2011. Vol.2, No.9, 909-916
Copyright © 2011 SciRes. DOI:10.4236/psych.2011.29137
Development of the Higher Education Value Inventory:
Factor Structure and Score Reliability
Vickie R. Luttrell1, David C. S. Richard2
1Department of Behavioral Sciences, Drury University, Springfield, USA;
2Department of Psychology, Rollins College, Winter Park, USA
Received September 16th, 2011; revised October 11th, 2011; accepted November 24th, 2011.
Students bring to college a value system that affects their levels of academic achievement and persistence. The
goal of this project was to develop a self-report inventory that measures the value students place on higher edu-
cation. The Higher Education Value Inventory (HEVI) surveys students’ attitudes and behaviors in five domains:
family expectations, scholastic focus, achievement value, general education value, and achievement obstacles.
We describe the development of the HEVI and report the results of reliability studies and factor analyses. HEVI
scores accounted for 35.9% of the variance in freshman grades. Implications for educational researchers and
admissions officers are provided
Keywords: Expectancy-Value Theory, Task Value, Motivation, College Students
Researchers in higher education seek comprehensive theories
of learning and instruction to guide their daily work. Our phi-
losophy has been shaped by Piaget (1928/2009), Ausubel
(1968), and Novak (1977) who emphasized that what the
learner already knows and values are among the most important
influences on behavior. According to Rokeach (1979) and
Schwartz (1992, 2010), values are transcendent core beliefs that,
when activated, serve as standards that guide our actions,
choices, judgments, and justifications. Values mediate students’
decision-making as they pursue scholastic activities (Feather,
1982) and are related to motivation in the sense that the value
one instills in a behavior functionally determines the strength
with which the behavior is pursued (Rheinberg, Vollmeyer, &
Rollett, 2000).
Given that students bring to college a value system that can
affect their levels of engagement and persistence, students’
values have been investigated extensively. Rotter (1954) and
Atkinson (1957) proposed that students’ expectancies for suc-
cess and the inherent value they place on that success interac-
tively mediate achievement-related behavior. Unfortunately,
investigations of college academic achievement using Rotter’s
theoretical framework have focused almost exclusively on the
expectancy component (i.e., locus of control) of the expec-
tancy-value dichotomy (e.g., Findley & Cooper, 1983; Kalech-
stein & Nowicki, 1997). This is problematic because even if
students are certain they can master certain tasks, they may
have little incentive to do so (Eccles & Wigfield, 2002).
Grounded in the seminal work of Atkinson (1957), Eccles et
al. (1983) and Eccles, Adler, and Meece (1984) developed a
comprehensive model of achievement-related choices, which
highlighted the multidimensional nature of task value as well as
its importance as a proximal predictor of behavior. They pro-
posed that task value is reflected in students’ interest in a task
(intrinsic value), its importance to them (attainment value), its
utility for them (utility value), and its cost. Using confirmatory
factor analysis, Eccles and Wigfield (1995) demonstrated that
the three value components could be differentiated in the
mathematics domain, supporting the construct validity of their
Most empirical tests of Eccles et al.’s (1983, 1984) expec-
tancy-value theory have been done with children and adoles-
cents. Researchers have devoted most of their attention to
studying relationships between task value and math participa-
tion, and findings across studies are consistent. Math value has
been shown to predict grades in math (Berndt & Miller, 1990),
course enrollment intentions (Meece, Wigfield, & Eccles, 1990),
number of math courses taken (Simpkins, Davis-Kean, & Ec-
cles, 2006; Updegraff, Eccles, Barber, & O’Brien, 1996), diffi-
culty of math courses completed (Nagy, Trautwein, Baumert,
Köller, & Garrett, 2006; Watt, Eccles, & Durik, 2006), math-
related career aspirations (Jozefowicz, Barber, & Eccles, 1993;
Watt, 2006), and plans to attend college (Eccles, Vida, & Bar-
ber, 2004). Longitudinal changes in task value across the ele-
mentary and secondary years have also been studied exten-
sively (e.g., Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2000).
Research involving college students (e.g., Battle & Wigfield,
2003; Bong, 2001; Chiu, Sun, Sun, & Ju, 2007; Feather, 1988;
Frome, Alfeld, Eccles, & Barber, 2006; Platt, 1988; Van-
Zile-Tamsen, 2001) has been less common, but results high-
light the importance of the values construct in higher educa-
tional research.
Rational e for the Higher Ed u ca ti on Value Inve nt ory
Value, expressed as a verb, refers to the process of appraising
the worth of some commodity (Rohan, 2000). Guided by the
premise that the value construct should occupy a central posi-
tion in research on motivation and cognizant of the expectancy-
value framework, the purpose of our study was to develop a
self-report inventory that measures the value students place on
higher education. Fundamentally, we hypothesized that those
students who placed greater value on higher education would
evidence greater academic achievement and be more likely to
matriculate through an undergraduate curriculum. A goal from
the outset, therefore, was to make the HEVI unique in its as-
sessment of the degree to which college students value their
education in a general sense. An instrument that facilitated this
assessment would be of considerable assistance to admissions
officers interested in estimating a student’s likelihood of com-
pleting an undergraduate degree.
In addition to its potential utility in admissions decision-
making, the HEVI can provide a means to assess the relation-
ship between higher education value and academic achievement
and/or retention. Also, the inventory could facilitate examina-
tion of longitudinal changes in students’ valuing of higher edu-
cation over the course of their undergraduate experience. Lon-
gitudinal studies may provide insight into why certain students
remain motivated to achieve and persist while others become
disinterested and drop out (Neuville et al., 2007). Finally, when
used alone or in conjunction with expectancy measures, the
HEVI might incrementally increase prediction of undergraduate
success and retention afforded by high school GPA or stan-
dardized achievement scores.
Haynes, Richard, and Kubany (1995) and the Standards for
Educational and Psychological Testing (American Educational
Research Association, American Psychological Association, &
National Council on Measurement in Education, 1999) suggest
that to maximize validity, researchers should take into consid-
eration their intended population of test takers when designing
and/or selecting assessment tools. Although questionnaires
have been developed to study perceived value (e.g., Eccles et
al., 1983, Lupart, Cannon, & Telfer, 2004), they were unsuit-
able for our purposes because they were designed for younger
students and focused on discrete subject areas, to include math,
science, English, and computer usage. Given our focus on en-
tering freshman students and our understanding that value pri-
orities may differ for students of traditional and nontradi-
tional-age (e.g., Faust & Courtenay, 2002; Hermon & Davis,
2004; Jinkens, 2009), our target population includes tradi-
tional-age freshman students, who are 18 to 24 years old.
Study 1: Development of the HEVI Item Pool
and Item Tryout
Content Domain Specification, Identification of
Facets, and Item Generation
We conceptualized the higher education values domain as
encompassing those values that bear directly on a person’s
motivation for excelling in postsecondary education. The value
domain was composed of five facets. Four of the facets were
based on the work of Eccles et al. (1983, 1984) and were tenta-
tively labeled interest value, utility value, attainment value, and
perceived cost. An additional facet, family expectations, was
included to tap important family and social influences on an
individual’s appraisal of worth.
We defined interest value as the enjoyment or satisfaction
derived by an individual from participating in school-related
activities. Interest value is conceptually similar to intrinsic mo-
tivation; consequently, if a task has high interest value, one is
intrinsically motivated to perform it (Eccles et al., 1983). We
defined utility value as the value an individual places on a task
or activity due to its instrumentality in achieving one or more
short- and long-range goals. Although a task high in utility
value may not be intrinsically interesting, it may still be valued
because of its perceived effects on one’s personal or profes-
sional achievement (Husman & Lens, 1999; Kauffman & Hus-
man, 2004). We defined attainment value as the value a person
places on doing well in school. The perceived value of grades
has been found to be a statistically significant predictor of GPA
even when standardized test scores are controlled (Pollio, Eison,
& Milton, 1988; Valencia, 1997).
We defined perceived cost as the subjective estimate of loss
suffered by an individual as a result of engaging in school-
related activities. If the perceived costs of educational attain-
ment outweigh the perceived benefits, scholastic success may
be devalued. For example, a person may devalue education if it
is perceived as conflicting with or inhibiting development of
identity, interpersonal relationships, immediate financial in-
come, or other incompatible but valued activities. Finally, we
defined family expectations as a student’s perception of family
expectations for his or her academic achievement. The family
expectations facet was included as a component of the higher
education values domain based on findings that values and
achievement-related choices are a function of the value orienta-
tions of parents and significant others (Ferry, Fouad, & Smith,
2000; Frome & Eccles, 1998; Jacobs & Harvey, 2005; Rey-
nolds & Burge, 2008).
Drawing from Feather (1988), Schwartz (1992), and Rohan
(2000), we recognized that underlying a behavior is a trade-off
between competing value priorities. We conceptualized interest
value, utility value, attainment value, and family expectations
as characteristics that would increase the perceived value of
attending college. Perceived cost was conceptualized in terms
of factors that would detract from that value. With these rela-
tionships in mind, we rationally constructed 76 Likert-type
items to reflect each of the five facets. Response options ranged
from 0 (Strongly disagree) to 4 (Strongly agree).
Participants and Procedure
The initial version of the HEVI was administered to 781 un-
dergraduates at a large state university in the Midwest. Partici-
pants completed the informed consent, a demographic ques-
tionnaire, and the inventory. Given our educational focus,
however, only those inventories completed by freshmen under
the age of 25 were analyzed. Given these inclusion criteria, the
final sample consisted of 305 freshmen (173 women and 132
men). Participants ranged in age from 18 to 24 years (M =
18.79, SD = .90) and included 277 Whites, 14 Blacks, 6 Asians,
3 Hispanics, and 5 students self-classified as “Other”. Comple-
tion times averaged about 20 minutes.
Statistical Analyses
Items were evaluated in terms of skewness, kurtosis, and in-
ter-item correlations. Items were rejected if the ratio of skew-
ness and/or kurtosis to their respective standard errors (e.g.,
skewness index/standard error of skewness) was greater than
±2.00. Items with nonnormal distributions were eliminated, and
highly intercorrelated items (r .80) were examined for redun-
dancy of content.
Based on recommendations by Floyd and Widaman (1995),
we subjected data to a principal components analysis (PCA).
The number of components to be retained was determined via
visual inspection of a scree plot and component eigenvalues
greater than 1. Retained components were subjected to varimax
rotation (Kaiser, 1958) and decisions regarding item retention
were based on structure coefficients reported in the rotated
component matrix. Following the recommendations of Comrey
(1973) and Stevens (2002), we set the coefficient criterion
to .45 to ensure that a constituent item correlated significantly
with its respective component and minimally shared 20% of its
variance with the component. In order to minimize component
overlap, we excluded items with complex coefficients. An item
with complex coefficients was defined as an item that loaded
.45 on one component and .30 on one or more other compo-
nents. Excluding these items maximized the unidimensionality
and internal consistency of scores on each subscale. Regarding
internal consistency, Nunnally and Bernstein (1994) advised
that alpha coefficients should reach or exceed .70 during pre-
liminary scale development. Therefore, scales with alpha coef-
ficients of .70 were determined to show an acceptable degree of
item interrelatedness.
Seven of the 76 items were removed from the pool based on
extreme skewness and/or kurtosis. The remaining 69 items
were subjected a PCA (Cattell, 1966). We removed 42 items for
the following reasons: initial communality below .20 (n = 8),
item failure to obtain a coefficient of .45 or greater on any
component (n = 12), or complex items (n = 22). We ran the
analysis a second time using the remaining 27 items. Five prin-
cipal components accounted for 49.29% of variance explained.
After inspecting the items, we assigned content-relevant la-
bels to the components. The five components were named as
follows (original theoretically-derived facet labels appear par-
enthetically): Scholastic Focus (9 items; interest value), Gen-
eral Education Value (8 items; utility value), Achievement
Value (5 items; attainment value), Competing Obligations or
Obstacles (3 items; personal cost) and Others’ Expectations
(Family Expectations; 2 items). Although the conceptual simi-
larity between the factor labels and their respective facet may
seem obvious, the revised factor labels better reflected the con-
tent of the actual items. Regarding internal consistency, coeffi-
cient alphas for scores on the Scholastic Focus, General Educa-
tion Value, Achievement Value, Competing Obligations or
Obstacles subscales, and Others’ Expectations were α
= .84, .77, .77, .63, and .62, respectively.
Study 2: Item Refinement and Final Scale
In preparation for the second item tryout, we generated 26
new items to enhance the measurement of each facet, bringing
the total number of items to 53. We then asked six undergradu-
ate students to evaluate the 53 items for meaning and clarity of
content. After receiving information about the purpose of the
HEVI and descriptions of each facet, students independently
rated the wording of each item by way of a dichotomous rating
scale (i.e., clearly written items were rated 1, while items re-
quiring revision were rated 0). In the case of a revision, stu-
dents were asked to provide recommendations. Students also
had the option of generating additional items for any or all of
the five facets. Students judged 11 of the 53 items to be written
unclearly, and those items were revised. Students provided no
new item suggestions for inclusion in the pool.
Providing evidence for content validity requires multiple ex-
perts to assess the degree to which items represent the facets of
interest (Haynes et al., 1995). Therefore, we selected five uni-
versity professors, who specialized in testing and measurement
to participate in an item-sorting task. For the sorting task, we
typed each item on an index card. Labels representing each of
the five facets were also typed on individual cards, with an
extra card labeled “Other.” Using descriptions of the five facets
as guides, experts attempted to sort each item into the facet that
seemed logically most appropriate. If an item did not seem to fit
logically into any of the facets, we instructed them to sort it into
the “Other” category. Items failing to be assigned to the same
facet by at least 80% (or 4 out of 5) of the experts were elimi-
nated from the item pool. Experts were also asked to generate
additional items for any or all of the five facets. As a result of
the task, we excluded 3 of the 53 items because they were
sorted into multiple facets. Experts provided no recommenda-
tions for additional items.
After having students and experts review the item pool in its
entirety, we administered the HEVI to a large sample of under-
graduates and conducted a PCA in a manner similar to that
reported in Study 1. In addition, we took a preliminary look at
convergent validity by regressing HEVI subscale scores on
self-reported GPAs and also tested for gender-related differ-
ences in HEVI performance.
A total of 560 students agreed to participate, but only those
inventories completed by freshmen under the age of 25 were
analyzed. Given these inclusion criteria, the final sample con-
sisted of 326 freshmen (180 women and 146 men). This pro-
duced a participant-to-item ratio of 6.52, which is consistent
with published recommendations (Gorsuch, 1997; Guadagnoli
& Velicer 1988). Participants ranged in age from 17 to 24 years
(M = 18.79, SD = .86) and included 302 Whites, 7 Blacks, 9
Asians, 3 Hispanics, and 6 students self-classified as “Other”.
Completion times averaged approximately 10 minutes.
Procedure and Statistical Analyses
Items were evaluated for skewness, kurtosis, and redundancy
of content, and data were subjected to a PCA. We also exam-
ined the correlation between subscale scores and self-reported
overall GPA and conducted a standard multiple regression
analysis. Scores on the five HEVI subscales served as predic-
tors and overall self-reported GPA was the criterion. The stan-
dard multiple regression procedure, where all predictor vari-
ables remain in the model, was selected so that the value of the
entire set of HEVI predictors could be evaluated. Finally, men’s
and women’s scale scores as well as self-reported GPAs were
compared by way of two-tailed independent-sample t-tests.
Item and Principal Component Analyses
The 50 HEVI items were examined initially for normality of
response distributions. One item was excluded because of ex-
treme skewness and kurtosis. Because the purpose of this study
was to reduce the number of HEVI items to a final set, PCA
was considered the most appropriate exploratory method avail-
able (Floyd & Widaman, 1995). Subsequent to conducting the
PCA, we conducted a common factor analysis using maximum
likelihood extraction with varimax rotation to investigate
whether the two procedures led to any meaningful differences
in structure coefficients and variance accounted for in the re-
spective solutions.
Based on observation of the scree plot, five factors were ex-
tracted and subjected to varimax rotation with Kaiser Normali-
zation (Kaiser, 1958). We removed 16 items for the following
reasons: initial communality below .20 (n = 3), failure for an
item to achieve the coefficient criterion of .45 or greater on any
of the five components (n = 8), and complex coefficients (n =
5). To produce an inventory with equal-item subscales, we
retained the six items that evidenced the highest coefficients for
each of the five components, resulting in a 30-item inventory.
We conducted a PCA again with these 30 items. Total variance
explained was 53.12%. One item (“I love school.”) failed to
reach the .45 coefficient criterion for Factor IV; however, the
decision was made to retain this item after we found the item
contributed positively to the alpha coefficient for the subscale.
We calculated scale scores by summing scores on constituent
items. Overall, factor correlations were small, with only one
correlation exceeding .30. Given the small intercorrelations and
the greater interpretability of orthogonal rotations relative to
oblique rotations, our use of varimax rotation was justified (see
Nunnally & Bernstein, 1994). However, given that 5 of the 10
factor intercorrelations were statistically significant, we sub-
jected the retained factors to oblique (promax) rotation to see if
meaningful differences emerged. The underlying factor struc-
ture remained unchanged. Thus, we report results from the PCA
for the final 30 items, subscale descriptive statistics, Cronbach
alphas, and component correlations in Table 1.
Regression Analysis
Scores on four of the five subscales correlated statistically
with self-reported GPA, including Scholastic Focus, r(318)
= .22, p < .001, Achievement Value, r(317) = .54, p < .001,
General Education Value, r(317) = .23, p < .001, and Achieve-
ment Obstacles, r(319) = .23, p < .001. Self-reported GPA also
statistically correlated with HEVI Total Score, r(306) = .39, p
< .001. It is important to note that higher scores on Achieve-
ment Obstacles reflected fewer perceived obstacles to achieve-
ment; therefore, consistent with logical predictions, fewer
achievement obstacles was related to higher GPAs. Only the
Family Expectations subscale did not reach statistical signifi-
cance, r(318) = .05, p = .353.
In a standard multiple regression analysis, the five HEVI pre-
dictors accounted for 35.9% of the variance in overall GPA and
produced a statistically significant model, F(5, 300) = 33.56, p
< .001. Four of the five predictors, Achievement Value (t =
10.79, p = .001), Achievement Obstacles (t = 3.59, p = .001),
Family Expectations (t = 2.86, p = .004), and General Educa-
tion Value (t = 2.00, p = .046) were statistically significant
contributors to the model.
Gender Comparison
Scores for men and women on each of the subscales were
compared using independent-sample t-tests, and effect sizes
were computed using Cohen’s d. Women reported statistically
greater scholastic focus, t(322) = 3.10, p = .002, d = 0.34, 95%
CI [0.62, 2.77], and placed greater value on high achievement,
t(320) = 2.75, p = .006, d = 0.31, 95% CI [0.41, 2.45], and gen-
eral education courses, t(320) = 4.64, p < .001, d = 0.50, 95%
CI [1.36, 3.35] than men. HEVI total-scale score differences
were also statistically significant, with women reporting a
greater valuing of higher education in general, t(310) = 4.20, p
< .001, d = 0.48, 95% CI [3.09, 8.54]. However, there were no
statistically significant differences between men and women on
perceptions of family expectations, t(321) = –.32, p = .748,
95% CI [–0.76, 0.55], or achievement obstacles, t(322) =.84, p
= .401, 95% CI [–0.54, 1.34]. Finally, women reported higher
overall GPAs than men, t(318) = 3.77, p < .001, d = 0.44, 95%
CI [0.11, 0.34].
Study 3: Reliability of HEVI Scores
The main purpose of Study 3 was to examine the test-retest
reliability of scores on the HEVI. The goal of Study 3 was to
administer the HEVI twice over a two-week period to a sample
of undergraduate students.
Participants and Procedure
We administered the HEVI to 61 undergraduate students
with the intent of having each student complete the HEVI a
second time two weeks later. Of the original 61 students, 47
returned for the retest session (20 men and 27 women). Partici-
pants ranged in age from 18 to 24 years (M = 19.25, SD = 1.50)
and included 42 Whites, 3 Blacks, 1 Asian, 0 Hispanics, and 1
student self-classified as “Other.” Completion times averaged
less than five minutes per testing session.
Two-week retest correlations for the five HEVI scales were
as follows: Family Expectations, r(45) = .85, p < .001; Scholas-
tic Focus, r(45) = .76, p < .001; Achievement Value, r(45)
= .78, p < .001; General Education Value, r(45) = .77, p < .001;
and Achievement Obstacles, r (45) = .80, p < .001. The
two-week retest correlation of the HEVI total-scale score,
which was the sum of the 30 item scores, was r(45) = .85, p
< .001. Retest coefficients at the item level were more variable,
and ranged from r = .37 to r = .75.
Value, in its verb form, refers to the process of appraising the
worth of some commodity (Rohan, 2000). The HEVI includes
30 items that measure the value college students place on higher
education. Across two undergraduate samples, factor analytic
results suggested a five-factor solution was the most parsimo-
nious and theoretically-consistent accounting of variance in
HEVI scores. The five facets contributing to subjective worth
included Family Expectations, Scholastic Focus, Achievement
Value, General Education Value, and Achievement Obstacles.
An obvious shortcoming of our work was that only about 10%
of the students we surveyed were nonwhite, and we focused
exclusively on traditional-age freshman students. Given that
traditional and nontraditional students may have different
mindsets (Faust & Courtenay, 2002; Hermon & Davis, 2004;
Jinkens, 2009) and that value orientations may differ as a func-
tion of ethnicity (e.g., Schwartz, 1992; Valencia, 1997), future
analyses with other student samples will allow us to explore the
generalizability of our findings to more diverse samples.
Cumulatively, our results suggest that the scores on the
HEVI possess acceptable reliability with samples of tradi-
tional-age university freshmen. Using Cronbach alpha, all in-
ternal consistency estimates exceeded .70, suggesting accept-
able item interrelatedness. Likewise, all retest reliability coeffi-
cients exceeded .70. However, scores on the Family Expecta-
tions and Achievement Obstacles subscales were more stable
over a two-week period than were scores on the other scales.
Item content on the Family Expectations and Achievement
Obstacles subscales is interpersonal in nature, and quantifies
students’ perceptions of others’ expectations as well as their
behavioral activities that involve significant others. Scores on
these scales are likely influenced by long-standing learning
histories, particularly where family members are involved, and
are less likely to fluctuate over short time intervals. By contrast,
the Achievement Value, General Education Value, and Scho-
lastic Focus scales include items that may be more situation-
ally-determined and context-relevant. Scores on these scales
may be sensitive to fluctuations in students’ recent experiences
and the demands of their academic schedules.
Table 1.
Structure coefficients for the PCA with varimax rotation of the 30 final HEVI items.
HEVI Components and Constituent Items I II III IV V Total Scale
Family Expectations (I)
I must do well in school to satisfy my family. .85 –.04 .06 –.12 –.12
I receive a lot of pressure from family members to do well in school. .80 –.09 .06 –.07 –.07
My family’s expectations about my academic achievement are unrealistically high. .79 –.05 –.08 –.13 –.11
My family would be disappointed if I were just an average student. .72 –.07 .28 –.02 –.11
My family’s own expectations of my academic achievement are higher than my own..65 –.19 –.27 –.08 –.21
It is important for me to meet the expectations of my family members. .60 .12 .17 .12 .09
Scholastic Focus (II)
I only study when it is absolutely necessary (r). –.08 .76 .07 .18 .07
I usually put off studying until the day before a test (r). –.02 .76 .01 .05 .08
I find other things to do instead of studying (r). .04 .75 .06 –.03 .13
I find it difficult to study when there are more interesting things to do (r). .03 .72 –.11 .06 .09
I rarely study on the weekends (r). –.12 .70 .17 .06 –.04
I party more than I study (r). –.11 .59 .26 .12 .05
Achievement V al ue (III)
High grades are important to me. .06 .02 .77 .09 .03
I almost always get one of the top grades in a class. –.13 .05 .71 .07 .15
I place a lot of pressure on myself to do well in school. .07 .14 .70 .08 .01
If someone were to say I was an average student, I would be upset. .17 .03 .69 .05 .08
If I do not receive an “A” on an exam, I am disappointed. .02 .00 .69 .07 –.10
I’m a perfectionist. .04 .11 .59 .01 –.11
General Education Value (IV)
I should only have to take courses in my major. –.08 .12 –.02 .83 .07
General education requirements are a waste of my time (r). –.11 .11 –.04 .82 .05
I understand why I am required to take a variety of courses to graduate. –.08 –.05 .01 .72 .11
Taking classes outside my area(s) of interest is a valuable experience. .01 –.02 .18 .70 .07
Most of what I learn in school is not useful (r). –.06 .29 .08 .48 .14
I love school. .01 .11 .21 .40 –.06
Achievement Obstacles (V )
I would do better in school if other obligations took less of my time (r). .03 –.05 –.01 .05 .77
Work-related activities interfere with my schoolwork (r). .01 –.07 –.02 .04 .72
It’s hard to focus on school when I have so much else to do (r). –.02 .23 –.08 –.05 .68
My family or friends make it hard for me to succeed in school (r). –.29 .22 .13 .13 .54
Family responsibilities make it difficult for me to do well in school (r). –.29 .04 .00 .25 .48
Someone close to me (for example, boyfriend, girlfriend, husband, wife) makes it
difficult for me to do well in school (r). –.17 .27 .04 .09 .47
Mean 12.97 9.98 14.28 13.16 14.05 62.36
Standard deviation 2.98 4.95 4.68 4.67 4.28 11.89
Coefficient alpha .84 .82 .79 .77 .71 .75
Component correlation matrix
I -
II .05 -
III .35 .20 -
IV .03 .25 .17 -
V –.07 .26 .04 .22 -
ote: Lowercase “r” indicates reverse-scored item. Primary structure coefficients are in boldface.
In the second item tryout, we examined the relationship be-
tween HEVI scores and overall GPA. As hypothesized, higher
scores on Family Expectations, Scholastic Focus, Achievement
Value, and General Education Value were related to higher
GPAs. Achievement Obstacles were inversely related to GPA
because higher scores on this scale reflect fewer perceived ob-
stacles to achievement. Therefore, students who valued earning
high grades, valued becoming well educated across curricular
boundaries, and believed that outside obstacles would not im-
pede their scholastic success had higher GPAs than students
who held contrasting beliefs. Likewise, HEVI total scores were
positively associated with overall GPAs. These findings lend
support for the construct validity of scores on the inventory.
When the five HEVI subscales were regressed on self-re-
ported GPA, 35.9% of the variance in overall GPA was ac-
counted for. An obvious limitation of these analyses is that the
self-reported current GPAs of second-semester freshmen served
as the criterion. Such reports are subject to memory biases and
errors. In addition, because of sample restrictions, students’
GPAs necessarily reflected only one semester’s worth of course
grades. Future criterion-related studies of the HEVI should use
grade point averages documented by the university that include
grades from the entire year.
Another limitation is that the studies did not directly assess
the degree to which HEVI scores incrementally improved pre-
diction of freshman academic performance relative to other
standardized instruments of academic proficiency (e.g., SAT
and ACT scores). Measures of achievement-related psycho-
logical variables, such as control expectancies and academic
self-concept, have been shown to enhance predictive accuracy
over and above scores on traditional measures, particularly in
minority students and students with lower pre-admission crite-
ria (Cone & Owens, 1991; Gerardi, 1990). Given the ambigu-
ous predictive validity of traditional academic measures, par-
ticularly for lower-performing, disadvantaged, or minority stu-
dents (e.g., Farver, Sedlacek, & Brooks, 1975; Kanoy, Wester,
& Latta, 1989; Sedlacek, 2005), future studies will examine the
degree to which HEVI scores can incrementally improve accu-
racy in prediction of academic achievement.
We also found evidence that women placed more value on
making studying a priority, earning high grades, and becoming
well educated across curricular boundaries than men. Given
these differences, and the hypothesized relations between HEVI
scores and GPA, we were not surprised to learn that women
also had higher self-reported GPAs than men. Gender-related
differences, particularly in achievement-related choices in math,
pervade the task value literature (e.g., Eccles, 1985; Lupart et
al., 2004). Eccles et al. (1983, 1984) proposed that task value is
a function of a person’s needs, goals, and self-perceptions.
Therefore, one possibility is that scores on the HEVI may be
affected by gender-role socialization and personal goals. As
examples, men may exhibit single-minded devotion to a single
goal, such as career success (Eccles, Barber, & Jozefowicz,
1999), whereas women may focus on career training as well as
general intellectual improvement (Green & Hill, 2003; Schab,
1974). As such, women, who have multiple roles and multiple
goals (Eccles et al., 1999), may place a higher value on demon-
strating competence in several academic domains simultane-
ously than men.
According to Rokeach (1973), “the value concept, more than
any other, should occupy a central position... able to unify the
apparently diverse interests of all the sciences concerned with
human behavior” (p. 3). Although Rokeach made this statement
nearly 40 years ago, empirical investigations of the values con-
struct are relatively limited at the university level and ample
opportunities for inspection and discovery remain. We pre-
sented preliminary evidence for the factorial validity, concur-
rent validity, and reliability of scores on the HEVI, an inventory
designed to measure the value college students place on higher
education. At the present time, however, we have not collected
data regarding the discriminant validity of HEVI scores nor
have we examined the relationship between HEVI scores and
attrition. Future investigations will aim to provide further evi-
dence of the construct validity of HEVI scores and will assess
factor invariance across gender and culturally diverse groups.
American Educational Research Association, American Psychological
Association, & National Council on Measurement in Education
(1999). Standards for educational and psychological testing. Wash-
ington DC: American Educational Research Association, American
Psychological Association, & National Council on Measurement in
Atkinson, J. W. (1957). Motivational determinants of risk-taking be-
havior. Psychological Review, 64, 359-372. doi:10.1037/h0043445
Ausubel, D. P. (1968). Educational psychology: A cognitive view. New
York, NY: Holt, Rinehart and Winston.
Battle A., & Wigfield, A. (2003). College women’s value orientations
toward family, career, and graduate school. Journal of Vocational
Behavior, 62, 56-75. doi:10.1016/S0001-8791(02)00037-4
Berndt, T. J., & Miller, K. E. (1990). Expectancies, values, and
achievement in junior high school. Journal of Educational Psychol-
ogy, 82, 319-326. doi:10.1037/0022-0663.82.2.319
Bong, M. (2001). Role of self-efficacy and task-value in predicting
college students’ course performance and future enrollment inten-
tions. Contemporary Educ ati ona l Psychology, 26, 553-570.
Cattell, R. B. (1966). The scree test for the number of factors. Multi-
variate Behavioral Research, 1, 245-276.
Chiu, C.-M., Sun, S.-Y., Sun, P.-C., & Ju, T. L. (2007). An empirical
analysis of the antecedents of web-based learning continuance.
Computers and Education, 49, 1224-1245.
Comrey, A. L. (1973). A first course in factor analysis. New York, NY:
Academic Press.
Cone, A. L., & Owens, S. K. (1991). Academic and locus of control
enhancement in a freshman study skills and college adjustment
course. Psychological Reports, 68, 1211-1217.
Eccles, J. S. (1985). Why doesn’t Jane run? Sex differences in educa-
tional and occupational patterns. In F. D. Horowitz, & M. O’Brien
(Eds.), The gifted and talented: Developmental perspectives (pp.
251-295). Washington DC: USical Association.
Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M.,
Meece, J. L., & Midgley, C. (1983). Expectancies, values, and aca-
demic behaviors. In J. T. Spence (Ed.), Achievement and achieve-
ment motivation (pp. 75-146). San Francisco: W. H. Freeman.
Eccles, J. S., Adler, T., & Meece, J. L. (1984). Sex differences in
achievement: A test of alternate theories. Journal of Personality and
Social Psychology, 46, 26-43.
Eccles, E. S., Barber, B., & Jozefowicz, D. (1999). Linking gender to
educational, occupational, and recreational choices: Applying the
Eccles et al. model of achievement-related choices. In W. B. Swann,
J. H. Langlois, & L. A. Gilbert (Eds.), Sexism and stereotypes in
modern society: The gender science of Janet Taylor Spence (pp.
153-192). Washington DC: American Psychological Association.
Eccles, J. S., Vida, M. N., & Barber, B. (2004). The relation of early
adolescents’ college plans and both academic ability and task-value
beliefs to subsequent college enrollment. Journal of Early Adoles-
cence, 24, 63-77.
Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The
structure of adolescents’ academic achievement task values and ex-
pectancy-related beliefs. Personality and Social Psychology Bulletin,
21, 215-225. doi: 10.1177/0146167295213003
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and
goals. Annual Review of Psychology, 53, 109-132.
Farver, A. S., Sedlacek, W. E., & Brooks, G. C., Jr. (1975). Longitu-
dinal predictions of university grades for blacks and whites. Meas-
urement and Evaluation in Guidance, 7, 243-250.
Faust, D. F., & Courtenay, B. C. (2002). Interaction in the intergenera-
tional freshman class: What matters. Educational Gerontology, 28,
401-422. doi:10.1080/03601270290081362
Feather, N. T. (1982). Expectancy-value approaches: Present status and
future directions. In N. T. Feather (Ed.), Expectations and actions:
Expectancy-value models in psychology. Hillsdale, NJ: Erlbaum.
Feather, J. T. (1988). Values, valences, and course enrollment: Testing
the role of personal values within an expectancy-value framework.
Journal of Educational Psycholo gy, 80, 381-391.
Ferry, T. R., Fouad, N. A., & Smith, P. L. (2000). The role of family
context in a social cognitive model for career-related choice behavior:
A math and science perspective. Journal of Vocational Behavior, 57,
Findley, M. J., & Cooper, H. M. (1983). Locus of control and academic
achievement: A literature review. Journal of Personality and Social
Psychology, 44, 419-427. doi:10.1037/1022-3514.44.2.419
Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the develop-
ment and refinement of clinical assessment instruments. Psychologi-
cal Assessment, 7, 286-299. doi:10.1037/1040-3590.7.3.286
Frome, P. M., Alfeld, C. J., Eccles, J. S., & Barber, B. L. (2006). Why
don’t they want a male-dominated job? An investigation of young
women who changed their occupational aspirations. Educational Re-
search and Evaluation, 12, 359-372.
Frome, P. M., & Eccles, J. S. (1998). Parents’ influence on children’s
achievement-related perceptions. Journal of Personality and Social
Psychology, 74, 435-452. doi:10.1037/0022-3514.74.2.435
Gerardi, S. (1990). Academic self-concept as a predictor of academic
success among minority and low-socioeconomic status students.
Journal of College Student Development, 31, 402-407.
Gorsuch, R. (1997). Exploratory factor analysis: Its role in item analy-
sis. Journal of P ersonality Assessment, 68, 532-560.
Green, R. J., & Hill, J. H. (2003). Sex and higher education: Do men
and women attend college for different reasons? College Student
Journal, 37, 557-563.
Guadagnoli, E., & Velicer, W. F. (1988). Relation of sample size to the
stability of component patterns. Psychological Bulletin, 103, 265-
275. doi:10.1037/0033-2909.103.2.265
Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content
validity in psychological assessment: A functional approach to con-
cepts and methods. Psychological Assessment, 7 , 238-247.
Hermon, D. A., & Davis, G. A. (2004). College student wellness: A
comparison between traditional- and nontraditional-age students.
Journal of College Counseling, 24, 32-39.
Husman, J., & Lens, W. (1999). The role of the future in student moti-
vation. Educational Psychologist, 34, 113-125.
Jacobs, N., & Harvey, D. (2005). Do parents make a difference to chil-
dren’s academic achievement? Differences between parents of higher
and lower achieving students. Educational Studies, 31, 431-448.
Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A.
(2002). Changes in children’s self-competence and values: Gender
and domain differences across grades one through twelve. Child De-
velopment, 73, 509-527.
Jinkens, R. C. (2009). Nontraditional students: Who are they? College
Student Journal, 43, 979-987.
Jozefowicz, D. M., Barber, B. L., & Eccles, J. S. (1993, March). Ado-
lescent work-related values and beliefs: Gender differences and rela-
tion to occupational aspirations. Biennial Meeting of the Society for
Research on Child Development, New Orleans.
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in
factor analysis. Psychometrika, 23, 187-200.
Kalechstein, A. D., & Nowicki, S. Jr. (1997). A meta-analytic examina-
tion of the relationship between control expectancies and academic
achievement: An 11-year follow-up to Findley and Cooper. Genetic,
Social, and General Psychology Monographs, 123, 27-56.
Kanoy, K. W., Wester, J., & Latta, M. (1989). Predicting college
success of freshmen using traditional, cognitive, and psychological
measures. Journal of Research and Development in Education, 22,
Kauffman, D. F., & Husman, J. (2004). Effects of time perspective on
student motivation: Introduction to a special issue. Educational Psy-
chology Review, 16, 1-7.
Lupart, J. L., Cannon, E., & Telfer, J. A. (2004). Gender differences in
adolescent academic achievement, interests, values and life-role ex-
pectations. High Abili ty Studies, 15, 25-42.
Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math
anxiety and its consequences for young adolescents-course enroll-
ment intentions and performance in mathematics. Journal of Educa-
tional Psychology, 82, 60-70.
Nagy, G., Trautwein, U., Baumert, J., Köller, O., & Garrett, J. (2006).
Gender and course selection in upper secondary education: Effects of
academic self-concept and intrinsic value. Educational Research and
Evaluation, 12, 323-345.
Neuville, S., Frenay, M., Schmitz, J., Boudrenghien, G., Noël, B., &
Wertz, V. (2007). Tinto’s theoretical perspective and expectancy-
value paradigm: A confrontation to explain freshmen’s academic
achievement. Psychologica Belgica, 47, 31-50.
Novak, J. D. (1977). A theory of education. New York, NY: Cornell
University Press.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd
ed.). New York, NY: McGraw-Hill.
Piaget, J. (1928/2009). Children’s understanding of causality. British
Journal of Psychology, 100, 207-224.
Platt, C. W. (1988). Effects of causal attributions for success of
first-term college performance: A covariance structure model. Jour-
nal of Educational Psycho logy, 80, 569-578.
Pollio, H. R., Eison, J. A., & Milton, O. (1988). College grades as an
adaptation level phenomenon. Comtemporary Educational Psychol-
ogy, 13, 146-156. doi:10.1016/0361-476X(88)90015-X
Reynolds, J. R., & Burge, S. W. (2008). Educational expectations and
the rise in women’s postsecondary attainments. Social Science Re-
search, 37, 485-499. doi:10.1016/j.ssresearch.2007.09.002
Rheinberg, F., Vollmeyer, R., & Rollett, W. (2000). Motivation and
action in self-regulated learning. In M. Boekaerts, P. R. Pintrich, &
M. H. Zeidner (Eds.), Handbook of self-regulation (pp. 503-529).
San Diego, CA: Academic Press.
Rohan, M. J. (2000). A rose by any name? The values construct. Per-
sonality and Social Psychology Review, 4, 255-277.
Rokeach, M. (1973). The nature of human values. New York, NY: Free
Rokeach, M. (1979). From the individual to institutional values with
special reference to the values of science. In M. Rokeach (Ed.), Un-
derstanding human values (pp. 47-70). New York, NY: Free Press.
Rotter, J. B. (1954). Social learning and clinical psychology. New York,
NY: Prentice-Hall.
Schab, F. (1974). Reasons for attending college as reported by female
students in a southern university. Florida Journal of Educational
Research, 16, 55-58.
Schwartz, S. H. (1992). Universals in the content and structure of val-
ues: Theoretical advances and empirical tests in 20 countries. In M. P.
Zanna (Ed.), Advances in experimental social psychology (Vol. 25,
pp. 1-65). San Diego, CA: Academic Press.
Schwartz, S. H. (2010). Basic values: How they motivate and inhibit
prosocial behavior. In M. Mikulincer, & P. R. Shaver (Eds.), Proso-
cial motives, emotions, and behavior: The better angels of our nature
(pp. 221-241). Washington, DC: American Psychological Associa-
Sedlacek, W. E. (2005). The case for noncognitive measures. In W. J.
Camara & E. W. Kimmel (Eds.), Choosing students: Higher educa-
tion admissions tools for the 2 1 s t c en t u r y (pp. 177-193). Mahwah, NJ:
Lawrence Erlbaum.
Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and
science motivation: A longitudinal examination of the links between
choices and beliefs. Developmental Psychology, 42, 70-83.
Stevens, J. (2002). Applied multivariate statistics for the social sciences
(4th ed.), Mahwah, NJ: Lawrence Erlbaum.
Updegraff, K. A., Eccles, J. S., Barber, B. L., & O’Brien, K. M. (1996).
Course enrollment as self-regulatory behavior: Who takes optional
high school math courses? Learning and Individual Differences, 8,
Valencia, A. A. (1997). Anglo-American and Mexican American stu-
dents’ estimation of value placed on higher educational attainments
by significant persons in their lives. Journal of Multicultural Coun-
seling and Development, 25, 269-280.
VanZile-Tamsen, C. (2001). The predictive power of expectancy of
success and task value for college students’ self-regulated strategy
use. Journal of College Student Deve lopment, 42, 233-241.
Watt, H. M. G. (2006). The role of motivation in gendered educational
and occupational trajectories related to maths. Educational Research
and Evaluation, 12, 305-322. doi:10.1080/13803610600765562
Watt, H. M. G., Eccles, J. S., & Durik, A. M. (2006). The leaky
mathematics pipeline for girls: A motivational analysis of high
school enrolments in Australia and the USA. Equal Opportunities
International, 25, 642-659.