Teaching and Learning Forum 98 [ Contents ]

Inherent gender differences as an explanation of the effect of instructor gender on accounting students' performance

Janne Chung and Kenis Tang
School of Accounting
Edith Cowan University
Prior studies found that female students outperform male students when the instructor's gender is female, and male students outperform female students when the instructor's gender is male (Mutchler et al., 1987; Lipe, 1989). This study explains the findings of Mutcher et al. (1987) and Lipe (1989) and makes suggestions to counteract the effect of instructor gender on student performance. It proposes that the observations of Mutchler et al. and Lipe may be attributed to inherent differences, specifically differences in risk-taking propensity between the genders. This study speculates that the observed effect of instructor gender on students' performance is due to a positive (negative) bias when student gender and instructor gender are matched (mismatched). To demonstrate this, we made use of a case study involving the assessment of risk and hypothesised that there will be a match between the responses of female students and female instructors and between the responses of male students and male instructors. However, no match between the responses of male and female students and between the responses of male and female instructors is expected. An experiment with 117 students and 23 instructors was carried out. The results support our hypotheses.


Introduction

Mutchler et al. (1987) found that female students perform better than male students when the instructor is female. Male students' performance is similarly enhanced by male instructors. Mutchler et al.'s study examined data from three major US state universities and comprised five male and five female instructors. Lipe (1989) proposed that the effect of instructor gender on students' performance may be due to differences in female and male instructors' grading policies[1]. She speculated that if grading policy is controlled, then instructor gender should not have an effect on students' performance. An experiment where grading policy was strictly coordinated was carried out, and like Mutchler et al., Lipe found that instructor gender has an effect on students' performance. This rules out grading policy as an explanation of Mutchler et al.'s observation. Lipe argued that other factors may have contributed to these results, and suggested that "matching students and instructors according to gender may have helped to match them on learning/teaching styles" (150). This suggests that there are inherent differences between males and females. While some of the differences are physiological, there are also psychological and sociological differences that result from the manner in which male and female children are socialised into societal roles (see Carlson [1971; 1972], and Gilligan [1982][2]). This implies that matching student and instructor gender will result in a positive bias and mismatching them will result in a negative bias. We propose that inherent differences between males and females may have accounted for Lipe's and Mutchler et al. findings, and that these observations indicate a bias which results when student gender is matched/mismatched with instructor gender.

Inherent differences between the genders comprise many dimensions. One dimension examined in the psychology and marketing studies is risk-taking propensity (Darley and Smith, 1995; Lenney, 1983; Meyers-Levy, 1986). These studies found that males tend to take more risk, while females tend to be risk-averse. The practice of accounting frequently involve risky judgments (such as capitalisation of expenses and treatment of leases), and these are included in the curricula of many accounting courses. In class assignments or assessments, students may be given a particular treatment of leases (e.g., classification of financing or operating leases), and asked to decide on the validity of the treatment. Similarly in auditing, risk evaluation forms a substantial part of the course. These include topics such as audit risk, control risk, inherent risk and going-concern risk. When a female (male) instructor constructs a going-concern class assignment or an examination question, she (he) assembles together various information cues about a hypothetical (and sometimes, actual) company and determines what the acceptable answer is. Typical requirements of such questions are for students to identify the various going-concern problem indicators and mitigating factors and make an overall going-concern judgment. Due to inherent differences between the genders, the female (male) instructor's predetermined answer would match the female (male) students' answers, whereas the male (female) students' answers would not. This bias results in female (male) students obtaining higher scores than male (female) students. Thus, inherent differences between the genders may result in a positive bias when student gender and instructor gender are matched, and a negative bias when student gender and instructor gender are mismatched. This may be an explanation of Lipe's and Mutchler et al.'s observations. The purpose of this study is to examine whether inherent differences between the genders is an alternative explanation for these findings.

Background and hypotheses development

Even though there is no accounting education study which compares gender-based risky judgments, extant psychology and marketing studies have examined this subject (see Darley and Smith [1995], and Meyers-Levy [1986]). These studies found that males tend to take more risk than females. On the one hand, males are more likely to donate blood (a risky activity) (Nonis et al., 1996; Andaleeb and Basu, 1995), purchase risky products (Darley and Smith, 1995; Meyers-Levy, 1986), and make more risky business judgments than females would (Sexton and Bowman-Upton, 1990). On the other hand, compared to females, males are less likely to purchase health and life insurance (Gandolfi and Miners, 1996; Garber and Phelps, 1997), and engage the services of a financial planner (Stinerock et al., 1991) (these being risk-reducing services). Male advocates also take more risks than female advocates (DiBerardinis et al., 1984).

In attempting to explain the observed gender difference(s), prior literature examined the information processing styles of males and females. These studies consistently found that males and females process information differently (Meyers-Levy, 1986; Gilligan, 1982). Males encode fewer details while females pay more attention to details (Meyers-Levy, 1986; Gilligan, 1982). Because females process more details than males, therefore males must use heuristics in their processing of information (Meyers-Levy, 1986; Meyers-Levy and Maheswaran, 1991). The tendency of males to use heuristics in information processing could be explained in two ways. First, the amount of information that is stored in long term memory is dependent upon the amount of information processed. As males encode fewer details, they would recall less information for decision-making. Hence they would have to resort to using heuristics to compensate for the lack of stored information (Reder, 1987). Second, the presence of contrasting information in the data set causes cognitive strain (Kahneman, 1973). When faced with contrasting information, normative decision theories suggest that people should pay greater attention to details in order to reconcile the contrasting information. As females pay greater attention to details than males do (Meyers-Levy, 1986), this suggests that males and females do not react to contrasting information in the same way. Females deal with contrasting information by increasing processing effort whereas males make greater use of heuristics.

A heuristic often cited in the marketing literature to explain the differences between males' and females' risk-taking propensity is the "cue sensitivity thresholds" (see Meyers-Levy [1986]). Cue sensitivity threshold theory states that a person's sensitivity to the nature of the cues contained in a data set determines the judgment outcome. Meyers-Levy (1986) suggests that males' more risky judgment is a result of their lack of sensitivity to risk cues. Other studies also found that males are less sensitive to risk cues compared to females (e.g., Darley and Smith 1995; Meyers-Levy, 1985). Meyers-Levy (1985) gave both males and females descriptions of a new product which contained a risky attribute. The salience of the risky attribute was given at two levels - weak and moderate. Female participants were able to detect the risky attribute when its description was moderately salient. However, male participants were unable to detect it at both levels of salience. The males in both levels of salience and the females in the weak salience condition appeared to rely on other cues. Darley and Smith (1995) found similar gender differences in an experiment which manipulated two consumer products (risky and non-risky) at two levels of risk (low and moderate) and two types of product claims (objective and subjective). The results showed that when risk level increased, females changed their processing strategy from evaluating both objective and subjective claims to evaluating mainly objective claims. Males, however, did not change their processing strategy when the risk level was increased. Like Meyers-Levy, Darley and Smith concluded that males are less sensitive to risk cues than females are.

Goldhaber and deTurck (1988) found that males are more likely to ignore "No Diving" signs, and dive into the swallow end of a pool. Like Meyers-Levy (1986) and Darley and Smith (1995), deTurck and Goldhaber (1989) and Goldhaber and deTurck (1988) attributed this finding to differences in their information processing styles. Females, as detailed processors are more persuaded by the information contained in the data set than males are. Males tend to process only a portion of the information before forming their judgments. When the data set contains risk cues, the males would register the presence of these cues but would not read their details (Goldhaber and deTurck, 1988). Females, however, would read the cues in detail before making their final judgment (Goldhaber and deTurck, 1988). Because females process risk cues in greater detail, they are more aware of the dangers implied by these cues, and this leads to more risk-averse judgments (Goldhaber and deTurck, 1988). Consequently, the difference between the genders' risky judgments is a result of the difference in their information processing styles (Darley and Smith, 1995; Goldhaber and deTurck, 1989; deTurck and Goldhaber, 1988; Meyers-Levy, 1986). Similarly, in a class assignment involving a risky task, male students' encoding of the risk cues would be at a lower level compared to female students' encoding of these cues. Because female students process risk cues in greater details, they would make more risk-averse judgments compared to male students.

In this study, a going-concern task was used. In this task, the going-concern problem indicators are risk cues whereas the mitigating factors are non-risk cues. The cues were preselected and premeasured, and participants were required to evaluate and combine the cues into a global going-concern judgment. Female students are expected to make more risk-averse judgments compared to male students. That is, female students would judge the company as more likely to fail and the male students would judge the company as less likely to fail. Similarly, male instructors' judgments will be more risky compared to female instructors' judgments. The literature does not suggest that such attitudes towards risk-taking diminish with age and experience. In fact, studies seem to suggest that it does not (Sexton and Bowman-Upton, 1990). This suggests that the risk-taking propensity of males and females is not affected by age (general life experience) and domain-specific experience. It is expected that there will be no significant difference between the judgments of male instructors and male students, as well as between female instructors and female students. However, significant differences are expected when student and instructor gender are mismatched. Therefore, the following hypotheses state:

H1Male instructors' judgments will not be different from male students' judgments.
H2Female instructors' judgments will not be different from female students' judgments.
H3Male instructors' judgments will be more risky compared to female students' judgments.
H4Female instructors' judgments will be less risky compared to male students' judgments.

Experiment

Case Materials
The case materials comprised a typical case study that would be used in class discussion and class assignment. It contained a set of financial statements together with information about the company. The information cues included both going-concern problem indicators as well as mitigating factors. To derive these information cues, a modified version of Kida's (1984) going-concern case materials was used. The four most predictive going-concern problem indicators were selected. As for the mitigating factors, the three most predictive were selected. The fourth was not chosen because it contradicted the accompanying financial statements[3]. Instead it was substituted with the next most predictive cue. Some words were changed to reflect an Australian context as well as the financial statements that accompanied the information cues. The financial statements of a real but disguised company together with the eight information cues made up the case materials.

Procedures
The experiment for the students was conducted during class time. The participants were given some introductory information which explained the purpose of the study. This was to examine going-concern judgments and an assurance of confidentiality was also provided in the introductory information. The students read the financial statements and made an initial going-concern judgment on a nine-point Likert-type scale anchored by "will fail within the next 12 months" (1) and "will continue in operation for the next 12 months" (9). In the next task, the participants were provided with the eight information cues. These were provided in random order. After evaluating these cues, the students made a final going-concern judgment on a similar scale. The eight information cues are presented in Appendix A. The participants then provided some demographic information. The procedures for the instructors were similar to those for the students except the instructors completed the tasks at their own pace.

Participants
The student participants were 117 final year accounting majors enrolled in an auditing course, and comprised 61 males and 56 females[4]. At the time of the experiment, they had attended two three-hour classes on risk evaluation including going-concern risk. They had also completed a comprehensive class assignment on going-concern risk evaluation which was compulsory. The average age of the students was 25.3 years (sd 7.4). Eight of the students had an average of 10.5 (sd 11) months of audit experience while the rest did not have any audit experience. Each student was paid A$10 for their efforts. The 23 instructors (12 males and 11 females) comprised both full-time (15) and part-time (8) accounting instructors. The part-time instructors were in full-time employment as accountants/auditors, and had at least one year teaching experience. The full-time instructors had teaching experience ranging from four to twenty years. The instructors were not paid.

Results

H1 predicts no significant difference between the judgments of male students and male instructors. ANCOVA analysis shows no significant difference between their going-concern judgments (F = 1.46, p = .23) (panel A, Table 1). The male instructors' judgments (6.58) were riskier than the male students' judgments (6.13) but this difference is not significant. There is support for H1. This finding also supports the prior finding of Sexton and Bowman-Upton (1990) that males' risk-taking propensity does not diminish with age and experience. H2 similarly predicts no significant difference between the judgments of female students and female instructors. The ANCOVA analysis supports this hypothesis (F = .17, p = .68) (panel B, Table 1). The female instructors' judgments (5.55) were more risk-averse compared to the female students' judgments (5.84), but these were not significantly different. Like males' risk-taking propensity, females' risk-averse nature does not diminish with age and experience.

Table 1: ANCOVA Analyses

Panel A - Male Students and Male Instructors
Source of VariationMSdfFp

Covariate: Initial Judgment164.81169.59.00
Main effect - Student/Instructor3.4611.46.23
Residual2.3772

Panel B - Female Students and Female Instructors
Source of VariationMSdfFp

Covariate - Initial judgment63.03129.39.00
Main effect - Student/Instructor.361.17.68
Residual2.1566

Panel C - Male Instructors and Female Students
Source of VariationMSdfFp

Covariate - Initial judgment115.55154.06.00
Main effect - Student/Instructor16.0417.50<.01
Residual2.1467

Panel D - Female Instructors and Male Students
Source of VariationMSdfFp

Covariate - Initial judgment112.00147.00.00
Main effect - Student/Instructor6.6113.13.08
Residual2.3868

H3 predicts a significant difference between the judgments of female students and male instructors. ANCOVA analysis between the responses of male instructors and female students was carried out. Significant differences between their mean scores were noted (F = 7.50, p < .01) (panel C, Table 1). The male instructors' judgments were more risky compared to the female students' judgments. In H4, female instructors' judgments are expected to be less risky compared to the judgments of male students. Significant differences between the female instructors' and male students' judgments were observed (F = 3.13, p = .08) (panel D, Table 1). The male students' judgments were more risky compared to the female instructors' judgments. These results lend further support to our proposition that inherent gender differences may be an explanation for the findings of Mutchler et al. (1987) and Lipe (1989). Significant differences were noted when student gender and instructor gender were mismatched. Translated into a classroom situation, students of the same gender as the instructor would have answers similar to the instructor's answers. Consequently, they would be awarded more credit compared to students whose answers are dissimilar from the instructor's. These latter students are usually students of the opposite gender.

Table 2: Means (SD) for Male/Female Students and Male/Female Instructors
Scale = "will fail within the next 12 months" (1) and "will continue in operation for the next 12 months" (9).

Students InstructorsTotal

Males 6.13
(2.10)
n = 61
6.58
(2.50)
n = 12
6.20
(2.16)
n = 73
Females 5.84
(1.84)
n = 56
5.55
(1.21)
n = 11
5.79
(1.75)
n = 67
Total 5.99
(1.92)
n= 117
6.09
(2.02)
n = 23
6.00
(1.97)
n = 140

Conclusion

In this paper, we are motivated by prior studies which found that instructor gender has an effect on accounting students' performance (Mutchler et al., 1987; Lipe, 1989). Prior literature indicates that males and females react differently to risk. In order to explain Mutchler et al.'s and Lipe's findings, we examined male and female students' risky judgments in an auditing context, and compared these with male and female instructors' risky judgments. No significant difference between male students' and male instructors' judgments were noted. Similarly, there was no significant difference between the judgments of female students and female instructors. However, significant differences were observed when student gender and instructor gender were mismatched. When instructors construct class assignments and assessments, they often judgmentally determine the desirable (correct) answer. In accounting and auditing where the curricula often include risky judgments, the male students' responses to these class assignments and assessments would match the male instructors' predetermined answer whereas the female students' responses would match the female instructors' answers. When the student and instructor gender are mismatched, the students' responses would not match the instructors' predetermined answers. Consequently, these students would perform less well compared to the students whose gender matches the instructor's gender. This supports our proposition that inherent differences between the genders is an explanation for the findings of Mutchler et al. (1987) and Lipe (1989).

This problem could be redressed in two ways. The first is to be aware of the problem as awareness is the first step towards solving it. Second, when the gender of the students enrolled in a class is mixed, it is beneficial to obtain the input of instructors of both genders when designing and writing class assignments and assessments. This ensures that the effect of the instructor gender on student performance is neutralised.

References

Andaleeb, S. S., and Basu, A. K. (1995). Explaining blood donation: The trust factor. Journal of Health Care Marketing, 15(1), 42-48.

Berg, H., and Ferber, M. (1983). Men and women graduate students: Who succeeds and why? Journal of Higher Education, August/September: 629-648.

Bonner, S. E. (1991). Is experience necessary in cue measurement: The case of auditing tasks. Contemporary Accounting Research, 8(1), 253-269.

Bonner, S. E. (1990). Experience effects in auditing: The role in task-specific knowledge. The Accounting Review, 65(1), 72-92.

Carlson, R. (1972). Understanding women: Implications for personality theory and research. Journal of Social Issues, 28, 17-32.

Carlson, R. (1971). Sex differences in ego functioning: Exploratory studies of agency and communion. Journal of Consulting and Clinical Psychology, 37, 267-277.

Chalos, P. and Pickard, P. (1985). Information choice and cue use: An experiment in group information processing, Journal of Applied Psychology, 634-641.

Darley, W. K., and Smith R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 14(1), 41-56.

deTurck, J. A., and Goldhaber, G. M. (1989). Effectiveness of signal words in product warnings: Effects of familiarity and gender. Journal of Products Liability, 12(1/2), 103-113.

DiBerardinis, J., Ramage, K. and Levitt, S. (1984). Risky shift and gender of the advocate: Information theory versus normative theory. Group and Organization Studies, 9(2), 189-200.

Garber, A. J., and Phelps, C. E. (1997). Economic foundation of cost-effectiveness analysis. Journal of Health Economics, 16, 1-31.

Gilligan, C. (1982). In a Different Voice: Psychological Theory and Women's Development. Cambridge, MA: Harvard University Press.

Goldhaber, G. M., and deTurck, M. A. (1988). Effectiveness of warning signs: Gender and familiarity effects. Journal of Products Liability, 11(3), 271-284.

Kahneman, D. (1973). Attention and Effort. Englewood Cliffs, NJ: Prentice-Hall: 136-155.

Lenny, E., Gold, J. and Browning, C. (1983). Sex differences in self-confidence: The influence of comparison to others' ability level. Sex Roles, 9(9), 925-942.

Lipe, M. G. (1989). Further evidence on the performance of female versus male accounting students. Issues in Accounting Education, 4(1), 144-152.

Lipe, M. G., Slovic, P. and Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge, UK: Cambridge University Press.

Meyers-Levy, J. and Maheswaran, D. (1991). Exploring differences in males' and females' processing strategy. Journal of Consumer Research, 18(June), 63-70.

Meyers-Levy, J. (1986). Gender differences in information processing: A selectivity interpretation. In P. Cafferata and A. M. Tybout (eds), Cognitive and Affective Responses to Advertising. Lexington, MA: Lexington.

Meyers-Levy, J. (1985). Gender differences in information processing: A selectivity interpretation. Ph. D. dissertation, Northwestern University.

Mutchler, J. F., Turner, J. H., and Williams, D. D. (1987). The performance of female versus male accounting students. Issues in Accounting education, (Spring), 103-111.

Nonis, S. A., Ford, C. W., Logan, L. and Hudson, G. (1996). College student's blood donation behavior: Relationship to demographic, perceived risk, and incentives. Health Marketing Quarterly, 13(4), 33-46.

Sexton, D. L. and Bowman-Upton, N. (1990). Female and male entrepreneurs: Psychological characteristics and their role in gender-related discrimination. Journal of Business Venturing, 5, 29-36.

Stinerock, R., Stern, B. B., and Solomon, M. R. (1991). Sex and money: Gender differences in the use of surrogate consumers for financial decision-making. Journal of Professional Services Marketing, 7(2), 167-182.

Appendix A: Information Items

Risk Cues:1.The competence of the company's management has been questioned by outside observers.
2.It appears that if needed it will be difficult to obtain additional debt capital.
3.Discussions with management indicate that a material liability from litigation is likely this year.
4.Management believes there is a good chance of losing a major customer.

Non-risk Cues:5.The company's major service is generally considered to be of good quality.
6.Management states that it is possible that a contract with a major hospital may be obtained in the near future.
7.This year the company reported a positive cash flow from operations.
8.In general, suppliers of the company indicate that usual trade credit to the company will be available.

Footnotes

  1. Prior education research found that instructors are more sympathetic to students of their own gender (see Berg & Ferber 1983).
  2. Carlson (1971; 1972) found that males are guided by agentic goals that stress self-achievement while females are guided by communal goals that emphasize the welfare of others.
  3. These financial statements show profits instead of losses.
  4. The instructor of the auditing class (who was present in the room during the experiment) was male whereas the researcher who collected the data was female.
Please cite as: Chung, J. and Tang, K. (1998). Inherent gender differences as an explanation of the effect of instructor gender on accounting students' performance. In Black, B. and Stanley, N. (Eds), Teaching and Learning in Changing Times, 72-79. Proceedings of the 7th Annual Teaching Learning Forum, The University of Western Australia, February 1998. Perth: UWA. http://lsn.curtin.edu.au/tlf/tlf1998/chung.html


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