# The Relationship of Math Anxiety and Gender

**Martha Tapia****, ****Berry**** ****College**

**George E. Marsh II****, The ****University**** of ****Alabama**

*Martha Tapia** is assistant professor of mathematics education at **Berry** **College** where she teaches mathematics and mathematics education courses. Her research agenda includes attitudes toward mathematics, technology in education, and emotional intelligence.*

*George E. Marsh II** is a professor of instructional technology in the Institute of Interactive Technology at the **University** of **Alabama**. He teaches research and technology courses. His research agenda includes attitudes toward mathematics, technology in education, and distance education.*

**Abstract**

The effects of mathematics anxiety and gender on attitudes toward mathematics were examined using the Attitudes Toward Mathematics Inventory (ATMI). A sample of 134 students enrolled in mathematics classes in a state university was asked to complete the ATMI. Data were analyzed using a multivariate factorial model. In this sample, the results showed that gender had no effect on attitudes toward mathematics, and gender and math anxiety had no influence on attitudes toward mathematics. There was an overall significant effect of math anxiety on self-confidence, enjoyment and motivation with large effect size. Students with no math anxiety scored significantly higher in enjoyment than students with high math anxiety. Students with little or no math anxiety scored significantly higher than students with some or high math anxiety in measures of self-confidence and motivation. Students with some math anxiety scored significantly higher in motivation than those with high math anxiety.

**Introduction**

It is indisputable that males have higher achievement in mathematics and higher levels of enrollment in mathematics courses (Hanna, 2003), but whether these results are caused by socialization factors or innate differences has been a matter of dispute. Gender differences in mathematics have long been explained as deficits, particularly inferior spatial visualization among girls (Collins & Kimura, 1997). Some presume this to be a sex-linked characteristic of females. Justification for this point of view is often based on deficits found in boys, such as higher levels of reading disabilities and attention deficit disorders, as well as the superiority of males on spatial tests (Nass, 1993; Nordvik & Amponsah, 1998). As a result, innate differences have long been used to explain the performance gap between the sexes. A report by the American Association of University Women (1992) blames achievement differences on differential treatment of girls in the classroom, curricula that either ignores or stereotypes women, and gender bias that undermines girls’ self-esteem.

Boys and girls have similar mathematics and science proficiency scores on tests at the age of 9, but a gap begins to appear at around age 13, or at least this has been the pattern from 1973 to 1994 on the National Assessment of Educational Progress (NAEP). However, in 1994 there was no measurable difference in the math proficiency of 13-year-old boys and girls (Campbell, Reese, O’Sullivan, & Dossey, 1996). If there was a problem in spatial visualization or other innate sexual-biological traits explaining math differences, they suddenly cleared up about a decade ago.

According to the Third International Math and Science Study (TIMSS) results, among participating countries, girls and boys had similar average mathematics achievement scores (U.S. National Research Center, 1996). However, on the NAEP, 17-year-old females have consistently scored lower, on average, than 17-year-old males, and in 1994, they were 5 scale-score points lower than males (Campbell et al., 1996). Even more interesting, average mathematics scores among 17-year-girls turned down between 1973 and 1982, but increased in 1994** **to a level similar to the age cohort in 1973 (Campbell et al., 1996).

Students who do well in mathematics have more positive attitudes about the subject, thus they are likely to take more courses and may perform better. Attitudinal research has been limited and instruments have been lacking, but generally the questions asked of students show mixed findings about 13-year-old boys and girls. Linn and Hyde (1989) reported that attitudes are more negative for girls earlier than age 13, but the Longitudinal Study of American Youth found no differences for 7th-grade students (Miller, Kimmel, Hoffer, & Nelson, 1999).

Students’ attitudes are clearly important, but little is known about the factors that intervene to create significant differences. It is clear that career aspirations of boys and girls are quite different beginning around age 13, which must be explained by social and cultural factors, because girls at that age have virtually identical abilities in mathematics. Boys are twice as likely to say they want to become scientists or engineers, but girls express a preference for professional, business, or managerial occupations (U.S. Department of Education, 1990).

Enrollment patterns of college undergraduates show that few students anticipate a career in science, mathematics or engineering, and very few major in mathematics, in fact, less than 1 percent of undergraduates (Haycock & Steen, 2002). The Conference Board of the Mathematical Sciences (Lutzer & Maxwell, 2000) showed that bachelor degrees granted in mathematics fell 19 percent between 1990 and 2000, although undergraduate enrollment rose 9 percent. Attitudinal research among college students has not been thoroughly investigated. This study was an effort to determine if there are gender differences in college, a level where there has been little research compared to that at the K-12 level.

*Method*

*Subjects*

The subjects were 134 undergraduate students enrolled in mathematics classes at a state university in the southeast. Seventy-one subjects were male and 58 were female. Five participants did not report their gender. Approximately 80% of the sample was Caucasian and about 20% African-American. The ages of the sample ranged from 17 to 34. Ten participants did not report their ages. All subjects were volunteers and all students in the classes agreed to participate.

*Materials*

*Materials*

The Attitudes Toward Mathematics Inventory (ATMI) consists of 40 items designed to measure students’ attitudes toward mathematics (Tapia, 1996). The items were constructed using a Likert-format scale of five alternatives for the responses with anchors of 1: strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree. Eleven items of this instrument were reversed items. These items were given appropriate value for the data analyses. The score was the sum of the ratings.

A Student’s Demographic Questionnaire was also used. This questionnaire consisted of four questions. The purpose of these questions was to identify gender, age, ethnic background, and level of math anxiety. Level of math anxiety consisted of four levels (none, little, some, high).

Exploratory factor analysis of the ATMI using a sample of high school students resulted in four factors identified as self-confidence, value, enjoyment, and motivation. Self-confidence consisted of 15 items. The value scale consisted of 10 items. The enjoyment scale consisted of 10 items. The motivation scale consisted of five items. Alpha coefficients for the scores of these scales were found to be .95, .89, .89, and .88 respectively (Tapia, 1996).

**Procedure**

The ATMI was administered to participants during their mathematics classes. Directions were provided in written form and students recorded their responses on computer scannable answer sheets.

**Results**

Tapia (1996) found a four-factor solution from an exploratory factor analysis with maximum likelihood method of extraction and a varimax, orthogonal, rotation. The names for the factors reported were self-confidence, value of mathematics, enjoyment of mathematics, and motivation. Based on that factor analysis, the 40 items were classified into four categories each of which was represented by a factor. A composite score for each category was calculated by adding up all the numbers of the scaled responses to the items belonging to that category. Cronbach alpha coefficients were calculated for the scores of the scales and were found to be .96 for self-confidence, .93 for value, .88 for enjoyment, and .87 for motivation.

The data were analyzed by using multivariate factorial model with the four factors as dependent variables: (1) self-confidence, (2) value, (3) enjoyment, and (4) motivation and two independent variables: (1) gender and (2) level of math anxiety. Multivariate analysis of variance (MANOVA) was performed by using SPSS.

Data were analyzed testing for interaction effect and main effect at the .05 level. Data analysis indicated that the two-way interaction effect of the two variables Gender*MathAnxiety on the four dependent variables self-confidence, value, enjoyment, and motivation was insignificant with small effect size (Wilks’ Lambda *F *= 1.117, *p* < .35, *eta squared *= .04). Hence, it was concluded that there was not enough evidence to indicate a two-way multivariate interaction. The results also showed that the main effect of gender was insignificant with small effect size (Wilks’ Lambda *F*= 1.018, *p* < .40, *eta** squared* = .03), but the main effect of mathematics anxiety was significant with large effect size (Wilks’ Lambda *F *= 7.237, *p* < .00, *eta** squared *= .19). So it was concluded that there was enough evidence to say that there was an effect of the variable level of math anxiety on the four dependent variables self-confidence, value, enjoyment, and motivation. Therefore, follow ups were conducted.

Tests of between-subject effects showed that the effect of math anxiety to three of the four dependent variables was significant with large effect size. There was enough evidence to say that there was an effect of math anxiety on the variables self-confidence (*F*(3,121) = 31.158, *p* < .00, *eta** squared* = .44), enjoyment (*F*(3,121) = 9.614, *p* < .00, *eta** squared* = .19), and motivation (*F*(3,121) = 13.179, *p* < .00, *eta** squared* = .25).

Estimated marginal means in self-confidence were 62.96 (*SD* = 2.12) for students with no math anxiety, 57.64 (*SD* = 1.68) for students with little math anxiety, 48.89 (*SD* = 1.71) for students with some math anxiety, and 36.42 (*SD* = 2.16) for students with high math anxiety. Pairwise comparisons showed students with no or little math anxiety scored significantly higher in self-confidence than students with high math anxiety.

In enjoyment estimated marginal means were 36.78 (*SD* = 1.49) for students with no math anxiety, 34.37 (*SD* = 1.19) for students with little math anxiety, 31.74 (*SD* = 1.20) for students with some math anxiety, and 26.08 (*SD* = 1.52) for students with high math anxiety. Pairwise comparisons showed students with no math anxiety scoring significantly higher in enjoyment than students with high math anxiety.

Estimated marginal means in motivation were 17.06 (*SD* = 0.79) for students with no math anxiety, 16.14 (*SD* = 0.63) for students with little math anxiety, 13.65 (*SD* = 0.64) for students with some math anxiety, and 10.88 (*SD* = 0.80) for students with high math anxiety. In motivation pairwise comparisons showed students with no or little math anxiety scoring significantly higher in motivation than students with some or high math anxiety and students with some math anxiety scoring significantly higher than students with high math anxiety.

*Discussion*

With the multiple analysis of variance, one interaction and two main effects were found: (a) Gender did not have an effect on attitudes toward mathematics; (b) Different levels of math anxiety by gender classification had no effect on attitudes, (c) Levels of math anxiety had an effect on attitudes toward math, independent of gender, and (d) The level of math anxiety had an effect on measures of self-confidence, enjoyment, and motivation.

Students with no math anxiety scored significantly higher in enjoyment than students with high math anxiety. Self-confidence was significant, with students having little or no math anxiety scoring significantly higher than students with some or high math anxiety. Motivation was also significant and had an inverse relationship: students having little or no math anxiety scored significantly higher than students with some or high math anxiety, and students with some math anxiety scored significantly higher than students with high math anxiety.

Jordan and Nettles (1999), who analyzed data from the National Educational Longitudinal Study of 1988 (NELS), reported that girls had lower scores than boys on math in the12th grade, which is a pattern that exists in many other countries (Hanna, Kundiger, & Larouche, 1990). Numerous studies have shown that male achievement in math is higher (Entwistle, Alexander, & Olson, 1994; Gamoran, 1992; Callahan & Clements, 1984; Dossey, Mulis, Lindquist, & Chambers, 1988). Due to the fact that gender differences do not appear until around puberty, and they appear in several countries, the differences have often been attributed to innate biological differences, social factors, and anxiety among females (Callahan & Clements, 1984; Dossey et al., 1988). The logic has been that, because females take the same courses, learn under the same conditions, but have lower scores, must be intervening factors to explain the difference. One of the first studies about math anxiety was by Richardson and Suinn (1972), whose work drew attention to the problem. Since then, the literature has included results of studies about math anxiety and its effect on math achievement (Stent, 1977; Betz, 1978; Hembree, 1990). Research has shown that females, as a group, do not enjoy math and often see it as having little relationship to their lives or their futures (Fennema & Sherman, 1978). Females display more math anxiety than males in secondary school and college (Woodard, 2004).

While girls at various ages may have cultural or social pressures that help shape their attitudes about mathematics as a subject of study or an element in a future career, results with this sample of college-age students showed that the main effect of gender** **was insignificant. From these results, we conclude that feeling good about mathematics is not related to gender among this group of college students, but rather it is likely to be something related to individual, personal experiences. While the literature has reported a high relationship between math anxiety and gender, in this sample of students it is clear that math anxiety is unrelated to gender.

**References**

American Association of University Women. (1992). *How schools shortchange girls:* *A study of major findings on girls and education*. Washington, DC: AAUW Educational Foundation, The Wellesley College Center for Research on Women.

Betz, N. E. (1978). Prevalence, distribution, and correlates of math anxiety in college students. *Journal of Counseling Psychology*, 25, 441-448.

Callahan, L. G., & Clements, D. H. (1984). Sex differences in rote-counting ability on entry to first grade: Some observations. *Journal of Research in Mathematics Education*, 15, 378-382.

Campbell, J. R., Reese, C. M., O’Sullivan, C. Y., & Dossey, J. A. (1996). *NAEP 1994 trends in academic progress: Achievement of U.S. students in science, 1969 to 1994, mathematics, 1973 to 1994, reading 1971 to 1994, and writing, 1984 to 1994*. Washington, DC: National Center for Education Statistics.

Collins, D. W., & Kimura, D. (1997). A large sex difference on a two-dimensional mental rotation task. *Behavioral Neuroscience*, 111(4), 845-849.

Dossey, J. A., Mulis, I. V. S., Lindquist, M. M., & Chambers, D. L. (1988). *The mathematics report card: Are we measuring up? Trends and achievement based on the 1986 National Assessment*. Princeton: Educational Testing Service.

Entwistle, D. R., Alexander, K. L., & Olson, L. S. (1994). The gender gap in math: Its possible origins in neighborhood effects. *American Sociological Review*, 59, 822-838.

Fennema, E., & Sherman, J. (1978). Sex related differences in mathematics achievement and related factors: A further study. *Journal for Research in Mathematics Education*, 9, 189-203.

Gamoran, A. (1992). The variable effects of high school tracking. *American Sociological Review*, 57(6), 812-828.

Hanna, G. (2003). Reaching gender equity in mathematics education. *The Educational Forum,* 67(3), 204-214.

Hanna, G., Kundiger, E., & Larouche, C. (1990). Mathematical achievement of grade 12 girls in fifteen countries. In L. Burton (Ed.), *Gender and mathematics: An international perspective*. London: Cassell Educational Ltd. Pp. 87-97.

Haycock, K. & Steen, L. A. (2002) . Add it up: Mathematics education in the U.S. does not compute. *Thinking K-16*, 6, 1.

Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. *Journal for Research in Mathematics Education*, 21, 33-46.

Jordan, W. J., & Nettles, S. M. (1999). *How students invest their time out of school: Effects on school engagement, perceptions of life chances, and achievement* (Report No. 29). Washington, D.C.: Center for Research on the Education of Students Placed At Risk.

Linn, M. & Hyde, J. (1989). Gender, mathematics, and science. *Educational Researcher* 18, 17-19, 22-27.

Lutzer, D. J. & Maxwell, J. W. (2000). *Statistical abstract of undergraduate programs in the mathematical sciences in the United States*. Washington, D.C.: Conference Board of Mathematical Sciences.

Miller, J. D., Kimmel, L., Hoffer, T. B., & Nelson, C. (1999). *Longitudinal study of American youth: User’s manual*. Chicago: International Center for the Advancement of Scientific Literacy, Chicago Academy of Sciences.

Nass, R. D. (1993). Sex differences in learning abilities and disabilities. *Annals of Dyslexia*, 43, 61-78.

Nordvik, H. & Amponsah, B. (1998). Gender differences in spatial abilities and spatial ability among university students in an egalitarian educational system. *Sex Roles: A Journal of*

* Research*, June, 1998. Online: http://www.findarticles.com/cf_dls/m2294/n11-

12_v38/21109782/p1/article.jhtml

Richardson, F. C. & Suinn, R. M. (1972). The mathematics anxiety rating scale: Psychometric data. *Journal of Counseling Psychology*, 19, 551-554.

Stent, A. (1977). Can math anxiety be conquered? *Change*, 9, 40-43.

Tapia, M. (1996). *The attitudes toward mathematics instrument*. Paper presented at the annual meeting of the Mid-South Educational Research Association, Tuscaloosa, AL (ERIC Reproduction Service No. ED 404165).

Woodward, T. (2004). The effects of math anxiety on Post-Secondary developmental students as related to achievement, gender, and age. *Inquiry*, 9(1), Spring 2004. Online: http://www.vccaedu.org/inquiry/inquiry-spring2004/i-91-woodard.html

U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics. (1990). *A profile of the American eighth-grader: NELS:88 Student descriptive summary*, Washington, D.C.: Government Printing Office.

U.S. National Research Center. (1996).*Third international math and science study* (Report No. 7). East Lansing, MI: Michigan State University.

## Recent Comments