Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.79.4.205-208
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
An Examination of Two Learning Style Measures
and Their Association With Business Learning
Donald R. Bacon
To cite this article: Donald R. Bacon (2004) An Examination of Two Learning Style Measures
and Their Association With Business Learning, Journal of Education for Business, 79:4, 205-208,
DOI: 10.3200/JOEB.79.4.205-208
To link to this article: http://dx.doi.org/10.3200/JOEB.79.4.205-208
Published online: 07 Aug 2010.
Submit your article to this journal
Article views: 42
View related articles
Citing articles: 9 View citing articles
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=vjeb20
Download by: [Universitas Maritim Raja Ali Haji]
Date: 12 January 2016, At: 23:23
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
An Examination of Two Learning
Style Measures and
Their Association With
Business Learning
DONALD R. BACON
University of Denver
Denver, Colorado
O
ne of the major educational movements of the past 25 years has
been the increased attention to student
learning styles (Lemire, 2000). Learning style refers to the consistent way in
which a learner responds to or interacts
with stimuli in the learning context
(Loo, 2002). The learning style paradigm holds that when course delivery is
tailored to the different learning styles
of students, student learning is
enhanced. Some researchers have countered, however, that this fundamental
assumption has not yet been strongly
supported (Curry, 1990; Freedman &
Stumpf, 1980). The plethora of learning
style measures available confounds our
ability to understand the effects of
learning style differences.
My purpose in this research is to
compare two fairly new learning style
measures that have become widely
available: Felder’s Index of Learning
Styles (Soloman & Felder, 2000) and
Jester’s Learning Style Survey (Miller,
2000). I sought to investigate the reliability of these scales and the strength of
the relationships that each measure
exhibits with learning.
I selected two learning style frameworks for study because they are available for use with no charge on the Internet. The instruments are designed to be
completed online and automatically
scored, with the results reported imme-
ABSTRACT. In this study, the
author examined two learning style
measures that are available online,
Felder’s Index of Learning Styles and
Jester’s Learning Styles Survey. Most
of the subscales contained in these
measures were found to have poor
reliability. Further, the author found
that these measures exhibited little or
no meaningful association with business learning.
diately to the user. Because of their low
cost and ease of use, these tools have
gained a great deal of attention. Their
popularity is reflected in the fact that,
when I began this study, the two instruments scored the highest rankings in
Google (the most widely used Web
search engine) in a search using the keywords “learning styles.” Yet, these relatively new scales have not been thoroughly researched.
I first studied Felder’s Index of
Learning Styles (or ILS, Felder, 1996).
Working with Soloman (Soloman &
Felder, 2000) and drawing on a conceptual model developed earlier with Silverman (Felder & Silverman, 1988),
Felder developed a learning style measure comprising four dimensions. The
first dimension, sensing versus intuitive
learners, distinguishes between learners
who prefer concrete, practical facts and
procedures (sensors) and learners who
prefer conceptual or theoretical information (intuitors). The second dimension, visual versus verbal learners, dis-
tinguishes between learners who prefer
pictures, diagrams, or charts (visuals)
and learners who prefer written or spoken explanations (verbals). The third
dimension, active versus reflective
learners, distinguishes between learners
who prefer to learn by trying things out
or working with others (actives) and
learners who prefer thinking things
through and working alone (reflectives).
Finally, the fourth dimension, sequential versus global learners, distinguishes
between learners who prefer linear,
orderly learning in steps (sequentials)
and learners who are more comfortable
with holistic approaches and learn in
large leaps (globals).
I then studied Jester’s Learning Style
Survey (LSS, Jester, 2000; Miller,
2000). Jester conceptualizes four distinct learning styles, but learners may
use some combination of them. The first
is the visual verbal learning style.
Although Felder saw visual and verbal
learning as opposite ends of a continuum, Jester accepted that these styles
may co-occur. Visual verbal learners
like pictures and diagrams but learn
even more effectively when they write
out explanations for the material that
they are studying. Jester’s second style,
the visual nonverbal style, is more similar to Felder’s visual style, in which
learners benefit from pictures and diagrams but not as much from verbal
March/April 2004
205
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
material. The tactile kinesthetic learning
style is Jester’s third style. These learners prefer physically active, hands-on
activity, much like Felder’s active learners. Finally, Jester’s auditory verbal
learning style describes learners who
benefit from verbal material but learn
more when they can listen to spoken
words than when they just read material
for themselves.
Although there is an enormous
amount of published research on learning styles, relatively few studies have
critically evaluated the assumption that
learning style affects learning outcomes. Of those that have, many have
found null results, particularly in business (Freeman, Hanson, & Rison, 1998;
Huxham & Land, 2000; Karakaya, Ainscough, & Chopoorian, 2001; Tom &
Calvert, 1984). Many studies have
attempted to identify which categories
of learning styles are most common
among business students (Loo, 2002;
Wynd & Bozman, 1996), but this
research seems premature given the
questionable size of the effect of learning styles on learning outcomes.
One reason why few significant
learning style effects have been found
may be the lack of statistical power due
to small sample sizes and low measurement reliability (Curry, 1990). In their
analysis of Kolb’s classic measure of
learning style, Freedman and Stumpf
(1980) found that Kolb’s measure was
unreliable. Freedman and Stumpf suggested that the somewhat artificial,
forced bipolar nature of Kolb’s instrument may be another reason why the
measure is not associated strongly with
other variables. It should be noted that
the ILS uses scaling similar to Kolb’s,
but the LSS generally does not.
The learning style paradigm assumes
that when the learning environment
matches a student’s learning style, the
student’s learning is enhanced. We
would expect this effect to be particularly pronounced in a modified, traditionally taught course that relies heavily on
lecture. Such lecture-based courses have
been criticized for not meeting the needs
of students with different learning styles
(Karakaya, Ainscough, & Chopoorian,
2001); without improvement, such
courses would result in at least some students not learning as effectively as oth206
Journal of Education for Business
ers. In the present research, I observed
differences in learning across learning
styles in a traditionally taught course.
Approximately 90% of the class time in
this course was spent in lecture, with the
balance spent in discussion. The course
also included two projects that were
completed outside of class.
I formulated the two following
research questions:
RQ1: How reliable are the ILS and
LSS subscales?
RQ2: Are differences in learning
styles in a traditionally taught marketing
course associated with differences in
learning?
Method
Data
I collected the data for this study in
class in six sections of a traditionally
taught, junior-level marketing course
(Consumer Behavior). The same
instructor taught all sections. The total
number of students in these sections
was 211. I used within-construct mean
substitution to accommodate missing
responses within scales and used listwise deletion to eliminate respondents
with other missing data (e.g., the
pretest). This resulted in a usable sample of 161 respondents. Of these, 53%
were women, 50% were 3rd-year students, and 37% were 4th-year students.
The student body at the test site was
predominantly of traditional ages.
Instruments
The students completed Felder’s 44item ILS and Jester’s 32-item LSS
(both objectively-scored instruments)
in paper-and-pencil format during the
7th week of class.
Research has found that learning is
strongly affected by prior learning and
general academic ability (Bloom, 1976)
and that academic ability and learning
style often are correlated (Wynd & Bozman, 1996). Therefore, in a test of the
effect of learning style on learning outcomes, it is important to control for
prior learning and academic ability. I
accomplished this control through the
use of a pretest-posttest design. Students took the pretest on the first day of
class, and I used the scores on the final
exam, which was taken during finals
week, as the posttest. The course pretest
covered knowledge of marketing and
consumer behavior that a student might
have acquired in an earlier marketing
course. Of the 4 short-answer and 31
multiple-choice questions used in the
pretest, I dropped 10 multiple-choice
items to increase the reliability of the
test. The resulting 25-item test achieved
a reliability of .59. The final exam consisted of 107 multiple-choice questions
and had a reliability of .91. Among the
students who completed all the instruments in this study, the correlation
between the pretest and final exam
scores was .46 (n = 161, p < .001).
Results
To address the first research question, I computed the reliabilities for the
subscales of the ILS and the LSS (see
Table 1). The reliabilities of the learning style instruments were modest, generally falling below the .70 level, which
many consider to be a meaningful minimum (Nunnally, 1978). The ILS subscales are generally more reliable than
the LSS subscales, partly because the
ILS subscales are each 11 items long,
whereas the LSS subscales are each
only 8 items long.
TABLE 1. Reliabilities of the ILS and LSS Subscales
ILS Subscales
Subscale
Active/reflective
Sensing/intuitive
Visual/verbal
Sequential/global
LSS Subscales
Reliability
0.60
0.70
0.66
0.47
Subscale
Visual nonverbal
Visual verbal
Auditory
Kinesthetic
Reliability
0.49
0.32
0.61
0.58
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
To test whether or not learning style
was associated with differences in
learning, I analyzed two regression
models in sequence. In the first analysis,
final knowledge was regressed on previous knowledge. This model resulted in
an R2 of .21 and was statistically significant (F [1,159] = 42.65, p < .001). I
retained the residuals from this regression, representing that part of final
knowledge that was not explained by
prior knowledge, for use as the dependent variable in the next regression. In
this next regression, all of the learning
style subscales were made available as
potential explanatory variables in a
stepwise regression. In the final model,
only one variable, Jester’s auditory subscale, was statistically significant (F
[1,159] = 8.04, p = .005), but this variable explained only 5% of the variance
in learning. Surprisingly, the standardized coefficient for the auditory subscale, at -.21, indicates that those who
preferred to learn by listening actually
learned less effectively in this lecturebased course. A closer examination of
the items in the LSS auditory subscale
suggests that students scoring high in
auditory preference may be less comfortable with reading than other students are. The observed correlation
between the LSS visual verbal scale and
the auditory subscale was -.21 (n = 161,
p < .01). Thus, the only significant association found in this study between
learning styles and learning was consistent with the conclusion that students
who prefer to listen more than read do
not do as well in school as those who
are comfortable reading.
It should be noted that some of the
other subscales studied here were nearly significant. In Table 2, I show the par-
tial correlations and significance levels
associated with the subscales excluded
from the regression model; I sort these
data by level of significance. At the top
of this list is the visual verbal scale
mentioned previously. The positive
coefficient of .154 indicates that students who are comfortable reading and
have confidence in their verbal skills
may learn more in a traditionally taught
course than those who have less comfort
and confidence with verbal skills. The
positive coefficient associated with
active/reflective, at .146, indicates that
students who are more reflective than
active, or who prefer thinking and listening to doing and talking, may learn
more in a course like this one. It should
be reiterated that these coefficients did
not achieve statistical significance and
must be interpreted with caution. If the
sample were larger, these effects may
have achieved statistical significance,
but even if they did, the expected effect
sizes would still be fairly small by widely accepted standards (Cohen [1977]
described a correlation of .10 as small
and one of .30 as medium).
Discussion and Conclusions
The results of this study indicate that
only one of the eight learning style subscales studied, the sensing/intuitive subscale of the ILS, achieved what is commonly regarded as acceptable reliability.
These findings represent a warning flag
to researchers who might be tempted to
use a convenient scale without first
inspecting its reliability. We should note
that reliabilities may vary from school to
school, so the present research should
not be regarded as a condemnation of the
scales presented but merely as a
TABLE 2. Variables Excluded in Stepwise Regression
Subscale
Visual verbal
Active/reflective
Kinesthetic
Sensing/intuitive
Visual nonverbal
Visual/verbal
Sequential/global
Partial
correlation
p value
.154
.146
–.110
–.108
–.092
.035
–.031
.052
.065
.166
.175
.245
.663
.697
reminder to researchers to validate the
scales of interest on the populations that
they wish to study before making a
wholesale adoption of the measures.
In this study, I also found very little
effect of learning styles on learning outcomes. Only one subscale was significantly associated with learning, and with
an explained variance of 5%, the size of
this effect would be considered midway
between small and medium (Cohen
[1977] described an R2 of .01 as small
and an R2 of .09 as medium). Further, the
nature of the effect found in the present
study indicates that verbal aptitude or
verbal fluency may have more influence
on learning than does learning style.
(Also, the weak reliability of the pretest
may not have controlled adequately for
the effect of verbal aptitude.) Thus,
rather than adjusting a course to meet the
needs of underperforming students by,
for example, increasing the frequency of
auditory learning opportunities, the
underperforming students might be better served by remedial interventions targeted at raising their verbal comfort and
confidence. As previously noted, the
present course, in which auditory learners underperformed other students, was
approximately 90% lecture.
In summary, the present research
found that two learning style measures
freely available on the Internet exhibited poor reliabilities in this student population and had little meaningful impact
on student learning. Thus, although currently there is great interest in learning
styles among business education
researchers, these results indicate that
those researchers should first check the
reliabilities of the measures that they
plan to use as a foundation for their
research. The present study also serves
as a call for more careful examination of
the size of the effect, if any, that learning styles have on learning outcomes.
ACKNOWLEDGMENT
I gratefully acknowledge the insightful comments received from Kim A. Stewart on an earlier
draft and the financial assistance from the Daniels
College of Business, which was instrumental in
the completion of this research.
REFERENCES
Bloom, B. S. (1976). Human characteristics and
school learning. New York: McGraw-Hill.
March/April 2004
207
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
Cohen, J. (1977). Statistical power analysis for
the behavioral sciences (revised ed.). New
York: Academic Press.
Curry, L. (1990, October). A critique of the
research on learning styles. Educational Leadership, 50–56.
Felder, R. M. (1996). Matters of style. ASEE
Prism, 6(4), 18–23.
Felder, R. M., & Silverman, L. K. (1988). Learning styles and teaching styles in engineering
education. Engineering Education, 78(7),
674–681.
Felder, R. M., & Soloman, B. A. (1999). Learning
styles and strategies. North Carolina State University. Retrieved October 20, 2001, from
http://www.uncw.edu/cte/soloman_felder.htm
Freedman, R. D., & Stumpf, S. A. (1980). Learning style theory: Less than meets the eye. Academy of Management Review, 5(3), 445–447.
Freeman, J. L., Hanson, R. C., & Rison, F. (1998).
Student learning styles, satisfaction and perfor-
208
Journal of Education for Business
mance. Proceedings of the 1998 Western Decision Sciences Conference, Reno, Nevada.
Huxham, M., & Land, R. (2000). Assigning students in group work projects: Can we do better
than random? Innovations in Education and
Training International, 37(1), 17–22.
Jester, C. (2000). Introduction to the DVC learning style survey for college. Retrieved October
19, 2001, from http://www.metamath.com/
lsweb/dvclearn.htm
Karakaya, F., Ainscough, T. L., & Chopoorian, J.
(2001). The effects of class size and learning
style on student performance in a multimediabased marketing course. Journal of Marketing
Education, 23(2), 84–90.
Lemire, D. (2000). Research report—A comparison of learning styles scores: A question of concurrent validity. Journal of College Reading
and Learning, 31(1), 109–116.
Loo, R. (2002). A meta-analytic examination of
Kolb’s learning style preferences among busi-
ness majors. Journal of Education for Business,
77(5), 252–256.
Miller, S. (2000). A learning style survey for college. Retrieved October 24, 2001, from
http://www.metamath.com//multiple/multiple_
choice_questions.cgi
Nunnally, J. C. (1978). Psychometric theory (2nd
edition). New York: McGraw-Hill.
Soloman, B. A., & Felder, R. M. (2000). Index of
learning styles questionnaire. Retrieved October 20, 2001, from http://www.ncsu.edu/felderpublic/ILSdir/ilsweb.html
Tom, G., & Calvert, S. (1984). Learning style as a
predictor of student performance and instructor
evaluations. Journal of Marketing Education,
6(2), 14–17.
Wynd, W. R., & Bozman, C. S. (1996). Student
learning style: A segmentation strategy for
higher education. Journal of Education for
Business, 71(4), 232–235.
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
An Examination of Two Learning Style Measures
and Their Association With Business Learning
Donald R. Bacon
To cite this article: Donald R. Bacon (2004) An Examination of Two Learning Style Measures
and Their Association With Business Learning, Journal of Education for Business, 79:4, 205-208,
DOI: 10.3200/JOEB.79.4.205-208
To link to this article: http://dx.doi.org/10.3200/JOEB.79.4.205-208
Published online: 07 Aug 2010.
Submit your article to this journal
Article views: 42
View related articles
Citing articles: 9 View citing articles
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=vjeb20
Download by: [Universitas Maritim Raja Ali Haji]
Date: 12 January 2016, At: 23:23
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
An Examination of Two Learning
Style Measures and
Their Association With
Business Learning
DONALD R. BACON
University of Denver
Denver, Colorado
O
ne of the major educational movements of the past 25 years has
been the increased attention to student
learning styles (Lemire, 2000). Learning style refers to the consistent way in
which a learner responds to or interacts
with stimuli in the learning context
(Loo, 2002). The learning style paradigm holds that when course delivery is
tailored to the different learning styles
of students, student learning is
enhanced. Some researchers have countered, however, that this fundamental
assumption has not yet been strongly
supported (Curry, 1990; Freedman &
Stumpf, 1980). The plethora of learning
style measures available confounds our
ability to understand the effects of
learning style differences.
My purpose in this research is to
compare two fairly new learning style
measures that have become widely
available: Felder’s Index of Learning
Styles (Soloman & Felder, 2000) and
Jester’s Learning Style Survey (Miller,
2000). I sought to investigate the reliability of these scales and the strength of
the relationships that each measure
exhibits with learning.
I selected two learning style frameworks for study because they are available for use with no charge on the Internet. The instruments are designed to be
completed online and automatically
scored, with the results reported imme-
ABSTRACT. In this study, the
author examined two learning style
measures that are available online,
Felder’s Index of Learning Styles and
Jester’s Learning Styles Survey. Most
of the subscales contained in these
measures were found to have poor
reliability. Further, the author found
that these measures exhibited little or
no meaningful association with business learning.
diately to the user. Because of their low
cost and ease of use, these tools have
gained a great deal of attention. Their
popularity is reflected in the fact that,
when I began this study, the two instruments scored the highest rankings in
Google (the most widely used Web
search engine) in a search using the keywords “learning styles.” Yet, these relatively new scales have not been thoroughly researched.
I first studied Felder’s Index of
Learning Styles (or ILS, Felder, 1996).
Working with Soloman (Soloman &
Felder, 2000) and drawing on a conceptual model developed earlier with Silverman (Felder & Silverman, 1988),
Felder developed a learning style measure comprising four dimensions. The
first dimension, sensing versus intuitive
learners, distinguishes between learners
who prefer concrete, practical facts and
procedures (sensors) and learners who
prefer conceptual or theoretical information (intuitors). The second dimension, visual versus verbal learners, dis-
tinguishes between learners who prefer
pictures, diagrams, or charts (visuals)
and learners who prefer written or spoken explanations (verbals). The third
dimension, active versus reflective
learners, distinguishes between learners
who prefer to learn by trying things out
or working with others (actives) and
learners who prefer thinking things
through and working alone (reflectives).
Finally, the fourth dimension, sequential versus global learners, distinguishes
between learners who prefer linear,
orderly learning in steps (sequentials)
and learners who are more comfortable
with holistic approaches and learn in
large leaps (globals).
I then studied Jester’s Learning Style
Survey (LSS, Jester, 2000; Miller,
2000). Jester conceptualizes four distinct learning styles, but learners may
use some combination of them. The first
is the visual verbal learning style.
Although Felder saw visual and verbal
learning as opposite ends of a continuum, Jester accepted that these styles
may co-occur. Visual verbal learners
like pictures and diagrams but learn
even more effectively when they write
out explanations for the material that
they are studying. Jester’s second style,
the visual nonverbal style, is more similar to Felder’s visual style, in which
learners benefit from pictures and diagrams but not as much from verbal
March/April 2004
205
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
material. The tactile kinesthetic learning
style is Jester’s third style. These learners prefer physically active, hands-on
activity, much like Felder’s active learners. Finally, Jester’s auditory verbal
learning style describes learners who
benefit from verbal material but learn
more when they can listen to spoken
words than when they just read material
for themselves.
Although there is an enormous
amount of published research on learning styles, relatively few studies have
critically evaluated the assumption that
learning style affects learning outcomes. Of those that have, many have
found null results, particularly in business (Freeman, Hanson, & Rison, 1998;
Huxham & Land, 2000; Karakaya, Ainscough, & Chopoorian, 2001; Tom &
Calvert, 1984). Many studies have
attempted to identify which categories
of learning styles are most common
among business students (Loo, 2002;
Wynd & Bozman, 1996), but this
research seems premature given the
questionable size of the effect of learning styles on learning outcomes.
One reason why few significant
learning style effects have been found
may be the lack of statistical power due
to small sample sizes and low measurement reliability (Curry, 1990). In their
analysis of Kolb’s classic measure of
learning style, Freedman and Stumpf
(1980) found that Kolb’s measure was
unreliable. Freedman and Stumpf suggested that the somewhat artificial,
forced bipolar nature of Kolb’s instrument may be another reason why the
measure is not associated strongly with
other variables. It should be noted that
the ILS uses scaling similar to Kolb’s,
but the LSS generally does not.
The learning style paradigm assumes
that when the learning environment
matches a student’s learning style, the
student’s learning is enhanced. We
would expect this effect to be particularly pronounced in a modified, traditionally taught course that relies heavily on
lecture. Such lecture-based courses have
been criticized for not meeting the needs
of students with different learning styles
(Karakaya, Ainscough, & Chopoorian,
2001); without improvement, such
courses would result in at least some students not learning as effectively as oth206
Journal of Education for Business
ers. In the present research, I observed
differences in learning across learning
styles in a traditionally taught course.
Approximately 90% of the class time in
this course was spent in lecture, with the
balance spent in discussion. The course
also included two projects that were
completed outside of class.
I formulated the two following
research questions:
RQ1: How reliable are the ILS and
LSS subscales?
RQ2: Are differences in learning
styles in a traditionally taught marketing
course associated with differences in
learning?
Method
Data
I collected the data for this study in
class in six sections of a traditionally
taught, junior-level marketing course
(Consumer Behavior). The same
instructor taught all sections. The total
number of students in these sections
was 211. I used within-construct mean
substitution to accommodate missing
responses within scales and used listwise deletion to eliminate respondents
with other missing data (e.g., the
pretest). This resulted in a usable sample of 161 respondents. Of these, 53%
were women, 50% were 3rd-year students, and 37% were 4th-year students.
The student body at the test site was
predominantly of traditional ages.
Instruments
The students completed Felder’s 44item ILS and Jester’s 32-item LSS
(both objectively-scored instruments)
in paper-and-pencil format during the
7th week of class.
Research has found that learning is
strongly affected by prior learning and
general academic ability (Bloom, 1976)
and that academic ability and learning
style often are correlated (Wynd & Bozman, 1996). Therefore, in a test of the
effect of learning style on learning outcomes, it is important to control for
prior learning and academic ability. I
accomplished this control through the
use of a pretest-posttest design. Students took the pretest on the first day of
class, and I used the scores on the final
exam, which was taken during finals
week, as the posttest. The course pretest
covered knowledge of marketing and
consumer behavior that a student might
have acquired in an earlier marketing
course. Of the 4 short-answer and 31
multiple-choice questions used in the
pretest, I dropped 10 multiple-choice
items to increase the reliability of the
test. The resulting 25-item test achieved
a reliability of .59. The final exam consisted of 107 multiple-choice questions
and had a reliability of .91. Among the
students who completed all the instruments in this study, the correlation
between the pretest and final exam
scores was .46 (n = 161, p < .001).
Results
To address the first research question, I computed the reliabilities for the
subscales of the ILS and the LSS (see
Table 1). The reliabilities of the learning style instruments were modest, generally falling below the .70 level, which
many consider to be a meaningful minimum (Nunnally, 1978). The ILS subscales are generally more reliable than
the LSS subscales, partly because the
ILS subscales are each 11 items long,
whereas the LSS subscales are each
only 8 items long.
TABLE 1. Reliabilities of the ILS and LSS Subscales
ILS Subscales
Subscale
Active/reflective
Sensing/intuitive
Visual/verbal
Sequential/global
LSS Subscales
Reliability
0.60
0.70
0.66
0.47
Subscale
Visual nonverbal
Visual verbal
Auditory
Kinesthetic
Reliability
0.49
0.32
0.61
0.58
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
To test whether or not learning style
was associated with differences in
learning, I analyzed two regression
models in sequence. In the first analysis,
final knowledge was regressed on previous knowledge. This model resulted in
an R2 of .21 and was statistically significant (F [1,159] = 42.65, p < .001). I
retained the residuals from this regression, representing that part of final
knowledge that was not explained by
prior knowledge, for use as the dependent variable in the next regression. In
this next regression, all of the learning
style subscales were made available as
potential explanatory variables in a
stepwise regression. In the final model,
only one variable, Jester’s auditory subscale, was statistically significant (F
[1,159] = 8.04, p = .005), but this variable explained only 5% of the variance
in learning. Surprisingly, the standardized coefficient for the auditory subscale, at -.21, indicates that those who
preferred to learn by listening actually
learned less effectively in this lecturebased course. A closer examination of
the items in the LSS auditory subscale
suggests that students scoring high in
auditory preference may be less comfortable with reading than other students are. The observed correlation
between the LSS visual verbal scale and
the auditory subscale was -.21 (n = 161,
p < .01). Thus, the only significant association found in this study between
learning styles and learning was consistent with the conclusion that students
who prefer to listen more than read do
not do as well in school as those who
are comfortable reading.
It should be noted that some of the
other subscales studied here were nearly significant. In Table 2, I show the par-
tial correlations and significance levels
associated with the subscales excluded
from the regression model; I sort these
data by level of significance. At the top
of this list is the visual verbal scale
mentioned previously. The positive
coefficient of .154 indicates that students who are comfortable reading and
have confidence in their verbal skills
may learn more in a traditionally taught
course than those who have less comfort
and confidence with verbal skills. The
positive coefficient associated with
active/reflective, at .146, indicates that
students who are more reflective than
active, or who prefer thinking and listening to doing and talking, may learn
more in a course like this one. It should
be reiterated that these coefficients did
not achieve statistical significance and
must be interpreted with caution. If the
sample were larger, these effects may
have achieved statistical significance,
but even if they did, the expected effect
sizes would still be fairly small by widely accepted standards (Cohen [1977]
described a correlation of .10 as small
and one of .30 as medium).
Discussion and Conclusions
The results of this study indicate that
only one of the eight learning style subscales studied, the sensing/intuitive subscale of the ILS, achieved what is commonly regarded as acceptable reliability.
These findings represent a warning flag
to researchers who might be tempted to
use a convenient scale without first
inspecting its reliability. We should note
that reliabilities may vary from school to
school, so the present research should
not be regarded as a condemnation of the
scales presented but merely as a
TABLE 2. Variables Excluded in Stepwise Regression
Subscale
Visual verbal
Active/reflective
Kinesthetic
Sensing/intuitive
Visual nonverbal
Visual/verbal
Sequential/global
Partial
correlation
p value
.154
.146
–.110
–.108
–.092
.035
–.031
.052
.065
.166
.175
.245
.663
.697
reminder to researchers to validate the
scales of interest on the populations that
they wish to study before making a
wholesale adoption of the measures.
In this study, I also found very little
effect of learning styles on learning outcomes. Only one subscale was significantly associated with learning, and with
an explained variance of 5%, the size of
this effect would be considered midway
between small and medium (Cohen
[1977] described an R2 of .01 as small
and an R2 of .09 as medium). Further, the
nature of the effect found in the present
study indicates that verbal aptitude or
verbal fluency may have more influence
on learning than does learning style.
(Also, the weak reliability of the pretest
may not have controlled adequately for
the effect of verbal aptitude.) Thus,
rather than adjusting a course to meet the
needs of underperforming students by,
for example, increasing the frequency of
auditory learning opportunities, the
underperforming students might be better served by remedial interventions targeted at raising their verbal comfort and
confidence. As previously noted, the
present course, in which auditory learners underperformed other students, was
approximately 90% lecture.
In summary, the present research
found that two learning style measures
freely available on the Internet exhibited poor reliabilities in this student population and had little meaningful impact
on student learning. Thus, although currently there is great interest in learning
styles among business education
researchers, these results indicate that
those researchers should first check the
reliabilities of the measures that they
plan to use as a foundation for their
research. The present study also serves
as a call for more careful examination of
the size of the effect, if any, that learning styles have on learning outcomes.
ACKNOWLEDGMENT
I gratefully acknowledge the insightful comments received from Kim A. Stewart on an earlier
draft and the financial assistance from the Daniels
College of Business, which was instrumental in
the completion of this research.
REFERENCES
Bloom, B. S. (1976). Human characteristics and
school learning. New York: McGraw-Hill.
March/April 2004
207
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:23 12 January 2016
Cohen, J. (1977). Statistical power analysis for
the behavioral sciences (revised ed.). New
York: Academic Press.
Curry, L. (1990, October). A critique of the
research on learning styles. Educational Leadership, 50–56.
Felder, R. M. (1996). Matters of style. ASEE
Prism, 6(4), 18–23.
Felder, R. M., & Silverman, L. K. (1988). Learning styles and teaching styles in engineering
education. Engineering Education, 78(7),
674–681.
Felder, R. M., & Soloman, B. A. (1999). Learning
styles and strategies. North Carolina State University. Retrieved October 20, 2001, from
http://www.uncw.edu/cte/soloman_felder.htm
Freedman, R. D., & Stumpf, S. A. (1980). Learning style theory: Less than meets the eye. Academy of Management Review, 5(3), 445–447.
Freeman, J. L., Hanson, R. C., & Rison, F. (1998).
Student learning styles, satisfaction and perfor-
208
Journal of Education for Business
mance. Proceedings of the 1998 Western Decision Sciences Conference, Reno, Nevada.
Huxham, M., & Land, R. (2000). Assigning students in group work projects: Can we do better
than random? Innovations in Education and
Training International, 37(1), 17–22.
Jester, C. (2000). Introduction to the DVC learning style survey for college. Retrieved October
19, 2001, from http://www.metamath.com/
lsweb/dvclearn.htm
Karakaya, F., Ainscough, T. L., & Chopoorian, J.
(2001). The effects of class size and learning
style on student performance in a multimediabased marketing course. Journal of Marketing
Education, 23(2), 84–90.
Lemire, D. (2000). Research report—A comparison of learning styles scores: A question of concurrent validity. Journal of College Reading
and Learning, 31(1), 109–116.
Loo, R. (2002). A meta-analytic examination of
Kolb’s learning style preferences among busi-
ness majors. Journal of Education for Business,
77(5), 252–256.
Miller, S. (2000). A learning style survey for college. Retrieved October 24, 2001, from
http://www.metamath.com//multiple/multiple_
choice_questions.cgi
Nunnally, J. C. (1978). Psychometric theory (2nd
edition). New York: McGraw-Hill.
Soloman, B. A., & Felder, R. M. (2000). Index of
learning styles questionnaire. Retrieved October 20, 2001, from http://www.ncsu.edu/felderpublic/ILSdir/ilsweb.html
Tom, G., & Calvert, S. (1984). Learning style as a
predictor of student performance and instructor
evaluations. Journal of Marketing Education,
6(2), 14–17.
Wynd, W. R., & Bozman, C. S. (1996). Student
learning style: A segmentation strategy for
higher education. Journal of Education for
Business, 71(4), 232–235.