This
processes, and variables that may also play a role. conceptualization of the role of SHSAs is also
These warrant brief discussion. First, it is possible reflected in the study-skill training programs that
that the relationships between factors such as universities have instituted to assist those
cognitive ability or study skills and academic students judged to be performing below their
performance are moderated or mediated by potential (e.g., Bahe, 1969; Giles-Gee, 1989;
additional variables that are not explicitly included Hattie et al., 1996). This theoretical moderating
in our model. Tinto’s (1975) model of student role is illustrated in Figure 2.
attrition, for example, positions academic and
social integration as mediators of the relationship
between individual attributes (e.g., cognitive Goals of this Article
ability) and of the decision to drop out of Finding an explanation for unexpected student
university. Second, it is possible that some of the failures and successes in higher education is a
effects outlined in our model may in fact be high priority. SHSAs are not only likely candidates
bidirectional or recursive in nature: good study that can help account for these prediction errors
they are also responsive to training, thus making
432 V
their practical utility even greater. Therefore, the not included in our analysis. Including such studies
goals of this study are to provide comprehensive would have resulted in an upwardly biased
estimates of the predictive, incremental, and estimate of the relationship between academic
construct validity of SHSAs. More specifically, our performance and measures of study habits, study
goal here is to provide validity summaries for both skills, and study attitudes. The database was also
scores on individual inventories of SHSAs and closely examined to ensure that only one element
broader SHSA constructs, of any overlapping samples (e.g., dissertations
High SHSA that were later published as journal articles) was
Low SHSA included in our analyses.
Cognitive Ability
Coding Procedures
The coding of all articles, reports, and
dissertations was systematized via the use of strict
coding procedures and coding sheets. These
sheets facilitate the capture of all relevant data
and cue the coder to attend to important study
information. Marcus Crede´ did all coding of
validity and ability correlations, and Nathan R.
Academic Performance Kuncel completed accuracy checks on a small
portion of the coded material and coded
Fig. 2. Theoretical moderating role of SHSAs for the personality correlates.
relationship between cognitive ability and academic
performance. All predictor–criterion correlations were coded
and entered into an Excel spreadsheet. Other
important study information was also captured.
as well as their relationships with traditional
This included study design (predictive, concurrent,
predictors and personality traits. Establishing the
and retrospective), sample characteristics
strengths of these relationships will allow us to
(gender, ethnicity, age, year in college, and
estimate the degree to which SHSA constructs and
major), time lag between collection of predictor
inventories are able to account for variance in
and collection of criterion, type of university at
academic performance above and beyond that
which data was collected (public or private), as
accounted for by traditional predictors and help
well as year of publication.
clarify their place in a nomological network of
Intercorrelations among the predictor variables
individual differences.
and correlations between predictor variables and
METHOD traditional cognitive predictors of college
performance and personality constructs were also
Literature Search coded. Intercorrelations among predictor
Possible sources of data for this study were variables, such as correlations among the
identified via searches of the PsycINFO (1872– subscales of a test, allow unitweighted composites
2005), Dissertations Abstracts (1980–2005), to be formed from subscale level data.
Education Full Text, and ERIC databases. Further Intercorrelations between predictor variables and
possible data sources were obtained by examining cognitive ability tests allow an examination of the
the citation list of all examined journal articles, degree to which the study habit predictors explain
technical reports, and dissertations for additional incremental variance in college performance over
promising sources. and above the variance explained by standard
Studies were only included in our analysis if cognitive admissions tests such as the SAT and
they reported zero-order correlations between ACT.
relevant criteria and SHSA predictors or if they
presented statistics or data that could be
transformed into correlations. A total of 19
studies that only reported statistically significant
correlations between criteria and predictors were

Final Database Personality Categories
After composites were formed and unusable data Numerous researchers have investigated the
was excluded, the database for the relationships relationship between students’ SHSAs and various
between SHSAs and academic performance personality constructs. To facilitate meta-analytic
consisted of 961 correlations from 344 aggregation, we grouped personality measures
independent samples representing 72,431 college into constructs using the taxonomy of personality
students. In addition, 424 correlations between scales developed by Hough and Ones (2001).
SHSA predictors and cognitive ability tests and 80 Sufficient data was available to allow analysis
correlations between SHSAs and personality tests between study habits and study attitudes and
were also coded. eight personality constructs: achievement
motivation, neuroticism, external locus of control,
internal locus of control, extroversion, openness
to experience, conscientiousness, and self-
Predictor Categories concept.
The study habits literature is highly diverse in
terms of both the measures of study habits and
study skills that are used and the criteria that are ANALYTICPROCEDURE
considered. Given the range of variables and
We used the Hunter and Schmidt (1990, 2004)
existence of a multidimensional predictor space,
psychometric meta-analytic method in this study.
we considered it inappropriate to collapse all
This method allows estimation of the amount of
SHSA inventories into a single category or to
variance attributable to sampling error and
equate all academic performance criteria.
artifacts such as unreliability in both the predictor
Therefore, we followed a dual meta-analytic
and criterion variables. In addition, this method
strategy. First, we conducted separate analyses
also provides the best estimate of the population
for SHSA inventories for which sufficient validity
correlation between the predictors (SHSAs) and
information was provided (at least five samples
criteria (GPA, course achievement) in the absence
for each criterion). Second, we grouped
of measurement error. As not all studies included
inventories and their subscales into broader
in our database reported the necessary
construct categories according to the content of
measurement error information, this study relied
the subscales. This was done on the basis of scale
on the existing research literature to construct
descriptions and item content. In addition to the
appropriate artifact distributions and then used
10 broad SHSA constructs summarized in Table 1,
the interactive meta-analytic procedure (Hunter &
we also analyzed the relationship between
Schmidt, 1990) to improve the accuracy of the
academic performance and measures of the
results. Artifact distribution information for
amount of time spent studying—a commonly
unreliability in both the criterion and predictor are
examined relationship.
presented in Table 2. The reliability of grades was
Criterion Categories
based on internal consistency reliabilities from
The large numbers of different criteria used by
three studies of college grades from Barritt
researchers in this area were grouped into four
(1966), Bendig (1953), and Reilly and Warech
categories to facilitate analysis: first-semester
(1993). Corrections for unreliability in the
freshman GPA, freshman GPA, general GPA, and
predictor variable were only conducted when
performance in individual classes. First-semester
reliability information was available for scores on
GPA was also included in the freshman GPA
the specific inventory. For inventories in which no
category, which in turn was also included in the
reliability information was available (e.g., Study
general GPA category. For some inventories and
Habits and Attitudes Inventory, Taylor-Kimber
for some SHSA categories, separate analyses
Study Skills Test), we made no corrections for
could not be conducted for all four of these
unreliability. In the case of the SSHA, we used two
criterion groups due to insufficient validity
separate reliability distributions. The first was
information (k < 5).
based on test–retest reliability data, and the
second was based on indicators of internal
consistency (Cronbach’s alpha).
434 V
TABLE2 measure, we used a constant level of unreliability
Reliability Artifact Distributions for SHSA Constructs and to estimate the operational validity of scores on
GPA specific tests.
Criterion Correcting the sample size weighted mean
Categories M rxx SD rxx observed correlation (robs) and the observed
Predictors standard deviation (SDobs) for measurement error
Aggregate measures 0.82 0.10 and measurement error variability, respectively,
Study habits 0.83 0.07 yields more accurate estimates of the relationship
Study skills 0.71 0.07 between two variables. Furthermore, such
Study attitudes 0.83 0.09 corrections permit us to evaluate if the variability
Study motivation 0.71 0.09 in observed correlations is due to systematic
Study anxiety 0.75 0.05 artifactual biases or if it reflects the existence of
Deep processing 0.68 0.09 substantive
Surface processing 0.64 0.09 TABLE3
Strategic processing 0.73 0.09 Reliability Artifact Distributions for SSHA and LASSI
Metacognitive skills 0.79 0.06 Subscales
Criterion
First-semester freshman GPA 0.83 0.02 Alpha Test–retest
Freshman GPA 0.83 0.02 distribution distribution
GPA 0.83 0.02 Scale Subscale M rxx SD rxx krel M rxx SD rxx krel
Note. SHSA 5 Survey of Study Habits and Attitudes; Mr xx 5 mean of SSHA Delay avoidance .82 .11 2 .72 .13 6
reliability distribution; SD rxx 5 standard deviation of reliability
SSHA Work methods .82 .07 2 .71 .10 6
distribution; krel5 number of independent reliability coefficients on
which distributions are based. SSHA Study habits .93 .00 2 .78 .09 6
SSHA Teacher approval .84 .05 2 .64 .14 6
SSHA Educational .77 .15 2 .69 .11 6
The reliability artifact distributions for the SSHA experience
and LASSI are presented in Table 3. SSHA Study attitudes .88 .06 2 .74 .12 6
For the cases in which subscale composites SSHA Study orientation .93 .03 2 .76 .11 7
were formed into overall scales, we calculated LASSI Attitude .72 .03 8 — — —
LASSI Motivation .76 .06 8 — — —
Mosier (1943) reliability estimates when subscale
LASSI Time management .79 .06 8 — — —
intercorrelations were available and used the
LASSI Anxiety .78 .05 8 — — —
mean of the subscale reliabilities if the
LASSI Concentration .80 .06 8 — — —
intercorrelations were not available. LASSI Information .77 .03 8 — — —
For each construct category, we weighted the processing
available reliability data for scores on each scale LASSI Selecting main .70 .04 8 — — —
by frequency to match the frequency with which ideas
scales occurred with the frequency of their LASSI Study aids .62 .11 8 — — —
corresponding reliability estimates. That is, LASSI Self-testing .74 .05 8 — — —
reliability information for scores on frequently LASSI Test strategies .75 .06 8 — — —
studied inventories such as the SSHA and LASSI Note. SHSA 5 Survey of Study Habits and Attitudes; LASSI 5
Learning and Study Skills Inventory; M r xx 5 mean of reliability
were included proportionately more often in the distribution; SD rxx 5 standard deviation of reliability distribution;
artifact distribution. krel5 number of independent reliability coefficients on which
In addition to the population correlation (r), this distributions are based.
study also provides estimates of the operational

validity of scores on the most commonly used moderators. Moreover, correcting SDobs for the
measures of study skills and study habits. occasionally massive differences in sample sizes
Operational validity refers to the test–criterion across studies yields a more accurate estimate of
correlation coefficient that has been corrected for whether or not the differences observed in the
unreliability in the criterion, but not in the literature are merely the result of sampling error.
predictor. Because admissions and counseling We also applied corrections for unreliability
decisions are made with an imperfectly reliable when computing variability estimates across the

correlations included in each meta-analysis. The variance in the correlations remains after
standard deviation of observed correlations corrections, it can be concluded that the
corrected for statistical artifacts is the residual relationships of study skills and study habits with
standard deviation (SDres). The standard deviation relevant criteria are positive across situations,
of the true score validities (SDr) describes the although the actual magnitude may vary
standard deviation associated with the true somewhat across settings. However, the
validity after variability due to sampling error, remaining variability may also be due to
unreliability in the predictor, unreliability in the uncorrected statistical artifacts, other
criterion, and range restriction have been methodological differences, and unidentified
removed. The magnitude of SDr is an indicator for moderators. All of these interpretations of SD r and
the presence of moderators. Smaller values the credibility interval are based on the
suggest that other variables are unlikely to assumption that the studies included in the meta-
substantially moderate the validity of scores on analysis are randomly sampled from a population
the predictor of interest. If all or a major portion of samples, situations, and instruments. Although
of the observed variance in a correlation is due to our database represents a very wide range of
statistical artifacts, one can conclude that the samples, situations, and instruments, the existing
relationship is constant or nearly so. The SD r was literature is a study-level convenience sample of
also used to compute a lower bound of the 90% convenience samples. Therefore, our estimates of
credibility interval, which is used as an indicator of variability may be over or under estimates.
the likelihood that the true relationship
generalizes across situations. If the lower 90% RESULTS
credibility value is greater than zero, one can
conclude that the presence of a relationship can Results for SSHA
be generalized to new situations (Hunter & The meta-analytic results for the SSHA are
Schmidt, 1990). In this metaanalysis, if the 90% presented in Tables 4, 5, and 6 and include meta-
credibility value is greater than zero, but analytic estimates of the observed
TABLE4
Meta-Analytic Results for the SSHA for Freshman GPA
Lower Upper
Reliability Subscale N k robs SDobs rop SDop r SDr 90% 90%
Test–retest Delay avoidance 4,163 20 .27 .09 .30 .06 .36 .07 .20 .40
Test–retest Work methods 4,163 20 .26 .11 .29 .09 .34 .11 .14 .44
Test–retest Study habits 4,642 23 .28 .12 .31 .11 .35 .12. .13 .49
Test–retest Teacher approval 4,163 20 .20 .09 .22 .07 .28 .08 .10 .36
Test–retest Educational acceptance 4,163 20 .30 .09 .33 .07 .39 .08 .21 .45
Test–retest Study attitudes 4,642 23 .26 .10 .29 .08 .33 .10 .16 .42
Test–retest Study orientation 5,500 33 .29 .13 .32 .11 .36 .13 .14 .50
Alpha Delay avoidance 4,163 20 .27 .09 .30 .06 .33 .07 .20 .40
Alpha Work methods 4,163 20 .26 .11 .29 .09 .32 .10 .14 .44
Alpha Study habits 4,642 23 .28 .12 .31 .11 .33 .12 .13 .49
Alpha Teacher approval 4,163 20 .20 .09 .22 .07 .24 .07 .10 .34
Alpha Educational acceptance 4,163 20 .30 .09 .33 .07 .37 .08 .21 .45
Alpha Study attitudes 4,642 23 .26 .10 .29 .09 .30 .09 .16 .42
Alpha Study orientation 5,500 33 .29 .13 .32 .11 .33 .12 .14 .50
Note. SHSA 5 Survey of Study Habits and Attitudes; k5 number of studies; r obs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation;
rop 5 operational validity; SD op 5 standard deviation of operational validity; r5 true score correlation; SD r 5 standard deviation of true score correlation; lower 90% 5
lower bound of 90% credibility interval based on operational validity; upper 90% 5 upper bound of 90% credibility interval based on operational validity; test–retest
5r and SDr based on test–retest reliability data; alpha 5r and SD r based on alpha reliability data.
TABLE5
Meta-Analytic Results for the SSHA for General GPA
Lower
Reliability Subscale N k robs SDobs rop SDop r SDr 90%
436 V
Test–retest Delay avoidance 5,601 32 .28 .11 .30 .09 .36 .10 .15 .45
Test–retest Work methods 5,601 32 .26 .13 .28 .12 .33 .14 .08 .48
Test–retest Study habits 6,259 40 .28 .14 .30 .13 .34 .15 .09 .51
Test–retest Teacher approval 5,651 33 .18 .10 .20 .08 .25 .10 .07 .33
Test–retest Educational acceptance 6,601 32 .29 .11 .31 .09 .38 .11 .16 .46
Test–retest Study attitudes 6,309 41 .24 .13 .26 .11 .31 .13 .08 .44
Test–retest Study orientation 12,250 83 .30 .22 .33 .22 .38 . .25 . !.03 .15 .69
Alpha Delay avoidance 5,601 32 .28 .11 .30 .09 34 10 .45
Alpha Work methods 5,601 32 .26 .13 .28 .12 .31 .13 .08 .48
Alpha Study habits 6,259 40 .28 .14 .30 .13 .33 .14 .09 .51
Alpha Teacher approval 5,651 33 .18 .10 .20 .08 .22 .08 .07 .33
Alpha Educational acceptance 6,601 32 .29 .11 .31 .09 .36 .11 .16 .46
Alpha Study attitudes 6,309 41 .24 .13 .26 .11 .26 .11 .08 .44
Alpha Study orientation 12,250 83 .30 .22 .33 .22 .34 .23 !.03 .69
Note. SHSA 5 Survey of Study Habits and Attitudes; k5 number of studies; r obs 5 sample size weighted mean observed correlation; SD obs 5
observed standard deviation; rop 5 operational validity; SDop 5 standard deviation of operational validity; r5 true score correlation; SD r 5
standard deviation of true score correlation; Lower 90% 5 lower bound of 90% credibility interval based on operational validity; Upper
90% 5 upper bound of 90% credibility interval based on operational validity; test–retest 5r and SD r based on test–retest reliability data,
alpha 5r and SDr based on alpha reliability data.
TABLE6
Meta-Analytic Results for the SSHA for Performance in Individual Classes
Lower Upper
Reliability Subscale N k robs SDobs rop SDop r SDr 90% 90%
Test–retest Delay avoidance 671 8 .20 .14 .20 .09 .24 .11 .05 .35
Test–retest Work methods 671 8 .23 .16 .23 .11 .27 .14 .05 .41
Test–retest Study habits 881 11 .23 .16 .23 .12 .26 .14 .03 .43
Test–retest Teacher approval 981 9 .13 .14 .13 .10 .17 .12 !.03 .29
Test–retest Educational acceptance 983 9 .12 .15 .12 .12 .14 .14 !.08 .32
Test–retest Study attitudes 1,145 11 .14 .15 .14 .11 .15 .12 .32
Test–retest Study orientation 1,915 17 .23 .15 .23 .11 .26 .13 !.04 .05 .41
Alpha Delay avoidance 671 8 .20 .14 .20 .09 .23 .10 .05 .35
Alpha Work methods 671 8 .23 .16 .23 .12 .25 .13 .05 .41
Alpha Study habits 881 11 .23 .16 .23 .12 .25 .13 .03 .43
Alpha Teacher approval 981 9 .13 .14 .13 .10 .14 .11 !.03 .29
Alpha Educational acceptance 983 9 .12 .15 .12 .12 .13 .14 !.08 .32
Alpha Study attitudes 1,145 11 .14 .15 .14 .11 .14 .11 .32
Alpha Study orientation 1,915 17 .23 .15 .23 .11 .24 .12 !.04 .05 .41
standard deviation of true score correlation; Lower 90% 5 lower bound of 90% credibility interval based on operational validity; Upper
90% 5 upper bound of 90% credibility interval based on operational validity; test–retest 5r and SD r based on test-retest reliability data;
alpha 5r and SDr based on alpha reliability data.
correlations, the operational validities (corrected 5 20). The range of operational validities was
for unreliability in the criterion), and the similar for the general GPA criterion, ranging from
population correlation (corrected for unreliability r5 .20 for teacher approval (N 5 5,651, k 5 33) to
in both the criterion and the SSHA predictor). r5 .33 for study orientation (N 5 12,250, k 5 83).
Given that the literature provides estimates of The smallest operational validities were observed
both test–retest reliability and measures of for performance in individual courses, partly
internal consistency, we performed two separate because we did not correct for the unreliability of
sets of analyses using two different reliability individual grades. Using the test–retest reliability
distributions. The SSHA operational validities for estimates for the first-year GPA criterion, we
freshman GPA were moderately large, ranging found that the population correlation estimates
from r5 .22 for teacher approval (N5 4,163, k5 20) ranged from r 5 .28 for teacher approval (N 5
to r5 .33 for educational acceptance (N 5 4,163, k 4,163, k5 20) to r5 .39 for educational acceptance

(N5 4,163, k 5 20). For the general GPA criterion, for motivation (N5 961, k5 6). A similar range of
an almost identical range of population validities was observed for the general GPA
correlation estimates were observed, ranging criterion. The lowest validity of r5 .16 was
from a low of .25 for teacher approval (N 5 5,651, observed for informational processing (N 5 3,287,
TABLE7
Meta-Analytic Results for the LASSI for First-Year GPA, and General GPA
Freshman GPA General GPA
Lower Upper Lower Upper

Subscale N k robs SDobs rop SDop r SDr 90% 90% N k robs SDobs rop SDop r SDr 90% 90%
Attitude 961 6 .23 .14 .25 .13 .30 .15 .04 .46 3,287 16 .21 .12 .23 .10 .27 .12 .07 .39
Motivation 961 6 .31 .12 .34 .10 .40 .12 .18 .50 3,287 16 .30 .13 .34 .12 .38 .13 .14 .54
Time 961 6 .23 .10 .25 .08 .28 .09 .12 .38 3,287 16 .21 .09 .23 .07 .26 .07 .11 .35
Management
Anxiety 961 6 .15 .14 .16 .13 .18 .14 !.05 . 37 . 3,287 16 .18 .10 .19 .09 .22 .10 .04 .34
Concentration 961 6 .23 .14 .25 .12 .28 .14 05 45 3,287 16 .24 .10 .26 .08 .29 .08 .13 .39
Information 961 6 .13 .11 .14 .08 .16 .08 .01 .27 3,287 16 .14 .10 .16 .08 .18 .10 .03 .29
processing
Selecting main 961 6 .16 .11 .17 .08 .20 .10 .04 .30 3,287 16 .15 .09 .17 .07 .20 .08 .05 .29
ideas
Study aids 961 6 .16 .05 .17 .00 .22 .00 .17 .17 3,287 16 .14 .07 .16 .02 .20 .02 .13 .19
Self-testing 961 6 .24 .06 .27 .00 .31 .00 .27 .27 3,287 16 .19 .08 .21 .04 .24 .04 .14 .28
Test strategies 961 6 .23 .15 .25 .13 .25 .13 .04 .45 3,287 16 .24 .11 .27 .10 .31 .12 .11 .43
Note. LASSI 5 Learning and Study Skills Inventory; k5 number of studies; r obs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation;
rop 5 operational validity; SD op 5 standard deviation of operational validity; r5 true score correlation; SD r 5 standard deviation of true score correlation; lower 90% 5
lower bound of 90% credibility interval based on operational validity; upper 90% 5 upper bound of 90% credibility interval based on
k 5 33) to a high of r5 .38 for educational k 5 16), with the highest validity of r5 .34 for the
acceptance (N 5 5,601, k 5 32) and for the motivation subscale (N 5 3,287, k 5 16). Estimates
aggregate measure of study orientation (N 5 of the population correlation ranged from r5 .16
12,250, k 5 83). The population correlation for information processing to r5 .40 for
estimates for the relationship between SSHA motivation for freshman GPA and from r5 .18 for
scales and individual class grades were lower, information processing to r5 .38 for motivation
ranging from r5 .14 for educational acceptance (N for the general GPA criterion.
5 983, k 5 6) to r5 .27 for work methods (N 5 671,
k 5 8). The observed internal consistency Results for Other Scales
estimates were slightly higher than the observed
The meta-analytic results for scales used less
test–retest reliability estimates, resulting in the
widely than the LASSI and SSHA are presented in
population correlation estimates being slightly
Table 8. We used broad academic performance as
lower when using the artifact distributions based
the criterion for each of these analyses and
in internal consistency measures.
included both GPAs and performance in individual
classes. Validity coefficients for the Inventory of
Results for LASSI Learning Processes were low to moderate,
The meta-analytic results for the LASSI are ranging from a low of r5 .11 for the study
presented in Table 7 and include meta-analytic methods subscale (N 5 1,900, k 5 11) to r5 .29 for
estimates of the observed correlations, the synthesis analysis (N 5 1,900, k 5 11). A similar
operational validities (corrected for unreliability range of validity
in the criterion), and the population correlations operational validity.
(corrected for unreliability in both the criterion

and the LASSI predictor). coefficients was observed for the Study Attitudes
The operational validities for freshman GPA in and Methods Survey. The weakest relationship
the LASSI subscales ranged from r5 .14 for with academic performance in this scale was
information processing (N 5 961, k5 6) to r5 .34 found in the manipulation subscale (r5!.04, N 5
438 V
880, k5 7), and the strongest relationship was r5 .28 for study habits (N 5 23,390, k 5 102), r5 .31
found in the academic interest subscale (r5 .24, N for study attitudes (N 5 7,211, k 5 37), and r5 .30
5 880, k 5 7). The validity coefficients of the study for study motivation (N 5 6,157, k 5 25). The
methods scale were also low to moderate in size validity coefficients for the deep, surface, and
for all subscales, ranging from r5 .14 for the lack strategic approaches were uniformly low with all
of distractions subscale (N 5 1,650, k 5 6) to r5 .31 credibility intervals including zero.
for the motivation subscale (N 5 1,650, k 5 6). A
high correlation was observed between academic
performance and the Study Habits and Attitudes Relationships With Traditional Predictors of
Inventory, a precursor to the SSHA, with an Academic Performance
operational validity of r5 .54 (N5 1,015, k5 9). No Tables 13, 14, and 15 present meta-analytic
reliability data was available for this inventory, estimates of the relationship that SHSA constructs
and the observed correlations could thus not be and the SSHA exhibit with both high school GPA
disattenuated for predictor unreliability. Another (HSGPA) and scores on college admissions tests
inventory in which we found a high validity such as the SAT and ACT. A lack of sufficient data
coefficient of r5 .48 but no available reliability meant that scale relationships with HSGPA and
information is the Tyler-Kimber Study Skills Test admissions test scores could only be examined at
(N5 752, k5 5). Finally, the validity of scores on the construct level and for the SSHA.
the Study Process Questionnaire was uniformly The meta-analytic relationships between the
low with the strongest observed validity SHSA constructs and admissions test scores were
coefficient of r5!.14 for the surface strategy generally low, with the
subscale (N 5 1,450, k 5 7).
Results for Time Spent Studying

The meta-analytic results relating to the amount
of time spent studying by students are presented
in Table 9. Across all three analyses, the validity
coefficients were low but positive. The size of the
relationships ranged from a low of r5 .01 for
individual grades to r5 .15 for the overall GPA
criterion (N 5 19,042, k 5 51) to a high of r5 .21
for the freshman GPA criterion (N 5 4,152, k 5 11).
Results for Construct Categories
The meta-analyses for the relationship between
the 10 SHSA constructs and the four academic
performance criteria are presented in Tables 10,
11, and 12, respectively. Analyses were not
possible for all criterion–predictor combinations
given the lack of available data. Validity
coefficients were highest for the aggregate
measure category, ranging from a low of r5 .22
for performance in individual classes (N 5 1,655, k
5 13) to a high of r5 .41 for general GPA (N 5
18,517, k 5 107). Relatively large validity
coefficients were also observed for the study
skills, study habits, study attitudes, and study
motivation construct categories. Validity
coefficients for these four constructs with general
GPAwere r5 .33 for study skills (N5 24,547, k5 87),

TABLE8
Meta-Analyses of the Relationship Between Academic Performance and SHSA Inventories
Lower Upper
Scale Subscale N k robs SDobs rop SDop r SDr 90% 90%
ILP Synthesis analysis 1,900 11 .22 .16 .24 .16 .29 .18 !.02 .50
ILP Study methods 1,900 11 .09 .13 . .10 .13 . .11 . .14 !.11 .08 .31
ILP Fact retention 1,900 11 .17 09 .18 06 25 .08 .28
ILP Elaborative processing 1,900 11 .17 .12 .18 .10 .24 .18 .02 .34
SAMS Academic interest 880 7 .18 .12 .20 .08 .24 .10 .08 .41
SAMS Academic drive 880 7 .11 .12 .12 .09 .14 .10 !.03 .31
SAMS Study methods 880 7 .13 !.12 .13 . . .10 . .13 !.03 .39
SAMS Study anxiety 880 7 !.07 16 15 !.14 .15 18 !.16 .17 .12
SAMS Manipulation 880 7 .13 .11 .13 !.44 .12
!.03 .49 !.07 !.09
SAMS Alienation 880 7 .16 .14 . .17 !.30 .24
SHAI Total 1,015 9 .04 !.03 .54 00 !.04 .54 .00 .54
!.32 .54
Study Habits Inventory Total 2,890 20 .21 .11 .24 .08 .27 .10 .11 .37
Study Methods Scale Study methods 1,650 6 .21 .06 .23 .00 .27 .00 .23 .23
Study Methods Scale Motivation 1,650 6 .21 .04 .23 .00 .31 .00 .23 .23
Study Methods Scale Lack of distractions 1,650 6 .11 .06 .12 .02 .14 .03 .09 .15
Study Methods Scale Exam technique 1,650 6 .18 .03 .19 .00 .25 .00 .19 .19
Taylor–Kimber Total 752 5 .44 .10 .48 .08 .48 .08 .35 .61
SPQ Surface motivation 1,447 7 !.05 .12 !.05 .11 !.08 .16 !.23 .
SPQ Surface strategy 1,450 7 !.10 .08 !.10 .05 !.14 .07 !.18 13 !.0
SPQ Surface approach 2,524 14 .10 .07 . .09 2
SPQ Deep motivation 1,160 6 !.09 .14 !.10 13 !.12 .16 !.22
.02
SPQ Deep strategy 1,165 6 .05 .10 .06 .08 .07 .10 !.15
.27
SPQ Deep approach 2,531 14 .03 .15 .04 .14 . .05 .16 !.09 .17
SPQ Achievement motivation 1,446 7 .06 . .14 . .07 14 .08 .17 .
!.16 .30
SPQ Achievement strategy 1,451 7 07 . 13 .08 .12 .10 . 14
.31
SPQ Achievement approach 2,239 13 07 .18 .08 .18 09 .21 !.15
.28
.074 .07 .09 !.12 .37
!.23
Note. No reliability data was available for the SHAI and the Tyler–Kimber and no corrections for scale unreliability where therefore conducted. SHSA 5 Survey of
Study Habits and Attitudes; k 5 number of studies; r obs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; rop 5 operational
validity; SDop 5 standard deviation of operational validity; r5 true score correlation; SD r5 standard deviation of true score correlation; lower 90% 5 lower bound of
90% credibility interval based on operational validity; upper 90% 5 upper bound of 90% credibility interval based on operational validity. ILP 5 Inventory of Learning
Processes; SHAI 5 Study Habits and Attitudes Inventory; Taylor–Kimber 5 Taylor–Kimber Study Skills Test; SPQ 5 Study Process Questionnaire.
21). Similar weak relationships were also
observed for the relationships with HSGPA. The
strongest observed correlation with HSGPA was
r5 .26 for time spent studying (N 5 2,326, k 5 3).
The low relationships between SHSA constructs
and traditional cognitive predictors of college
performance suggest that SHSA predictors would
explain significant and meaningful variance in
college academic performance above and beyond
that explained by admissions criteria such as
HSGPA and SAT/ ACT scores.
The observed relationship between the SSHA
subscales and admissions test scores were also
low, ranging from r5 .00 for delay avoidance (N 5
3,662, k 5 14) to .24 for work methods
TABLE9
440 V
Meta-
AnalysesoftheRelationshipBetweentheAmountofTimeSpentStudyingandFreshmanGPA,OverallGPA,andPerformanc
ein
Individual Classes
Criterion N k robs SDobs rop SDop r SDr Lower 90% Upper 90%
Freshman GPA 4,152 11 .19 .14 .21 .14 .21 .14 !.02 .44
Overall GPA 17,242 50 .15 .20 .16 .21 .16 .21 !.21 .21
Performance in 1,615 17 .01 .11 .01 .05 .01 .05 .09
!.07
individual classes
Note. k5 number of studies; r obs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; r op 5 operational
validity; SDop 5 standard deviation of operational validity; r5 true score correlation; SD r 5 standard deviation of true score correlation;
lower 90% 5 lower bound of 90% credibility interval based on operational validity; upper 90% 5 upper bound of 90% credibility interval
based on operational validity.
TABLE10
Meta-Analyses for the Relationships Between the 10 SHSA Constructs and First-Semester GPA and Freshman GPA
First-Semester GPA Freshman GPA
Lower Lower
SDop Upper Upper
Construct N k SDobs rop r SDr 90% 90%N k robs SDobs rop SDop r SDr 90% 90%
Aggregate 5,581 37 . .13 .37 .12 .40 .13 .17 .57 6,613 44 .32 .13 .35 .12 .39 .13 .15 .55
measures 33
Study skills 3,751 18 . .15 .28 .14 .34 .17 .05 .51 7,782 34 .25 .15 .28 .15 .33 .18 .03 .53
26
Study habits 6,922 34 . .13 .26 .12 .28 .13 .06 .46 9,693 47 .22 .13 .24 .12 .27 .13 .14 .44
24
Study — — — — — — — — — — 3,435 17 .27 .14 .29 .13 .32 .14 .08 .50
attitudes
Study — — — — — — — — — — 1,953 13 .30 .11 .33 .09 .39 .11 .18 .48
motivation
Study anxiety — — — — — — — — — — 1,210 9 !.14 .15 !.15 .14 !.18 .15 !.43 . .07
Deep — — — — — — — — — — 2,414 8 .16 .11 .18 .11 .22 .13 00 .36
approach
standard deviation of true score correlation; lower 90% 5 lower bound of 90% credibility interval based on operational validity; upper
90% 5 upper bound of 90% credibility interval based on operational validity.
(N5 3,662, k5 14). The relationship for the overall analytic estimates of the relationship between
score (study orientation) and admissions test SATscores and HSGPA and freshmen GPA (Hezlett
scores was also low at r5 .16 (N 5 6,710, k 5 23). et al., 2001). The operational validity of .35 for
This together with the high validity of scores on SAT scores and of .40 for HSGPA was used for
the SSHA discussed earlier suggests that this these calculations.
inventory would be particularly useful in For the SHSA constructs, the highest
predicting academic performance above and incremental R values are for the aggregate
beyond traditional cognitive predictors of college measures of study skills, study habits, study
performance. attitudes, and study motivation, with incremental
Rs for these four constructs ranging from .04 to .
12. For the SSHA subscales, incremental Rs
Incremental Validity ranged from .04 for the teacher approval subscale
Also included in Tables 13, 14, and 15 is the to .11 for both delay avoidance and educational
incremental R provided by SHSA constructs and acceptance.
SSHA subscales in predicting freshman GPA over
and above both HSGPA and admissions test
scores. Incremental validity was calculated using
hierarchical linear regression and existing meta-

Relationship With Personality Constructs study attitudes. A lack of information in the
Table 16 presents the relationships between the summarized literature regarding the reliability of
eight personality constructs and study habits and the utilized personality scales prevented us from
cor-
TABLE11
Meta-Analyses for the Relationships Between the 10 SHSA Constructs and General GPA
Construct N k robs SDobs rop SDop r SDr Lower 90% U
Aggregate measures 18,517 107 .33 .13 .37 .12 .41 .13 .17
Study skills 24,547 87 .25 .18 .28 .18 .33 .22 !.02 .00
Study habits 23,390 102 .23 .15 .25 .15 .28 .16
Study attitudes 7,211 37 .26 .11 .28 .09 .31 .10 .13
Study motivation 6,157 25 .23 .13 .25 .13 .30 .15 .04
Study anxiety 3,943 22 !.17 .12 !.19 .10 !.21 .12 !.41
Deep approach 4,238 28 .12 .18 .13 .17 .16 .21 !.15
Surface approach 6,224 32 .01 .15 .01 .14 .01 .17
Achievement approach 4,184 27 .18 .18 .21 !.22
.03 .03 .04
Metacognition 1,915 7 .18 .15 .19 .15 .22 .17 !.27
!.06
observed standard deviation; rop 5 operational validity; SDop 5 standard deviation of operational validity; r5true score correlation; SD r 5
TABLE12
Meta-Analyses for the Relationships Between the 10 SHSA Constructs and Performance in Individual Classes
Construct N k robs SDobs rop SDop r SDr Lower 90% U
Aggregate measures 1,655 13 .18 .10 .20 .05 .22 .06 .12
Study skills 2,175 21 .10 .20 .11 .18 .12 .22 !.19
Study habits 3,628 34 .18 .20 .18 .17 .20 .19 !.10 .05
Study attitudes 1,855 19 .15 .12 .15 .06 .16 .07
Study motivation 2,158 11 . .14 . .12 . .15 !.03
Study anxiety 704 8 17 !.09 .23 17 !.09 .20 20 !.11 .23 !.42 .07
Deep approach 3,025 21 .15 .20 .15 .05 .18 .06
Surface approach 2,134 17 !.04 .13 !.04 .09 !.05 .12 !.19
Achievement approach 1,608 10 .02 .15 .02 .12 .02 .14 !.18
Metacognition 1,978 9 .08 .17 .08 .16 .09 .17
!.18
observed standard deviation; rop 5 operational validity; SDop 5 standard deviation of operational validity; r5true score correlation; SD r 5
recting our estimates for unreliability. Only DISCUSSION
sample-size weighted correlations are therefore
presented. Study attitudes exhibited relatively The results provided in this article have a number
strong relationships with neuroticism (r obs 5!.40), of important practical and theoretical
openness (robs 5 .30), conscientiousness (robs 5 .30), implications for the admissions process and
an external locus of control (r obs 5!.28), and educational psychology. The most immediate
achievement motivation (robs 5 .20). The practical implication is that certain SHSA
relationship of personality constructs with study constructs need to be given a larger role in
habits was generally weaker, with the strongest admissions decision. They arguably represent the
relationships being found for achievement largest increase in predictive power observed in
motivation (robs 5 .35), conscientiousness (robs 5 . the literature beyond the mainstays of test scores
29), and self-concept (robs 5 .21). and prior grades. Stated differently, aspects of
SHSAs best explain why some succeed despite
predictions of failure and why some fail despite
predictions of success. Our greatest challenge as a
442 V
field will be finding ways to assess these three ways. First, we have illustrated that many of
characteristics in high stakes operational settings. the examined SHSA constructs are strongly
On a theoretical level, our results offer broad related to academic performance. Study skills,
support for our model of how individual study attitudes, study habits, and study
difference factors affect academic performance in motivation exhibited particularly strong and
college more so than other noncognitive robust relationships with academic performance
constructs. Support for the model is manifest in in college. Second, we were able to illustrate that,
TABLE13
Relationship of SHSA Constructs and Their Incremental Validity Over High School GPA
Construct N k robs SDobs rop SDop r SDr Lower 90% Upper 90% DR DR2
Aggregate measures 2,230 6 .20 .06 .20 .04 .22 .04 .13 .27 .09 .08
Study skills 3,916 10 .10 .19 .10 .18 .12 .21 !.20 .00 .40 .07 .06
Study habits 3,126 6 .13 .09 .13 .08 .14 .10 .26 .04 .03
Study attitudes 494 5 .00 .12 .00 .06 .01 .06 !.10 .11 .10 .09 .08
Study motivation 1,819 4 .14 .05 .14 .02 .16 .02 .17 .09 .08
Study anxiety 169 2 .04 .06 .04 .02 .05 .00 .01 .07 .02 .02
Deep approach — — — — — — — — — — — —
Surface approach — — — — — — — — — — — —
Achievement approach 169 2 !.12 .04 !.12 .00 !.14 .00 !.12 .12 .01 .01
Metacognition — — — — — — — — — — — —
Time spent studying 2,369 3 .26 .12 .26 .11 .26 .11 .08 .44 .02 .02
Note. Incremental validity is calculated using the operational validity coefficients. SHSA 5 Survey of Study Habits and Attitudes; k5
number of studies; robs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; rop 5 operational validity;
SDop 5 standard deviation of operational validity; r 5true score correlation; SD r 5 standard deviation of true score correlation; lower 90% 5
operational validity.
TABLE14
Relationship of SHSA Constructs and Their Incremental Validity Over College Admission Tests
Construct N k robs SDobs rop SDop r SDr Lower 90% Upper 90% DR DR2
Aggregate measures 7,422 25 .17 .11 .17 .09 .18 .10 .02 .32 .11 .09
Study skills 6,297 21 .23 .26 .23 .25 .27 .30 !.18 .64 .06 .05
Study habits 7,713 31 .06 .11 .06 .10 .06 .10 !.10 .05 .23 .06 .
Study attitudes 7,016 36 .12 .07 .12 .04 .13 .05 .19 .08 05 .
06
Study motivation 2,168 7 .07 .06 .07 .03 .08 .03 .02 .12 .12 .10
Study anxiety 344 4 .04 .27 .04 .24 .04 .28 !.35 .07 .44 .06 .05
Deep approach 1,054 5 .07 .05 .07 .00 .09 .00 .07 .02 .01
Surface approach 852 4 .08 .15 .08 .13 .09 .16 !.13 .08 .29 .00 .00
Achievement approach 1,025 6 .13 .08 .13 .03 .15 .03 .18 .00 .00
Metacognition 408 2 .58 .08 .25 .05 .28 .05 .17 .33 .02 .01
Time spent studying 2,394 5 !.02 .08 !.02 .06 !.02 .06 !.12 .08 .06 .05
Note. Incremental validity is calculated using the operational validity coefficients. SHSA 5 Survey of Study Habits and Attitudes; k5
number of studies; robs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; rop 5 operational validity;
SDop 5 standard deviation of operational validity; r 5true score correlation; SD r 5 standard deviation of true score correlation; lower 90% 5
operational validity.
with the exception of study skills, SHSA constructs accounted for by traditional predictors such as
are only weakly related to measures of general admissions test scores and academic
cognitive ability and to prior performance in high performance in high school.
school. This finding not only suggests that the Third, we were able to illustrate that study
acquisition of sound SHSAs is not dependent on attitudes and study habits are partially influenced
high cognitive ability, but that SHSA scores by students’ personality traits. Personality
explain a large amount of variance in academic constructs such as conscientiousness,
performance in college above the variance neuroticism, achievement motivation, and

external locus of control exhibited meaningful constructs, comparable with those of admissions
relationships with study attitudes and/or study tests such as the SAT, suggest that SHSA
habits. These observed relationships are in line constructs can indeed be positioned as direct
with findings that have linked personality traits to determinants of the acquisition of declarative and
desirable habits and attitudes in other domains procedural knowledge in a college setting. Our
including health habits (e.g., Bogg & Roberts, findings regarding the relationship between
2004; Roberts, Kuncel, Shiner, Caspi, & Goldberg, personality and study attitudes and study habits
2007; Schneider & Busch, 1998) and work habits suggeststhat the effect of certain personality
(Barrick, Mount, & Strauss, 1993; Mount, Witt, & traits on academic performance may be partially
Barrick, 2000). mediated through better study attitudes and
We were unable to test all aspects of our study habits, as indicated by our model. The
individual differences model of academic moderate strength of the relationships between
performance due to a lack of available evidence personality and both study habits and study
in the literature, but these three findings are habits coupled with the lack of a relationship
supportive of important components of the between study habits and attitudes and cognitive
overall model. The high validities of SHSA ability does, however, suggest to us that our
model does not fully
TABLE15
Meta-Analytic Correlations Between SSHA Subscales and Scores on Cognitive Admissions Tests
SSHA subscale N k robs SDobs rop SDop r SDr Lower 90% Upper 90% D
Delay avoidance 3,662 14 .00 .09 .00 .06 .00 .07 !.15 .06 .15 .1
Work methods 3,662 14 .24 .11 .24 .09 .28 .10 .42 .1
Study habits 3,662 14 .14 .10 .14 .08 .16 .09 !.02 .00 .30 .0
Teacher approval 3,662 14 .12 .07 .12 .04 .16 .05 .24 .0
Educational acceptance 3,662 14 .11 .08 .11 .05 .14 .07 !.02 .00 .24 .1
Study attitudes 3,662 14 .13 .08 .13 .06 .15 .07 .26 .0
Study orientation 6,710 23 .16 .11 .16 .10 .18 .11 !.02 .34 .0
Note. Incremental R refers to the incremental R of each subscale over admissions test scores and is calculated using the operational
validity coefficients for firstyear GPA (or general GPAwhen coefficients are not available for first-year GPA). SHSA 5 Survey of Study Habits
and Attitudes; k5 number of studies; robs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; rop 5
operational validity; SDop 5 standard deviation of operational validity; r 5true score correlation; SD r 5 standard deviation of true score
correlation; lower 90% 5 lower bound of 90% credibility interval based on operational validity; upper 90% 5 upper bound of 90%
credibility interval based on operational validity.
TABLE16
Meta-Analytic Correlations Between Study Habits and Study Attitudes and Eight Personality Constructs
Study habits Study
attitudes
Lower Upper Low
Construct N k robs SDobs SDr 90% 90% N k robs SDobs SDr 90%
Achievement 923 9 .35 .16 .13 .14 .57 465 4 .20 .09 .03 .1
motivation
Neuroticism 1,152 9 !.11 .35 .34 !.67 .45 292 3 !.40 ! .11 .07 !.52
External locus of 1,658 14 !.16 .19 .16 !.42 .10 1,026 7 .28 .10 .06 !.38
control
Internal locus of 610 6 !.06 .13 .13 !.30 .13 501 4 .03 .06 .00 .0
control
Extroversion 1,429 6 !.11 .09 .06 !.21 . !.01 . 881 3 !.14 .03 .00 !.14
Openness 1,020 4 .08 .08 .05 00 16 1,700 5 .30 .25 .12 1
Contentiousness 1,194 5 .29 .13 .11 .11 .47 891 4 .30 .05 .00 .3
Self-concept 767 8 .21 .07 .00 .21 .21 372 5 .13 .06 .00 .1
Note. k5 number of studies; robs 5 sample size weighted mean observed correlation; SD obs 5 observed standard deviation; SDr5 standard
deviation of true score correlation; lower 90% 5 lower bound of 90% credibility interval based on operational validity; upper 90% 5 upper
bound of 90% credibility interval
based on operational validity.
444 V
account for the factors that determine the of scores on different inventories are likely to
acquisition of sound study habits and study assist in the process of choosing appropriate
attitudes. inventories when attempting to assess and
Our results also showed that general cognitive diagnose learning difficulties in college students.
ability is moderately related to study skills (r5 . Scores on some inventories, such as the LASSI
27), which is in line with findings from the (C.E. Weinstein & Palmer, 2002) and the SSHA
organizational literature that show that cognitive (W.F. Brown & Holtzman, 1967), exhibited high
ability facilitates the acquisition of knowledge and validities across multiple samples and appear to
skills (e.g., Hunter & Hunter, 1984). The effect of be more useful in understanding the academic
general cognitive ability on academic performance of college students than are other
performance therefore appears to be partly inventories such as the Study Process
mediated through the acquisition of good study Questionnaire (e.g., Biggs et al., 2001) and the
skills, although a strong direct effect of cognitive Study Attitudes and Methods Survey (e.g., W.S.
ability on academic performance remains. Zimmerman et al., 1977). Tables 17 and 18
Our results also provide widely varying levels of highlight the content of the subscales of the SSHA
support for the different theoretical approaches and LASSI, respectively.
that have informed the manner in which SHSA Our findings also indicate a positive direction
constructs are conceptualized and assessed. for changes in the factors that are considered in
Constructs such as study skills, study attitudes, the college admissions process. The question is
study habits, and study motivation exhibited how to best integrate SHSA factors in the
relatively strong relationships with academic admissions process. Despite the high predictive
performance. Validities for these four constructs validity of scores on inventories such as the SSHA
for the freshman GPA criterion ranged from .27 and LASSI, we must caution against their use in
for study habits to .39 for study motivation. Other high-stakes admissions contexts due to the
constructs such as the deep, surface, and vulnerability of self-report inventories to faking
strategic approaches exhibited considerably and socially desirable responding. The precise
lower validities. The general GPA criterion scores degree of this vulnerability to faking is not known,
on both the surface approach (r5 .01) and but it could easily be established by investigating
strategic approach (r5 .04) exhibited no validity. the validity of scores on various inventories under
The validity of the depth-of-processing theoretical applied conditions when completed by both self
perspective is therefore called into question. and objective others. Rather, we hope that these
Some additional practical implications and results may act as a spur for the development of
benefits are also evident. Our findings are likely to psychometrically sound rating forms that could
assist colleges and college counselors in be used by high school teachers, principals, and
identifying and assisting those students who are counselors. Such ratings would reduce the impact
likely to struggle academically. Well-constructed of socially desirable response patterns and also
SHSA inventories could easily be administered to allow SHSA information about college applicants
incoming freshmen in order to identify students to be used in an admissions context or be used to
who may benefit from further training in effective identify at-risk freshman college students who
study techniques. Not only do our results focus may benefit from counseling and other forms of
attention on the apparent need for college academic assistance. If scores on these ratings
students to have sound study skills, habits, and forms were to illustrate validities in an applied
attitudes, but our findings regarding the validity selection context that are comparable
TABLE17
Scale Descriptions and Sample Items of the Survey of Study Habits and Attitudes
Subscale Content Sample item
Delay avoidance The degree to which the student is prompt in completing I put off writing themes, reports, term papers,
academic assignments. etc.
until the last minute.
Work methods The degree to which the student has an effective study I keep my place of study businesslike and cleared
procedure and efficiency in completing academic of unnecessary or distracting items such as
assignments. pictures, letters, and mementos.

Study habits Combination of delay avoidance and work methods that
reflects the student’s academic behavior.
Teacher approval The degree to which the student has a favorable opinion of My teachers succeed in making their s
teachers and their classroom behavior and methods. interesting and meaningful to me.
Educational The degree to which the student approves of the objectives, I feel that it is not worth the time, m
acceptance practices, and requirements of the educational institution. effort that one must spend to get
education.
Study attitudes Combination of teacher approval and educational acceptance
that reflects the student’s academic attitude.
Study orientation Combination of study habits and study attitudes that reflects
the student’s study habits and study attitudes.
with those of the inventories discussed in this article, then the One final notable finding is that SHSA
constructs exhibit utility of college admissions systems would likely witness sub- near-zero
relationships with high school academic performance stantive improvements (as indexed by the
proportion of correct despite being strongly related to college academic performance. to incorrect
admissions decisions). This finding may appear to be counterintuitive, as factors that
TABLE18
Scale Descriptions and Sample Items of the Learning and Study Skills Inventory
Subscale Content Sample item
Attitude Assesses the student’s attitudes and interest in college and I feel confused and undecided as to what my
academic success. educational goals should be.
Motivation Assesses the student’s diligence, self-discipline, and When work is difficult I either give up or stud
willingness to exert the effort necessary to successfully easy parts.
complete academic requirements.
Time Assesses the student’s application of time management I only study when there is the pressure of a te
management principles to academic situations.
Anxiety Assesses the degree to which the student worries about Worrying about doing poorly interferes with
school and their academic performance. concentration on tests.
Concentration Assesses the student’s ability to direct and maintain I find that during lectures I think of other thin
attention on academic tasks. not really listen to what is being said.
Information Assesses how well the student can use imagery, verbal I translate what I am studying into my own w
processing elaboration, organization strategies, and reasoning
skills as learning strategies to help build bridges
between what they already know and what they are
trying to learn and remember.
Selecting main Assesses the student’s skill at identifying important Often when studying I seem to get lost in d
ideas information for further study from less important can’t see the forest for the trees.
information and supporting details.
Study aids Assesses the student’s use of supports or resources to help I use special help, such as italics and headings
them learn or retain information. in my textbook.
Self-testing Assesses the student’s use of reviewing and I stop periodically while reading and mentally
comprehension monitoring techniques to determine or review what was said.
their level of understanding of the information learned.
Test strategies Assesses the student’s use of test preparation and test In taking tests, writing themes, etc. I fi
taking strategies. misunderstood what is wanted and lose poin
of it.
Note. Adapted from Weinstein & Palmer (2002).
are important in determining academic substantive differences in the nature of academic
performance in one domain (college) should also performance in these two settings. The college
be important in another domain (high school), academic environment is not only associated with
but we identify three possible reasons for the lack an increased levels of both quantity and difficulty
of a relationship between SHSA constructs and in academic assignments, but also with a lower
HSGPA. First, SHSAs may be better predictors of level of academic structure and a subsequent
college grades than of HSGPA because of increase in the amount of personal responsibility
446 V
that students must exercise to meet these examination of the relationship among
academic challenges (Larose, Bernier, & inventories or constructs that would shed light on
Tarabulsy, 2005). Effective study habits and study the discriminant validity of these 10 constructs.
skills therefore gain in importance. Most researchers in this field rely on individual
Second, colleges expend significant resources inventories that do not capture the full range of
on classes and workshops intended to help new constructs discussed in this article, making it
students acquire appropriate study skills and impossible to construct a meta-analytic construct
study attitudes, and many of these programs intercorrelation matrix. For example, despite
appear to be successful at improving students’ their widespread use in empirical investigations,
study skills and study habits (see Hattie et al., we are aware of only one study (Cole, 1988) that
1996, for a review of these programs). has examined the relationship between the SSHA
Differences in these newly acquired skills and and LASSI. Until more such studies have been
habits are likely to be reflected in students’ future completed, the important issue of construct
academic performance but not in their prior redundancy cannot be addressed.
academic performance. The lack of available validity evidence
Third, students in each of the samples included furthermore did not allow us to estimate the
in our metaanalyses were typically drawn from a strength of the relationships of SHSA constructs
single college, but they originally attended a wide and inventories with other important college
variety of high schools. Differences in grading outcomes, such as persistence in college. We
standards across these high schools would believe that the SHSA literature would benefit
substantially attenuate the observed correlations from greater attention to this relationship and to
between HSGPA and scores on SHSA measures the relationship between SHSAs and various
within any individual sample of college students, nonacademic college outcomes such as
as a HSGPA of, say, 3.0 in a school with adjustment, health behaviors, and stress. Our
substantial grade inflation would represent a results also show that study habits and study
significantly different level of educational attitudes are unrelated to cognitive ability and are
attainment than would a HSGPA of 3.0 in a school only moderately related to certain personality
with little or no grade inflation. These two constructs. Future research should attempt to
students are likely to have substantially different establish what other factors might contribute to
levels of SHSAs. Such differences in grading the development of effective study habits and
standards across high schools and their attitudes.
attenuating effect on correlations of HSGPA with Future research should also more closely
other variables such as socioeconomic status, examine the validity of scores on depth-of-
SATscores, and college grades have been well processing inventories (e.g., Study Process
documented in the educational literature (e.g., Questionnaire) that we’ve shown to be largely
Bassiri & Schultz, 2003; Rubin & Stroud, 1977; invalid with regard to academic performance as
Willingham, Pollack, & Lewis, 2002; Zwick & assessed by GPA and grades in individual classes.
Green, 2007). Bassiri and Schultz, for example, It is possible that scores on these inventories may
used ACT assessment test scores to adjust HSGPA exhibit more substantive validities for other
for different grading standards and showed that important academic outcomes (e.g., graduation,
HSPGA adjusted in this manner predicted retention, time to degree completion), and we
freshman GPA significantly better than did therefore do not recommend that these
unadjusted HSPGA. constructs and inventories be discarded. Rather,
we recommend further psychometric
LIMITATIONSANDFUTURERESEARCH development of these inventories and that
researchers and college counselors may benefit
This article has effectively summarized the from a greater reliance on those inventories (e.g.,
available validity evidence for scores on both LASSI, SSHA) that appear to capture the most
individual SHSA inventories and for SHSA criterionrelevant variance until the validity of
constructs. However, due to a lack of available depth-of-processing inventories has been
evidence, we have not been able to complete an adequately illustrated. It is unfortunate to

consider that although scores on the SSHA have References marked with an asterisk indicate
the highest criterion-related validities of all the studies included in the meta-analysis
examined SHSA inventories, its use has declined n
Aaron, S., & Skakun, E. (1999). Correlation of students’
considerably since it was first developed in the
characteristics with their learning styles as they
1950s. Its place appears to have been taken by begin medical school. Academic Medicine, 74,
more recently developed inventories that were 260–262.
not constructed with the same psychometric care Ahmann, J.S., & Glock, M.D. (1957). The utility of study
as the SSHA, and they appear to be considerably habits and attitudes inventory in a college reading
less useful in understanding college academic program. Journal of Educational Research, 51,
performance. 297–303.
n
We have noted above that the consideration of Ahmann, J.S., Smith, W.L., & Glock, M.D. (1958).
SHSA information may lead to a substantial Predicting academic success in college by means
of a study habits and attitude inventory.
improvement in the accuracy of college
Educational and Psychological Measurement, 18,
admissions decisions. What is currently unknown
853–857.
is whether this increase in accuracy could also be n
Albaili, M.A. (1994). Learning processes and academic
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n
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information. and highachieving college students on learning
CONCLUSION and study strategies. Educational Psychology, 17,
171–177.
n
We have shown that study skills, study habits, Allen, G.J., Lerner, W.M., & Hinrichsen, J.J. (1972).
Study behaviors and their relationships to test
study attitudes, and study motivation exhibit
anxiety and academic performance. Psychological
relationships with academic performance that are
Reports, 30, 407–410.
approximately as strong as the relationship n
Allen, W.R. (1992). The color of success: African-
between academic performance and the two America college student outcomes at
most frequently used predictors of academic predominantly white and historically black public
performance: prior academic performance and colleges and universities. Harvard Educational
scores on admissions tests. This finding, together Review, 62, 26–44.
n
with the relative independence of SHSA Altus, W.D. (1961). Correlational data for first-
constructs from both prior academic semester grade averages at the University of
performance and admissions test scores, suggests California, Santa Barbara. Journal of Genetic
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that study skills, study habits, study attitudes, and n
Arthur, A.D. (1995). Differences between EDPSY 100
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and nonEDPSY 100 students on study skills as
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Acknowledgments—The authors would like to dissertation, Ball State University, 1995).
thank the College Board for providing the funding Dissertation Abstracts International: Section A.
to conduct this study. The views expressed in this Humanities & Social Sciences, 55, 2321.
article are those of the authors and do not n
Ayers, J.B., & Rohr, M.E. (1974). Relationship of
represent the views of the College Board or its selected variables to success in a teacher
employees. The authors would like to thank Katie preparation program. Educational and
Hardek, Kim Kosenga, Scott Meyer, Michael Psychological Measurement, 34, 933–937.
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460 V

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PERSPECTIVES ON PSYCHOLOGICAL SCIENCE

Study Habits, Skills, and

University at Albany, SUNY, and University of

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Information Processing Approaches

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Fig. 1. Proposed model of academic performance determinants.

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study also provides estimates of the operational

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Lower Upper Lower Upper

(corrected for unreliability in both the criterion

Results for Time Spent Studying

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