Brain-Type, Gender, and Student Success in the Principles of Economics

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1 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Brain-Type, Gender, and Student Success in the Principles of Economics Duane B. Graddy and Fang Yang 1 ABSTRACT Studies of the impact of gender on student performance in the principles of economics yield ambiguous conclusions. Some studies find that women are less successful than men in the principles of economics, while others conclude that gender is inconsequential. Few, if any, scientific works find women s performance better than men s performance. These ambiguities arise because past studies have not considered the distribution of brain-types by gender. This paper addresses the question of which brain-types are more successful in the principles of economics and whether these brain-types can be associated with a particular gender. Introduction The conclusions drawn from studies of the role of gender on student performance in the principles of economics are equivocal. Some studies 2 find that gender has no effect on class performance, while others conclude that men consistently outperform women. Numerous studies 3 have addressed the issues of why women perform less well than men in the principles of economics and why women are less likely to major in economics. This gender gap has been related to learning styles, math skills, high school economics, teaching pedagogy, student personalities, faculty role models, and prior expectations. While the causes of the gap remain contentious, the overall results of these studies are consistent in finding that females, as a group, do not perform as well as males, as a group. In trying to explain these gender differences, an early study by Siegfried et al. (1979) made the intriguing finding that among individuals who had not had any college economics, men tended to be more interested in the subject than women. This finding was buttressed in a recent paper by Bollinger et al. (2006). They found that attitudes toward the subject matter differed significantly between genders. Women, as a group, had a significantly more negative attitude toward the subject of economics prior to taking principles than did men. Furthermore, while males attitudes were positive after taking the principles course, the negative attitudes of females persisted. 4 In their comprehensive survey of the literature, Ballard and Johnson (2005) hypothesized that the poorer performance of women in the principles of economics was due to prior expectations of success in the course. In their sample, women expected to do worse than men by 0.25 of a grade point, holding other determinants of performance constant. They suggested that prior expectations have a powerful influence on student performance. Ballard and Johnson conjectured that the expectations variable was merely a proxy for some aspect of ability that was private information for the student and unobserved by the researcher. However, the issue is not ability; it is the possibility of an innate disinterest in economics by women, as a group. 5 Our question is whether results, such as those of Ballard and Johnson, can be attributable to 1 Duane B. Graddy, Professor of Economics and Finance, Middle Tennessee State University, Murfreesboro, TN 37132; Fang Yang, Research Associate, Tennessee Advisory Commission on Intergovernmental Relations (TACIR), Nashville, TN Coates and Humphreys (2001), Cohn et al. (1998), Durden and Ellis (1995), Hill and Stegner (2003). 3 Anderson et al. (1994), Ballard and Johnson (2005), Bansak and Starr (2006), Bollinger et al. (2006), Borg and Stranahan (2002a and 2002b), Ferber et al. (1983), Elzinga and Melaugh (2008), Gorhmann and Spector (1989), Hopkins (2003), Siegfried et al. (1979). 4 Jensen and Owen (1999) drew a similar conclusion in their study. In a somewhat different context, Caplan (2007) determined that the factors that make people think like economists include education, positive income growth, and being male. 5 Or what Bansak and Starr (2006) refer to as a predisposition.

2 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer differing brain types? 6 Recent work in cognitive science provides some basis for believing that this may be possible. For example, extensive work by Baron-Cohen et al. (2003) and his colleagues at the ARC Research Centre at Cambridge University has resulted in the classification of two distinct brain-types. Baron-Cohen refers to these brain classifications as Type E and Type S. 7 Individuals with Type E brains are better at empathizing (E) than systemizing (S). In contrast, Type-S brains systemize better than empathize. The hypothesis set forth in the present paper is that gender cannot be viewed in isolation; i.e., what Ballard and Johnson (2005), Bansak and Starr (2006), and Bollinger et al. (2006) are observing is not gender per se but variations in brain types. For example, economics may be more attractive to Type S individuals, and they may have higher grade expectations than Type E students. Referring to the work of Baron-Cohen et al. (2003), females make up a larger percentage of Type E individuals than do males. This is not to say that there are no Type S female students. It means that the probability of a Type S individual being male is substantially higher than being female. Type E students, on average, would not be expected to be as excited or confident on entering an economics course as Type S students. Again, the low expectations observed by Ballard and Johnson (2005) may reflect the interaction of gender and brain type. Furthermore, the self-selection process observed by Ballard and Johnson is likely the clustering of Type S students in the advanced economics courses. Males should, on average, represent a larger proportion of the upper-division population in economics courses than females, because males represent a larger fraction of Type S individuals. This study addresses the question whether brain types, as measured by the Baron- Cohen indices, 8 help to explain differences in student interest and performance in economics courses. Brain-Types Type E and Type S brains occur in both male and female populations. However, on average, females are more likely to be empathizers, while males, on average, are more likely to be systemizers. 9 Nevertheless, considerable overlap exists in the distributions of these traits between males and females. According to Baron-Cohen (2003), Kimura (1987,1999), and Kanazawa (2004), females (Type E s) are prone to do those things that are responsive to another person s thoughts and emotions relative to those desires in males. Males (Type S s), on the other hand, are driven to analyze, construct, and control rulebased systems. Empathizing and systemizing are both mental processes and as such can be applied to any situation. In practice, however, empathizing spontaneously attempts to identify and respond to the thoughts and emotions of other people. In contrast, the systemizing process tries to understand any system that is deterministic, law-like, and bounded. Sample and Methodology Sample The sample for this study is comprised of 186 students enrolled in four sections of the principles of microeconomics at a large state university. The sections were taught in the Fall 2005 and Spring 2006 semesters by two instructors. Each instructor taught two sections of the course. Forty-five percent of the students were in Instructor 1 s classes. The remainder attended Instructor 2 s classes. Forty-two percent of the students in Instructor 1 s class were female, while women represented 43% of Instructor 2 s class. Table 1 lists the general characteristics of the sample. The gender composition of the sample was 41.9% females and 58.1% males. Females, on average, had significantly higher overall grade point averages (GPA) than did males. Females also had a somewhat higher course average than males. However, the 6 The differences referred to in this question relate to comparisons of groups of women and men and not to specific individuals. 7 The empathy quotient (EQ) and systemizing quotient (SQ) scores are obtained from the results of individual questionnaires and are measured using the same Likert scale. 8 To distinguish between these brain-types, Baron-Cohen and his research team developed two indices. Each index is a summary score from an individually administered questionnaire. The empathy quotient (EQ) measures how easily a person recognizes other people s feelings and how strongly other people s emotions affect the person. The degree to which a person is drawn to machines, mathematics, maps, statistics, and syllogistic thinking is measured by the systemizing quotient (SQ). 9 Research including Baron-Cohen (2003), Baron-Cohen and Wheelwright (2004), Baron-Cohen et al. (2003), Hines (2004), and Kimura (1987 and 1999) shows that, on average, males are more Type S than females; i.e., as a group, males are better at systemizing than empathizing.

3 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer difference was not statistically significant. 10 Furthermore, the effect size was negligible as measured by a Cohen-d = This compares to a medium effect size of 0.48 for the GPA. Thus, while females, as a group, had a higher overall scholastic average, their average performance in the principles of economics was not different than their male counterparts. Hours worked per week did not differ between female and male students. 12 Twenty-two percent of women were classified as freshman compared to 11% of men. More than half of the males were classified as sophomores in contrast to 34% of the females. Fifteen percent of the females were categorized as seniors while 5% percent of the males were in this category. The lower portion of Table 1 lists the declared majors of the principles of economics students. Of the 186 students only two declared themselves as economics majors, and both were male students. Eighty percent of the declared finance majors were male as were 90% of the information systems majors. In contrast, 73% of the management majors and 70% of the accounting majors were female. 13 Marketing and general business attracted approximately equal numbers of declared majors. Males represented 70% of the declared majors in entrepreneurship. Other majors represent a heterogeneous category of non-business subjects such as history, political science, and mass communications. Table 1: Sample Characteristics Characteristics Female Male Number of Students Academic: GPA Course Average Avg. Hours Worked per Week Class Standing: Freshman Sophomore Junior Senior 12 6 Declared Majors: Finance 3 12 Information Systems 1 9 Management 8 3 Marketing Economics 0 2 Accounting 20 9 Entrepreneurship 5 12 General Business Other Majors Notes: The sample for this study is comprised of 186 students enrolled in four sections of the principles of microeconomics economics at a large state university. The empathy quotient (EQ) and systemizing quotient (SQ) for the study were obtained from the results of two questionnaires. 14 The questionnaires were administered to the students by the authors during regularly scheduled classes. Before completing the questionnaires the students were advised that participation was strictly voluntary and that the anonymity of their responses was maintained at all times. 10 With t statistic = and P-value = The Cohen-d coefficient (Cohen, 1988) expresses the difference between the means in units of variability. For example, the difference between the means of the GPA differed by roughly one-half a standard deviation. 12 With the t-statistic = and P-value = It should be noted, however, that the average SQ score (28) for female accounting majors was significantly above the mean score of the general population (24); the scores were in the upper tail of the female sample distribution. 14 The EQ and SQ questionnaires were developed by the ARC at Cambridge University under the direction of Simon Baron- Cohen. Each has 60 questions. Forty of the questions are scored, while 20 questions are not included in the calculations. The 20 filleritems are randomly interspersed throughout the questionnaires. For the other questions, participants are scored two points if they strongly display the characteristic being measured (either EQ or SQ) and one point if they slightly display the trait.

4 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Completing the questionnaires took about 45 minutes. Only 3% of the questionnaires were deemed unusable because of omissions, multiple answers, failure to complete both questionnaires, and illegible responses. Methodology The study was conducted in two phases. The first stage involved an analysis of the SQ, and EQ scores by gender. This phase focused on determining whether significant differences existed among the SQ and EQ scores of male and female students in the principles of economics. The second phase of the analysis estimated an ordered probit model to determine a student s probability of receiving a specific grade in the principles of economics. Particular attention was focused on the importance of the brain-type indicators as independent determinants of the probability of success in the principles of economics. The ordered probit model is a method for predicting the probability that a given observation (student) can be identified with a given discrete category (grades) on the basis of a series of independent variables. The model can be expressed in the following form. * i y = x β + e where e i ~ N(0,1), = 1..., N (1) i i, yi is the observed ordinal grade of the student. yi takes on values 4 through 0 representing the grades A * through F. yi is the predicted grade as a function of the independent variables in the model. The x i s are the independent variables, and the βs are the estimated regression coefficients. The observed y i is of the form. * y i = J μ J 1 < yi μ J (2) where J = and the μ s are unknown parameters to be estimated with the β s. The probability of receiving a given letter grade based on the slope and threshold estimates is shown in equation (3). p[ y = J ] = Φ( μ x β ) Φ( μ 1 x β ) (3) i where Φ is the standard normal cumulative distribution. J i J Results Gender and Trait Scores Table 2 includes a comparison of the SQ and EQ scores for the students in the principles of economics classes. The mean SQ score for males was significantly higher than the score of females. The difference between the means of 7.94 was significant at the level. The SQ scores of both groups were in the average range for the general population. 15 The average EQ score was significantly higher for females than for males. The difference between the means of the EQ scores was significant at the level. The last row of Table 2 shows the Cohen-d statistics for the differences between the means of each brain-type measure. All of the d-statistics were in the medium effect size range. 16 i 15 For SQ scores, the low range is 0-19; the average range is 20-39; the above-average range is 40-50; and the very high range is For EQ scores, the low range is 0-32; the average range is 33-52; the above-average range is 53-63; and the very high range is The medium effect size range is from d = 0.50 to d = 0.80 (Cohen, 1988).

5 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Table 2: Difference Between the Means of the SQ and EQ Scores Principles of Economics Students Upper-Division Public Finance Students Students GPA SQ Averages EQ Averages GPA SQ Averages EQ Averages Male CI (2.65,2.89) (33.83,38.45) (38.35,42.13) (2.85,3.23) (28.89,37.47) (37.74,45.12) Female CI (2.90,3.19) (25.49,30.89) (42.99,47.91) (2.74,3.36) (24.64,42.50) (40.57,56.86) Male - Female CI (-0.49,0.12) (4.35,11.54) (-8.29,2.13) (-0.43,0.45) (-8.94,9.72) (-1.17,15.74) t-stat P-value *** *** *** * Cohend Notes:* indicates significance at the 10% level; ** indicates significance at the 5% level;*** indicates significance at the 1% level. CI stands for the confidence interval. The sample is comprised of 186 students enrolled in four sections of the principle of microeconomics at a large state university. For upper-division, there are 28 males and 7 females. The empathy quotient (EQ) and systemizing quotient (SQ) scores were obtained from the results of questionnaires. Each EQ and SQ questionnaires has 60 questions. Only 40 questions are relevant calculations. Twenty filler questions are randomly interspersed throughout the questionnaires. The numerical range for both scores is from 0 to 80. Benchmark-scoring for the SQ and EQ measures for samples of general population (Baron-Cohen, 2003) indicate that women averaged around 24 on the SQ scale and males averaged around 30. For the EQ measure, women typically scored around 47, while men averaged around 42. For SQ scores, the low range is 0-19; the average range is 20-39; the above-average range is 40-50; and the very high range is For EQ scores, the low range is 0-32; the average range is 33-52; the above-average range is 53-63; and the very high range is Table 3 extends the analysis by estimating the probability of being classified as female based on the two brain-type measures. In this probit estimation, both of the brain measures are significant at the percent level or better. A test hypothesizing that the coefficients of the brain-type measures were jointly zero rejected the null hypothesis at the level (χ 2 =27.09). Higher SQ scores decreased the probability of being classified as female, while increases in EQ enhanced the probability. While the results in this section substantiate the predictions of Baron-Cohen (2003), the issue at hand is whether they predict outcomes in the principles of economics course. Table 3: Probit Estimations for Being Classified as Female Based on the Two Brain-Type Measures Principles of Economics Students Upper-Division Public Finance Students Covariates Estimate t-statistic P-value Estimate t-statistic P-value SQ *** EQ *** ** Intercept ** N Log Likelihood Pseudo R Notes:* indicates significance at the 10% level; ** indicates significance at the 5% level;*** indicates significance at the 1% level. SQ is the systemizing coefficient; EQ is the empathizing coefficient. N indicates the number of observations. Success in the Principles of Economics The second phase of the analysis estimated the probability of making a particular grade in the principles of microeconomics based on a list of covariates and categorical variables. Among the covariates are the students cumulative grade point averages (GPA), hours spent in outside employment per week (Work), and brain-type measures (SQ and EQ). GPA is an indicant of the students abilities and aptitudes. Students with higher GPAs are expected to perform better in the principles of economics than students with lower GPAs (Ballard and Johnson 2005; Bollinger et al. 2006; Jensen and Owens 2001; Yang and Raehsler 2005). Work is a surrogate for hours unavailable for study. The anticipated sign of work is negative (Ballard and Johnson 2005; Bollinger, et al. 2006; Stinebrickner and Stinebrickner 2003): the more hours worked, the less available study time. SQ and EQ are the brain-type measures; they were discussed in the previous section.

6 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer The available fixed-effect factors include gender, class standing, instructor, and major. Past studies have generally found males perform at least as well or better in the principles of economics than females. As noted above, our contention is that it is not gender per se but brain-type that is being observed in these studies. The gender variable is included in order to compare our brain-type estimations to the more traditional models. Some studies (e.g., Anderson, et al. 1994; Ballard and Johnson 2005; Elzinga and Melaugh 2009) have found a maturity premium among students in the principles of economics. Class standing (i.e., freshman, sophomore, junior, senior) is included to account for this effect. Course instructors use different lecture styles and have different classroom idiosyncrasies which could impact performance (Dee 2007). An instructor variable is use to control for these effects. Performance in economics courses has also been linked to academic majors. Some studies find that students majoring in economics, finance, and accounting perform better in economics courses than students majoring in other business and nonbusiness subjects (Didia and Hasnat 1998; Spector and Mazzeo 1980). Other studies find little or no effect of academic major on performance (Ballard and Johnson 2005; Yang and Raehsler 2005) Dummy variables for eight business school majors were included among the original regressors. The omitted major was other majors, a heterogeneous category of nonbusiness subjects. The grading scale is the standard A through F with numerical scores ranging from 90 percent and above for an A to below 60 percent for an F. The ordinal rankings of the dependent variables run from y = 4 for an A to y = 0 for an F. Both instructors in the course used the same textbook. Examinations consisted of multiple-choice questions selected at random from the test bank accompanying the textbook. Each instructor administered four examinations during the semester. Each exam was one hour in duration. Estimates for two models are shown in Table 4. Model 1, referred to as the Gender Model, mimics past studies by including a raw gender variable as a categorical regressor. The model estimates the probability of receiving one of the five letter grades as a function of a student s GPA, work habits, gender, course instructor, and class standing. 17 This model expresses the unconditional relationship between gender and the probability of making a particular grade. The model is unconditional in the sense of estimating the effect of gender on the probability of making a grade when brain-types are not considered. GPA and Instructor_1 explain a significant portion of the probability of obtaining a given grade. Their positive signs imply that increases in GPA and attending Instructor_1 s classes increased the probability of making a higher grade. Senior class standing had a positive influence on grades and was significant at approximately the 12 percent level. In Model 1 hours worked and gender were insignificant in determining the probability of receiving a given letter grade. Model 2 substitutes the brain-type measures for the gender variable that was included in Model 1. The underlying hypothesis in Model 2 is that what is important in determining the distribution of grades is brain-type and not gender per se. Once again GPA, as an indicator of aptitude, and instructor_1 are important in explaining student success in the principles of economics. In addition, the coefficient for SQ is positive and significant at the 8% level. With other factors constant, including our measure of cognitive ability (GPA), increases in SQ increase the probability of making a higher grade in the principles course. Figure 1 shows the predicted probability of making each letter grade for increases in the SQ scores with other determinants held at their mean values. For example, as the SQ score is increased from 10 to 40, the predicted probability of making a B increases from 24.6% to 32.3%. Likewise the predicted probability of making an F falls from 15% to 8.7%. 17 The categorical variables representing declared major were insignificant in all of the estimations, and their effect sizes were negligible. Major was an insignificant factor determining grades in the ordered probit estimates of Yang and Raehsler (2005) as well. The models in Table 4 were estimated after eliminating major from the regressors.

7 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Table 4: Estimated Beta Coefficients for the Ordered Probit Models Principles of Economics Students Upper-Division Public Finance Students Model 1 Model 2 Model 1 Model 2 Gender Model Brain-Type Model Gender Model Brain-Type Model Regressors Coef. P-value Coef. P-value Coef. P-value Coef. P-value GPA *** *** *** *** Work Instructor_ *** *** Sophomore Junior Senior Gender SQ * EQ Constant *** *** *** *** u *** *** ** ** u *** *** *** *** u *** *** *** *** N Log Likelihood Pseudo R Notes: * indicates significance at the 10% level; ** indicates significance at the 5% level;*** indicates significance at the 1% level. The ordinal rankings of the dependent variable run from 4 for an A to 0 for an F. Instructor_1 is defined as one if the class was taught by instructor 1, zero otherwise; Sophomore is defined as one if a student is a sophomore, zero otherwise; Junior is defined as one if a student is a junior, zero otherwise; Senior is defined as one if a student is a senior, zero otherwise; Gender is defined as one for a female, zero otherwise; The covariates GPA, Work, SQ, and EQ are defined in the body of this section. N indicates the number of observations. u 1, u 2, and u 3 are the cut points generated in the probit model. Table 5 lists the marginal probabilities for both models. The marginal probabilities indicate the change in the probability of making a given grade based on a unit change in a regressor. For example, the marginal probabilities for SQ provide linear estimates of the slopes of the functions in Figure 1. A one-point increase in a student s SQ would increase the probability of making a B by 0.257%; thus, a 30-point increase from 10 to 40, as illustrated above, would increase the probability by 7.71% or from 24.6% to 32.3%. What this result implies is that Type S students have a higher probability of success in the principles of economics than Type E students. Since males are, on average, in the upper-tail of the SQ distribution, we would expect their performance to be somewhat better, on average, than the performance of females. Furthermore, differences in the sampling distribution of brain-types may account for the ambiguous results of past studies of gender effects in the principles of economics. 18 Table 5: Marginal Probabilities for the Significant Covariates and Categorical Variable in Models 1 and 2 Model 1: Gender Model Model 2: Brain-Type Model Grade GPA Instructor_1 Senior GPA Instructor_1 Senior SQ A B C D F Notes: The marginal probabilities for the discrete changes in the dummy variables are from 0 to One telling point is that there are almost no scientific studies showing a female advantage in the principles of economics.

8 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Increasing the GPA by one point in Model 2 raises the expectation of an A by 12.6% and B by 20.8%. The probability of making a D or F declines by 17% and 15.4%, respectively. Attending instructor 1 s class increased a student s probability of an A by 14.3% and a B by 23.7% relative to instructor 2. Some Results for Upper-Division Economics Students According to our hypothesis, upper-division (junior/senior) economics courses should be populated by similar brain-types. Students with higher SQ scores self-select into the upper-level economics courses. For example, in Table 2, the confidence interval for the female SQ scores in the principles course was , whereas it was for the junior/senior course considered in this section. Furthermore, for the overall samples, the standard deviation for SQ scores in the principles group was 12.84, while the standard deviation for the upper-division group was Ninety-seven percent of the upper-division students were in the highest three SQ ranges listed in Table 2 in contrast to 84% of the principles students. 19 Tables 2, 3, and 4 extend the analysis of the previous sections to a small sample of upper-division public finance students. Public finance is an elective course taken almost exclusively by economics majors. The sample of 35 students 20 represents two sections of public finance taught in the spring semesters of 2005 and 2006 by the same instructor. Sixty-six percent of the students were classified as seniors. The remaining students had junior standing. The grading scale was the same in both sections. Course grades were based on three one-hour exams and four problem sets. Table 2 includes a comparison of GPA, SQ, and EQ for female and male students in these upperdivision sections. The only significant difference between the means of these variables occurred for the empathy measure EQ. Upper-division female students had somewhat higher 21 EQ scores than their male counterparts. The Cohen-d was in the medium range of the effect size scale. For this group males did not have higher SQ scores than females. This finding contrasts sharply with the results for the principles students and for the general population reported by Baron-Cohen (2003). Also, in contrast to the results for the principles of economics students, there is no difference between the mean GPA s of male and female upper-division students. Table 3 shows estimates of the probability of being classified as female based on the two brain-type measures for the sample of upper-division students. Only one of the coefficients was significant at the 0.10 level or better. Higher EQ scores increased the probability of a student being classified as female. However, a test hypothesizing that the coefficients of the brain-type measures were jointly zero could not be rejected at the 0.10 level or better (χ 2 =0.3364). The model predicted 27 out of 28 males correctly but classified 6 out 7 females incorrectly. Essentially, the brain-types in the upper-division courses were indistinguishable. Specifically, the upper-division economics courses attracted women from the upper tail of the female SQ distribution. Table 4 shows the results of estimating the ordered probit models for the upper-division classes. Model 3 estimates the probability of receiving a given grade as a function of GPA, work, and gender. 22 GPA is the only significant regressor in Model 3. A higher GPA increased a student s probability of being in the upper ranges of the grade distribution. Gender did not contribute to the probability of receiving a particular grade in the course. Only the proxy for ability and aptitude (GPA) is significant in Model 4. Neither brain-type measure was significant. This result is expected since upper-division economics courses tend to attract students of similar brain-types whether male or female. Conclusions The conclusion of this paper is that success in the principles of economics is related to brain-type, not genders per se. Type S individuals have a higher probability of being in the upper-ranges of the grade distribution than Type E individuals, no matter what their gender. While there is some overlap in the probability distributions of SQ scores, high SQ scores are a male trait. Studies that show an advantage for 19 A Mann-Whitney test (Z = 0.145; P-value = 0.888) on the upper-division sample revealed that the SQ scores for females and males were from the same underlying distribution. In contrast, the same test on the principles sample produced an absolute Z score of (P-value = ), indicating the SQ scores for males and females were not from the same underlying distribution. 20 There are 28 males and 7 females. 21 With t-statistic = 1.93 and P-value = The categorical instructor variable is redundant in this case.

9 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer males over females in the principles of economics are really observing an advantage of Type S individuals over Type E individuals. 23 The tendency for fewer females to major in economics or to take fewer upperdivision economics courses may be attributable to the lower percentage of Type S females. The upperdivision economics courses in our sample were dominated by students with high SQ scores. The results of this study raise some challenging issues for instructors in the principles of economics. For example, how can Type E students be motivated to apply the tools of economics, some of which are quite abstract, to important policy issues? Further, can ways be found to overcome the negative pre- and postdisposition of Type E students toward the principles of economics? Finally, how can the principles of economics provide a gateway for these students to enter upper-division courses? Finding answers to these questions could increase the probability of success among Type E students and encourage them to major in economics. References Anderson, B., H. Benjamin, and M. Fuss The Determinants of Success in University Introductory Economics Courses. Journal of Economic Education 25: Ballard, C.L. and M. Johnson Gender, Expectations, and Grades in Introductory Microeconomics at a U.S. university. Feminist Economics 11(1): Bansak, C. and M. Starr Gender Differences in Predisposition towards Economics. Available at SSRN: Baron-Cohen, S The Essential Difference: The Truth about the Male and Female Brain. New York: Basic Books. Baron-Cohen, S., J. Richler, D. Bisarya, N. Gurunathan, and S. Wheelwright The Systemizing Quotient (SQ): An Investigation of Adults with Asperger Syndrome or High-Functioning Autism, and Normal Sex Differences. Philosophical Transactions of the Royal Society, Series B, special issue on Autism: Mind and Brain 358(1430): Baron-Cohen, S. and S. Wheelwright The Empathy Quotient: An Investigation of Adults with Asperger Syndrome or High-Functioning Autism, and Normal Sex Differences. Journal of Autism and Developmental Disorders 34(2): Bollinger, C., G. Hoyt, and K. McGoldrick Chicks Don t Dig It: Gender, Attitude and Performance in Principles of Economics Classes. Available at SSRN: Borg, M. and H. Stranahan. 2002a. Personality Type and Student Performance in Upper-Level Economics Courses: The Importance of Race and Gender. Journal of Economic Education 33(1): Borg, M. and H. Stranahan. 2002b. The Effect of Gender and Race on Student Performance in Principles of Economics: The Importance of Personality Type. Applied Economics 34: Caplan, B The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Princeton, N.J. Princeton University Press. Coates, D. and B. Humphrey Evaluation of Computer-Assisted Instruction in Principles of Economics. Educational Technology & Society 4(2): The Type S and Type E classifications might have some implications for the findings that students with introverted personalities are more successful in both lower- and upper-division economics courses. For example, Ziegert (2000) found that introverted students whether male or female performed better in economics courses than their extroverted counterparts. Type S individuals tend to be tolerant of solitude, individualistic, prone toward objects rather than people, and less interactive. Introverts have these characteristics to varying degrees.

10 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Cohen, J Statistical Power Analysis for Behavioral Sciences. Hillsdale, NJ: Erlbaum. Cohn, E., S. Cohn, R. Hult, D. Balch, and J. Bradley The Effects of Mathematics Background on Student Learning in Principles of Economics. Journal of Education for Business 74(1): Dee, T Teachers and the Gender Gaps in Student Achievement, Journal of Human Resources 42(3): Didia, D. and B. Hasnat The Determinants of Performance in University Introductory Finance Course. Finance Practice and Education 8: Durden, G. and E. Ellis The Effect of Attendance on Student Learning in Principles of Economics. American Economic Review 85(2): Elzinga, K. and D. Melaugh ,000 Principles Students: Some Lessons Learned. Southern Economic Journal 76(1): Ferber, M., B. Birnbaum, and C. Green Gender Differences in Economic Knowledge: A Reevaluation of the Evidence. Journal of Economic Education 14: Gorhmann, S. and L. Spector Test Scrambling and Student Performance. Journal of Economic Education 20: Hill, C. and T. Stegner Which Students Benefit from Graphs in a Principles of Economics Classes? American Economists 47(2): Hines, M Brain Gender. New York: Oxford University Press. Hopkins, S Assessment Modes in First Year Macroeconomics: Gender Differences in Performance. Economic Papers: Economic Society of Australia. Jensen, E. and A. Owen Pedagogy, Student Gender, and Interest in Economics. Journal of Economic Education 32(4): Kanazawa, S Is Discrimination Necessary to Explain the Sex Gap in Earnings? Journal of Economic Psychology 26: Kimura, D Are Men s and Women s Brains Really Different? Canadian Psychology 28: Kimura, D Sex and Cognition. Cambridge, MA: The MIT Press. Siegfried, J. and R. Fels Research on Teaching College Economics: A survey. Journal of Economic Literature 17(3): Spector, L. and M. Mazzeo Probit Analysis and Economic Education. Journal of Economic Education 11(1): Stinebricker, R. and T. Stinebrickner Working During School and Academic Performance. Journal of Labor Economics 21(2): Yang, C. and R. Raehsler An Economic Analysis on Intermediate Microeconomics: An Ordered Probit Analysis. Journal for Economic Educators 5(3): Ziegert, A The Role of Personality Temperament and Student Learning in Principles of Economics: Further Evidence. Journal of Economic Education 31(4):

11 JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 9 Number 1 Summer Figure 1: Estimated Probability of Making a Particular Grade Based on Different SQ Scores Probability B C A D SQ Score Notes: The lines denoted A through F display the predicted probabilities of making a specific letter grade when the SQ scores are increased with the other determinants held constant at their mean values. F

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