Brain Hemisphericity And Academic Majors: A Correlation Study – Statistical Data Included
This article reports on a study that investigated the correlation between students’ choice of academic majors and their brain hemisphericity. The participants in this research were 429 graduate and undergraduate students in a large university in the southern part of the United States. The data were analyzed using analysis of variance to determine the influence of brain hemisphericity on students’ choice of academic majors. The results lent support to earlier research in their findings of a strong correlation between academic majors and brain dominance. The ANOVA model showed a significant effect of brain hemisphericity on students’ choice of academic majors. Arts/literature students tended to be right brained while business/commerce students were left brained. Students majoring in education, nursing, communication, and law were right brained, while students majoring in business/commerce, engineering, and science were left brained. The study also demonstrated an evidence of a general shift in students’s brain hemisphericity from earlier research, where more students were identified as whole brained.
Research has demonstrated the importance of understanding brain behavior as it relates to learning styles and personality traits. In particular, studies revealed that brain hemisphericity greatly influences the individual’s learning style and all kinds of intellectual and personality characteristics (Boyle & Dunn, 1998: McCarthy, 1996; Shiflett, 1989; Torrance, 1982).
This study examines the relationship between brain hemisphericity and college students’ choice of academic majors. The results of this research should help teachers, school counselors, and college advisors to better understand their students’ interests and abilities and steer them towards fields or academic majors that are compatible with their interests.
Brain hemisphericity is the tendency of an individual to process information through the left hemisphere or the right hemisphere or in combination (Bradshaw & Nettleton, 1981; McCarthy, 1996; Springer & Deutsch, 1993). Research has demonstrated that the left hemisphere operates in a linear, sequential manner with logical, analytical, propositional thought. On the other hand, the right hemisphere operates in a nonlinear, simultaneous fashion and deals with non-verbal information as well as dreams and fantasy (Iaccino, 1993; McCarthy, 1996; Oxford, 1996; Oxford, Ehrman, & Lavine, 1991; Springer & Deutsch, 1993; Torrance, 1988). The left hemisphere appears to be specialized for language, whereas the right hemisphere is specialized for visuo-spatial and appositional thought. Kinsella (1995), Oxford (1996), and Oxford, Ehrman, and Lavine (1991) maintained that left hemispheric dominants are highly analytic, verbal, linear and logical learners, whereas right-hemispheric dominants are highly global, visual, relational, and intuitive learners. Whole-brain dominants are those who process information through both hemispheres equally and exhibit characteristics of both hemispheres. Those individuals have flexible use of both hemispheres (McCarthy, 1996).
Even though most of the literature appears to list characteristics associated with each of the brain hemispheres as dichotomies, the idea of hemispheric dominance suggests that brain hemisphericity operates on a continuum and is not dichotomous (Saleh & Iran-Nejad, 1995). It is important to keep in mind that individuals have different degrees of dominance, which affect to what degree they exhibit these characteristics.
Research has demonstrated that students are capable of mastering new skills if they are taught through instructional methods that complement their hemispheric preference (Boyle & Dunn, 1998; Dunn, Sklar, Beaudry, & Bruno, 1990). Several studies have found that students taught through methods that matched their hemispheric styles achieved statistically significant higher test scores than when they were taught through other teaching methods (Brennan, 1984; Dunn, Sklar, Beau&y, Bruno, 1990; Jarsonbeck, 1984).
Studies have suggested that brain hemisphericity is associated with different occupations and academic majors (Kolb, 1979; McCarthy, 1996). Kolb believed people choose majors/fields based on congruence between their learning styles and the norms of those majors/fields (1979). People choose their academic majors based on the compatibility between the norms of these disciplinary fields and the individual’s hemispheric dominance (Kolb, 1979; Gordon & Coscarelli, 1986; Rowe, Waters, Thompson, & Hanson, 1992). Academic subjects such as arts, the humanities, and architecture are believed by several researchers to require a more global, synthetic, and spatial orientation which make them more suitable for right-brain dominant students, whereas other subjects such as science, engineering, and language emphasize logic and verbal analysis, which make them a better fit for left-brain dominant students (Coulson & Strickland, 1986; Herrman, 1982; Katz, 1983). Lavach (1991) examined the brain hemisphericity of students with different majors. He reported that humanities students showed preference for the right-hemispheric dominance. Natural science students demonstrated a left-hemispheric mode, while social science majors showed preference for left-hemispheric dominance.
The present study adds to this line of inquiry by examining the link between brain hemisphericity and choice of college major. In the following section, the methodology will be introduced.
The participants in this study were students selected randomly at a large southern university; 402 students were undergraduates and 27 were graduates. Students were assured anonymity. The number of females in this sample was twice the number of males (females constituted 66.43% of the sample, while males constituted 33.57%). This could be attributed to the fact that a large number of the participants were drawn from the college of education and the foreign languages department where females constituted the majority.
The data were collected over a period of two years. The participants were asked to complete a demographic survey as well as McCarthy’s Hemispheric Mode Indicator (HMI) instrument to determine each individual’s brain hemisphericity. The Hemispheric Mode Indicator was developed to measure the preference in the individual’s approach to learning with a bias for right, left, or whole brain-mode processing techniques (McCarthy, 1987). The instrument has 32 items; each item consists of a continuum between two adjectives, such as “neat” and “sloppy.” On the continuum, there are four choices, the subject either chooses “a lot” or “somewhat” from one side of the continuum or “a lot” or “somewhat” from the other side of the continuum. The participant chooses one adjective and the degree to which he/she exhibits this characteristic for each item and then self scores the questionnaire. The high negative scores on the HMI continuum are associated with a left hemispheric mode, and the high positive scores are associated with a right hemispheric mode. Scores between -8 and +8 on the continuum are associated with whole-brain dominance. To be able to code and enter the data in the computer program for the statistical analyses in this study, negative and positive scores were converted into all positive scores. Scores below 72 were viewed as left hemispheric dominance, while scores above 80 were viewed as right hemispheric dominance. Scores in the range of 72 to 80 are considered balanced hemisphericity (whole brained).
The Cronbach’s alpha of the instrument is 0.90. The test-retest reliability (Pearson Product Moment correlation coefficient) is 0.904 (Lieberman, 1986). The HMI items were correlated, to check current validity, with the items of Torrance’s measure of hemispheric dominance (Your Style of Learning and Thinking, SOLAT-C). The Spearman rank correlation coefficient is 0.819. The Pearson Product-moment correlation is 0.659 (Lieberman, 1986).
Analytic techniques included descriptive statistics (frequencies, means, standard deviations, and so forth). ANOVA was used to determine the influence of brain hemisphericity on students’ choice of academic majors at p [is less than] 0.0001. SPSS and SAS computerized statistical programs were employed. In the following section, the results of the ANOVA will be presented and discussed.
The distribution of brain hemisphericity is detailed in Table 1. The three brain dominance categories were distributed in the following way: There were 124 or 28.9% left-brain dominants, 107 or 24.94% right-brain dominants, and 198 or 46.15% whole-brain dominants. Whole-brain dominants included individuals with weak preferences to the left or the right hemisphere processing mode as well as individuals with well-defined whole brain hemisphericity.
Students profile concerning brain hemisphericity
Hemispheric dominance Number (N) Percentage
Left 124 28.9
Right 107 24.94
Whole 198 46.15
The results of a one-way ANOVA performed to compare academic majors and brain hemisphericity indicate a significant difference between academic majors and brain hemisphericity. A summary of the ANOVA results is illustrated in Table 2. The sample’s means and standard deviations are depicted in Table 3.
Summary ANOVA for the whole model of the effects of Academic
majors on brain hemisphericity
df SS F p
Model 4 4420.65 8.65 0.0001
Error 383 48918.55
Corrected Total 387 53339.19
Sample Means and standard deviations for brain hemisphericity and
Major N Mean Std. Dev Variance
Arts & Literature 31 82.51 13.42 180.07
Business & Commerce 28 70.71 9.85 97.04
Education 189 81.27 11.16 124.46
Engineering & Science 54 76.95 12.52 156.94
Others 86 83.58 10.39 108.00
The results of the Tukey post hoc test showed that brain hemisphericity and academic majors can be summarized as follows (p [is less than] 0.05):
1. There was a significant difference between arts and literature majors and business majors. Arts and literature majors showed a tendency toward right-hemispheric dominance (y = 82.51), while business/commerce students showed a tendency towards left-hemispheric dominance (y = 70.71).
2. There was a significant difference between education majors and business/commerce students. Students majoring in education showed a tendency toward right-hemispheric dominance (y = 81.27), whereas business/commerce majors showed a tendency toward left-hemispheric dominance (y = 70.71).
3. There was a significant difference between students with majors other than education, arts, literature, business, engineering, and science. Nursing, communication, and law students showed a tendency toward right-hemispheric dominance (y = 83.58), while engineering/science, business and commerce students showed a tendency toward left-hemispheric dominance (y=76.95, 70.71).
Discussion and Implications
Results indicate that students majoring in science, engineering, and business showed a left- brain dominance. Students majoring in arts, literature, and education tended to be right-hemispheric dominants. Students with majors such as communication, law, and nursing were also found to have right-brain dominance. These results support previous studies, which found students majoring in areas such as arts and humanities demonstrated a right-hemispheric dominance, whereas students majoring in areas such as business, engineering and science show a left-hemispheric dominance (Bakan, 1969; Dabbs, 1980; Kolb, 1979; Lavach, 1991; McCarthy, 1996: Rowe, Waters, Thompson, & Hanson, 1992; Witkin et al., 1977). The results appear to confirm that students choose to study subjects that accommodate their cognitive/learning styles.
It is interesting to note the large percentage of the sample with whole-brain dominance, which represent a shift in brain hemisphericity than reported in earlier studies that contended that the majority of students in western schools are left-brain dominants (Bogen, 1975: Ornstein, 1977; Perrone & Pulvino, 1977; Springer & Deutsch, 1993; Stellern, Collins, Gutierrez, & Patterson, 1986; TenHouten, 1989; Williams & Murr, 1987). One might venture to hypothesize that this shift in brain hemisphericity could be attributed to the teachers’ increased awareness of learning styles and brain hemisphericity and to their efforts to include a variety of teaching methods and learning activities in order to accommodate more cognitive styles. This shift could also be due to students’ increased exposure to video games and computer activities that are believed to promote imagination and spatial skills. These abilities are associated with right brain characteristics (William & Murr, 1987).
This information can serve to help administrators in secondary schools and advisors in higher education to place students in programs that are compatible with their interests and abilities. Also, it can help students select programs that will meet their needs. When a school counselor or a college advisor is working with a student who is undecided on a major, knowledge of the student’s interests, learning styles, and brain dominance will help him/her give the student some insights into career choices. These insights, if acted on, could help decrease the number of students dropping out of college or changing majors several times because of the lack of fit between their cognitive styles and the requirements of certain fields (Kolb, 1979; McCarthy, 1996).
It is also important for school counselors and college advisors to increase their understanding of students’ cognitive styles and their relationship to student’s academic achievement, motivation, and drop out rates. Financial resources should be provided for counselors and college advisors to attend workshops designed to train counselors and advisors to administer learning styles or brain dominance surveys to students and on how to educate students on the congruence between learning styles and some educational fields.
Further research is needed to examine how this knowledge can help educators, administrators, and students improve the learning process for everyone involved. More research with subjects of different ages and backgrounds will add support to this study and provide educators at the secondary and college levels with the knowledge necessary to better teach their students. Also, follow up studies into the shift in students’ brain dominance, evidenced in this research, is important to examine if the shift could be attributed to changes into teaching practices or to changes in the environment and technology available to students. This knowledge is vital to educators since the literature on learning styles in the last three decades has accused teachers in western schools of catering to one learning style that correlate only with left hemispheric skills (Dunn, Sklar, Beaudry,& Bruno, 1990; Dunn, Sklar, Beaudry, & Bruno, 1990; McCarthy, 1987, 1996). The results of this study, supported with further research, might prove that teachers are becoming more aware of the impact of learning styles and brain dominance on students’ learning and they are accommodating the right hemispheric skills more often than in the past.
Bakan, P. (1969). Hypnotizability, laterality of eye movement, and functional brain asymmetry. Perceptual and Motor Skills, 28, 727-932.
Bogen, J. E. (1975). The other side of the brain II: Some educational aspects of hemispheric specialization. UCLA Educator, 17, 24-32.
Boyle, R.A. & Dunn, R. (1998). Teaching law students through individual learning styles. Albany Law Review, 62, 213-255.
Bradshaw, J. & Nettleton, N. (1981). The nature of hemispheric specialization in man. The Behavioral and Brain Sciences, 4, 51-91.
Brennan, P. (1984). An analysis of the relationships among hemispheric preference and analytic global cognitive style, two elements of learning style, method of instruction, gender, and mathematics achievement of tenth grade geometry students. (Doctoral dissertation, St. John’s University, 1984). Dissertation Abstracts International, 45 (11).
Coulson, L.T. & Strickland, A. (1986). The minds at the top. Journal of Creative Behavior, 17(3), 163-174.
Dabbs, J. (1980). Left-right differences in cerebral blood flow and cognition. Psychophysiology, 17, 548-551.
Douglas, C. (1979). Making biology easier to understand. The American Biology Teacher, 41(5), 277-299.
Dunn, R., Sklar, R. I., Beaudry, J.S., & Bruno, J. (1990). Effects of matching and mismatching minority developmental college students’ hemispheric preferences on mathematics scores. Journal of Educational Research, 83 (5), 283-288.
Dunn, R., Sklar. R. I., Beaudry, J. S., & Bruno, J. (1990). Effects of matching and mismatching minority developmental college students’ hemispheric preferences on mathematics scores. Journal of Educational Research, 83 (5), 283,-288.
Gordon. V. N. & Coscarelli, W. C. (1986). Comparative assessments of individual differences in learning and career decision making. Journal of College Student Personnel, 27, 233-247.
Herrmann, N. (1982). The Creative brain. NASSP Bulletin, 31-45.
Iaccino, J. F. (1993). Left brain-right brain differences: Inquiries, evidence, and new approaches. Hillsdale, NJ.: Lawrence Erlbaum Associates Publishers.
Jarsonbeck, S. (1984). The effects of a right-brain and a mathematics curriculum on low achieving fourth grade students. (doctoral dissertation. University of south Florida, 1984). Dissertation Abstracts International, 45, 2791A.
Katz, A.N. (1983). Creativity and individual differences in a symmetric cerebral hemispheric functioning. Empirical Studies of the Art, 1, 3-15.
Kinsella, K. (1995). Understanding and empowering diverse learners in the ESL classroom. In Joy M. Reid, Learning styles in the ESL/EFL classroom. Boston, Mass: Heinle & Heinle Publishers.
Kolb. D. A. (1979). Organizational psychology: A book of readings. Englewood Cliffs, N.J.: Prentice-Hall.
Lavach, J. (1991). Cerebral hemisphericity, college major and occupational choices. Journal of Creative Behavior, 25(3), 218-222.
Lieberman, M.G. (1986). Hemispheric mode indicator: Technical notes. Barrington, IL: Excel, INC.
McCarthy, B. (1987). The 4mat system: Teaching to learning styles with right/left mode techniques. Barrington, IL:Excel, Inc.
McCarthy, B. (1996). The 4mat system research: reviews of the literature on the differences and hemispheric specialization and their influence on learning. Barrington, IL: Excel, Inc.
Ornstein, R. (1977). The psychology of consciousness. NY: Harcourt Brace Jovanovich.
Oxford, R.L. (1996). Gender differences in language learning styles: What do they mean? In Joy M. Reid, Learning styles in the ESL/EFL classroom. Boston, Mass: Heinle & Heinle Publishers.
Oxford, R., Ehrman, M., & Lavine, M. (1991). Style wars: Teacher-student style conflicts in the language classroom. In S. S. Magnon (Ed.), Challenges in the 1990s for college foreign language programs (pp. 1-25). Boston: Heinle & Heinle.
Perrone, P. & Pulvino, C. (1977). New directions in the guidance of the gifted and talented. The Gifted Child Ouarterly, 3, 326-340.
Rowe, F. A., Waters, M. L., Thompson, M. P., & Hanson, K. (1992). Can personality-type instruments profile majors in management programs? Journal of Education for Business, 10-14.
Saleh, A. & Iran-Nejad, A. (1995). Wholetheme constructivism and whole-brain education: educational implications of the research on left and right brain hemispheres. A paper presented at the Annual Meeting of the Mid-South Educational Research Association. (ERIC Document Reproduction Service NO. ED 393 896).
Shiflett, S.C. (1989). Validity evidence for the Myers Briggs Type Indicator as a measure of hemisphere dominance. Educational and Psychological Measurements, 49(3), 741-745.
Springer, S.P. & Deutsch, G. (1993). Left Brain, Right Brain. New York, N.Y.:W.H. Freeman & Company.
Stellern, J., Collins, J., Gutierrez, B., & Patterson, E. (1986). Hemispheric dominance of Native American Indian students. Journal of American Indian Education, 8-18
TenHouten, W.D. (1989). Application of dual brain theory to cross-cultural studies of cognitive development and education. Sociological Perspectives, 32 (2), 153-167.
Torrance, E.P. (1982). Hemisphericity and creativity. Journal of Research and Development in Education, 15(3), 29-37.
Torrance, E.P. (1988). Style of learning and thinking administrator’s manual. Bensenville, IL: Scholastic Testing Service.
Torrance, E.P., Taggart, B., & Taggart, W. (1984). Human information processing survey. Bensenville, IL: Scholastic Testing Service.
Trautman, P. (1979). An investigation of the relationship between selected instructional techniques and identified cognitive style. (Doctoral dissertation, St. John’s University, 1979). Dissertation Abstracts International, 40, 1428A.
Williams, J.B. & Murr, L. (1987). Desk top publishing: New right brain documents. Library Hi Tech, 5(1), 7-13.
Witkin, H. A. Moore, C.A., Goodenough, D.R., & Cox, P. W. (1977). Field-dependence and field-independence cognitive styles and their educational implications. Review of Educational Research, 427, 1-64.
AMANY SALEH, PH.D.
Arkansas State University
COPYRIGHT 2001 Project Innovation (Alabama)
COPYRIGHT 2001 Gale Group