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Insights Regarding the Applicability of the Defining Issues Test to Advance Ethics Research with Accounting Students: A Meta-analytic Review

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Abstract

Numerous researchers have investigated accounting students’ levels of moral reasoning, ethical choice and judgment employing the Defining Issues Test (DIT) and using its P score as an indicator of moral reasoning. Not surprisingly, a number of DIT studies report conflicting results. Moreover, despite widespread use of the DIT, there is concern that it may not adequately measure all facets of ethical judgment (cf. Bailey et al., Behav Res Account 22(2):1–26, 2010). Thus, we endeavor to provide insight not only into the contradictory results but also about the applicability of the DIT for studying accounting students. To do so, we collect published and unpublished DIT studies employing accounting students as subjects and use meta-analysis to aggregate findings across these studies to quantify their results, examining commonly employed variables. We show significant relationships between P scores and some variables (length of professional experience, choice of major, political ideology, gender, GPA and education level) but not others (age). Further, our findings demonstrate that the DIT provides added insights when exploring questions of ethical choice, and ethics instruction, particularly when the instruction is embedded in an accounting course. Finally, we find that the level of DIT P scores reported in the studies relates to whether the study was published. We discuss the implications of our findings for future research.

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Notes

  1. Bernardi and Bean’s (2008) paper includes 13 published studies with subjects that are students enrolled in accounting courses. We include 47 unpublished and published studies using the DIT instrument to investigate various variables in relation to accounting majors. In contrast to Bernardi and Bean (2008), our analyses only include students clearly identified as accounting majors in studies using students enrolled in accounting principles courses. If separate scores for accounting majors are not provided, we do not include the study in our analyses. Thus, our samples differ from those included in Bernardi and Bean (2008). In addition to investigating college level and gender, we analyze studies comparing the DIT P scores of accounting majors to accounting professionals, accounting majors to other college students pursuing other majors, and studies investigating the relationship of accounting majors’ DIT P scores with variables such as ethics instruction, ethical choice, political ideology, grade point average and age.

  2. Proceedings were reviewed in this window to capture new studies that may not yet have made it through the journal review process. Our various search procedures, as described, indicated accounting student DIT studies included in proceedings prior to this period had made it to publication.

  3. As shown in Appendix, of the 86 studies, 17 are eliminated due to the inability to determine the number of accounting majors (e.g., the study included other business majors) or the study employed MBAs, 11 are removed as the study used a modified DIT or an instrument similar to a DIT, seven did not include the necessary data and four use the same subjects or data as a study already included in our meta-analysis.

  4. Among the conversions we used were the following (cf. Borenstein 2009; Card 2012):

    • Studies reporting t statistics: r = SQRT(t 2/(t 2 + df)).

    • Studies reporting F statistics: r = SQRT(F/(F + df)).

    • Studies reporting χ 2 statistics: r = SQRT(χ 2/N).

    • Studies reporting means for 2 groups: Step 1: d = (M 1 − M 2)/sdpooled, where M 1 and M 2 are the means for groups 1 and 2, respectively and sdpooled is the pooled sample standard deviation.

    • Step 2: r = d/SQRT(d 2 + a), Note a = (n 1 + n 2)2/(n 1 n 2), where n 1 and n 2 are the sample sizes for groups 1 and 2, respectively).

  5. There are two models for computing meta-analysis effect sizes (correlations): the fixed-effects model and the random effects model. Under the fixed-effects model, the assumption is there is homogeneity among individual studies in the meta-analysis and that the studies share one “true” effect size or, in other words, there is one “true” population correlation. The fixed-effects model is appropriate where the studies share the same design and similar subjects. The fixed effect model should be used when all factors that could influence the effect size are included in all studies in the analysis (Borenstein et al. 2009).

    Under the random-effects model, it is assumed that there is heterogeneity among individual studies and thus, the true effect may vary somewhat from study to study resulting in no one true rho (population correlation ρ). In some DIT studies included in our analyses, some subjects differ with respect to factors such as GPA and age, which could influence the effect size for different studies (Borenstein et al. 2009). In addition, some studies that measure the relationship between DIT P scores and a factor such as gender include other factors such as GPA or age and some do not include the factors GPA and age. Hence, we employ the random-effects meta-analysis model in all our analyses, which estimates the mean of a distribution of effects and allows for potential between-study differences (Erez et al. 1996; Borenstein et al. 2009). We report results based on the more conservative random effects model because of the variability in design and the subjects of the studies we analyze (Card 2012).

  6. Although a Q value that is statistically insignificant indicates that there is just one effect size in the population, it does not mean that a fixed-effects model should be used in an analysis of studies that may include different factors such as GPA or age in some studies, but not others as these different factors could influence the effect size (Borenstein et al. 2009). Further, random effects models allow generalization of conclusions to the population, whereas fixed effects models limit conclusions to the studies included in the analysis (Card 2012). The Q value is calculated in the same manner for both fixed-effects and random effects models and it measures the heterogeneity of effect sizes for the studies included in the analysis.

  7. Table 1 presents a total of 33 studies for RQ9 that incorporate data for at least one level of university education. Some of these studies report mean P scores for lower division students, some for juniors, some for seniors, and some for graduate students. Few of these studies report mean P scores for more than one level of university education. For example, we have only four studies that report mean P scores for both lower division and senior students. However, by conducting our analysis across studies, we are able to include seven studies that report lower division mean P scores and 24 studies that report senior level mean P scores, thus providing substantially more power than if we used only the four studies that report scores for both lower division and senior students.

  8. One study did not provide the mean DIT P score for accounting students and three studies did not provide the standard deviation. However, these four studies did provide the needed statistics for one or more of our other analyses.

  9. One of these studies (Ponemon 1993) provides scores for two samples, one with seniors and one with graduate students.

  10. In five samples, 154 students were identified as liberal and 255 as conservative. One additional sample (Klimek and Wenell 2011) provides the correlation between Liberal/Conservatism and DIT P scores, but not the breakdown of liberal and conservatives in their sample of 52 students. These students’ political ideology was evaluated on a 5 point scale ranging from 1 very liberal to 5 very conservative. A few studies, but not all, provide separate statistics for political moderates so moderates are excluded from our analysis.

  11. Bernardi et al. (2004) measure the political ideology and DIT P scores of business and other students enrolled in lower division accounting principles classes. However, separate scores for students majoring in accounting are not provided, so we do not include this study in our analysis.

  12. In some studies investigating ethical instruction pre and post-test P scores for females and males are provided. We include only the post-instruction scores in our analysis.

  13. The trend in our results (using both published and unpublished studies) of accounting majors at higher levels of college scoring higher on moral reasoning as measured by the DIT is consistent with results obtained by Bernardi and Bean (2008) using published studies of students enrolled in accounting courses (both accounting and other business majors).

  14. Rest (1990) reports that the P score of the 3 Story version of the DIT has a correlation of 0.93 with the P score of the 6 Story DIT. Rest et al. (1999, p. 651) also indicate that the DIT 6 Story and DIT2 are correlated with each other at a level similar to that shown in prior studies of test–retest correlations of the DIT 6 Story (Rest 1979, p. 239). Hence, we combine studies using all three versions of the DIT. However, Bernardi and Bean (2008) indicate that the scales for P scores differ between the 3 Story and 6 Story versions of the DIT with a maximum P score of 90 on the 3 Story and 95 on the 6 Story. They suggest similar scaling issues may arise with respect to the DIT2. To determine if our results might be influenced by DIT version we conduct two tests in which we converted DIT 3 Story P scores to the DIT 6 Story scale and excluded studies using the DIT2. We then repeated our analyses for pre and post ethics instruction and our comparison of lower division students with seniors and found our results did not significantly change from our results using unadjusted P scores in analyses combining all three versions of the DIT. Thus, consistent with the strong correlations among P scores in all DIT versions reported by Rest (1990) and Rest et al. (1999) our results are not significantly affected by the scaling issues noted by Bernardi and Bean (2008).

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Acknowledgments

The authors would like to express their appreciation for helpful comments from reviewers and discussants at the 2012 Western American Accounting Association Annual Meeting and the 2012 American Accounting Association Annual Meeting. The guidance provided by Professor Ioannis Kareklas has also been insightful.

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Authors and Affiliations

  1. Jake Jabs College of Business and Entrepreneurship, Montana State University, P.O. Box 173040, Bozeman, MT, 59717-3040, USA

    Anne L. Christensen

  2. Carson College of Business, Washington State University, 14204 NE Salmon Creek Avenue, Vancouver, WA, 98686-9600, USA

    Jane Cote & Claire K. Latham

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  1. Anne L. Christensen
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  2. Jane Cote
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Corresponding author

Correspondence to Jane Cote.

Appendix: Studies Excluded

Appendix: Studies Excluded

Unable to determine breakdown of undergraduate accounting majors (includes introductory accounting courses incorporating all business majors)

  • Abdolmohammadi, M., and J. Sultan. (2002). Ethical reasoning and the use of insider information in stock trading. Journal of Business Ethics, 37(2), 165–173.

  • Abdolmohammadi, M. J., D. L. Gabhart, and Reeves, M. (1997). Ethical cognition of business students individually and in groups. Journal of Business Ethics, 16(16), 1717–1725.

  • Bailey, C. D. (2011). Does the DIT measure ethical judgment ability or political position? Journal of Social Psychology, 151(3), 314–330.

  • Bay, D. D., and R. R. Greenberg (2001). The relationship of the DIT and behavior: A replication. Issues in Accounting Education, 16(3), 367–380.

  • Bernardi, R. A., D. F. Bean, and D. W. Massey. (2004). The influence of political ideology on DIT scores: Fact or artifact. Research on Professional Responsibility and Ethics in Accounting, 9, 21–48.

  • Bernardi, R. A., R. L. Metzger, R. Bruno, M. Hoogkamp, L. E. Reyes, and G. H. Barnaby. (2004). Examining the decision process of students’ cheating behavior: An empirical study. Journal of Business Ethics, 50(4), 397–414.

  • Chan, C. W., C. S.Troutman, and D. O’Bryan. (2000). An expanded model of taxpayer compliance: Empirical evidence from the United States and Hong Kong. Journal of International Accounting, Auditing & Taxation, 9(2), 83–103.

  • Falk, H., B. Lynn, M. Mestelman, and M. Shehata. (1999). Auditor independence, self-interested behavior and ethics: Some experimental evidence. Journal of Accounting and Public Policy 18(Winter), 395–429.

  • Fisher, D. G. (1997). Assessing taxpayer moral reasoning: The development of an objective measure. Research on Accounting Ethics, 3, 141–171.

  • Shawver, T., and J. Sennetti. (2009). Measuring ethical sensitivity and evaluation. Journal of Business Ethics, 88(4), 663–678.

  • Tolleson, T., B. D. Merino, and A. G. Mayper. (1996). Applying behavioural models as prescriptions for ethics in accounting practice and education. Research on Accounting Ethics, 2, 21–49.

  • Trivedi, V., M. Shehata, and B. Lynn. (2003). Impact of personal and situational factors on taxpayer compliance: An experimental analysis. Journal of Business Ethics, 47(3), 175–197.

  • West, T., S. Pickard Ravenscroft, and C. B. Shrader. (2004). Cheating and moral judgment in the college classroom: A natural experiment. Journal of Business Ethics, 54(2), 173–183.

Studies which specifically used MBAs

  • Maroney, J. J., and R. E. McDevitt. (2008). The effects of moral reasoning on financial reporting decisions in a post Sarbanes–Oxley environment. Behavioral Research in Accounting, 20(2), 89–110.

  • Kaplan, S. E., J. C. McElroy, S. P. Ravenscroft, and C. B. Shrader. (2007). Moral judgment and causal attributions: Consequences of engaging in earnings management. Journal of Business Ethics, 74, 149–164.

  • Kaplan, S. E., K. J. Newberry, and P. M. J. Reckers. (1997). The effect of moral reasoning and educational communications on tax evasion intentions. The Journal of the American Taxation Association, 19(2), 38–54.

  • Shapiro, B., J. Schatzberg, G. Sevcik, L. Thorne, and S. Wallace. 2005. A re-examination of behavior in experimental audit markets: The effects of moral reasoning and economic incentives on auditor reporting and fees. Contemporary Accounting Research 22(1), 229–264.

Studies which used an instrument similar to DIT or modified the DIT

  • Chan, S. Y. S., and P. Leung. 2006. The effects of accounting students’ ethical reasoning and personal factors on their ethical sensitivity. Managerial Auditing Journal 21(4), 436–457.

  • Doyle, E., J. Frecknall-Hughes, and B. Summers. (2009). Research methods in taxation ethics: Developing the DIT for a tax-specific scenario. Journal of Business Ethics, 88(1), 35–52.

  • Fleming, D. M., R. N. Romanus, and S. M. Lightner. (2009). The effect of professional context on accounting students’ moral reasoning. Issues in Accounting Education, 24(1), 13–30.

  • Green, S., and J. Weber. (1997). Influencing ethical development: Exposing students to the AICPA code of conduct. Journal of Business Ethics, 16(8), 777–790.

  • Hobson, J. L., M. J. Mellon, and D. E. Stevens. (2011). Determinants of moral judgments regarding budgetary slack: An experimental examination of pay scheme and personal values. Behavioral Research in Accounting, 23(1), 87–107.

  • Karacaer, S., R. Gohar, M. Aygün, and C. Sayin. (2009). Effects of personal values on auditor’s ethical decisions: A comparison of Pakistani and Turkish professional auditors. Journal of Business Ethics, 88(1), 53–64.

  • Leitsch, D. (2006). Using dimensions of moral intensity to predict ethical decision-making in accounting. Accounting Education: an international journal, 15(2), 135–149.

  • Liyanarachchi, G., and C. Newdick. (2009). The impact of moral reasoning and retaliation on whistle-blowing: New Zealand evidence. Journal of Business Ethics, 89(1), 37–57.

  • Mintchik, N. M., and T. A. Farmer. (2009). Associations between epistemological beliefs and moral reasoning: Evidence from accounting. Journal of Business Ethics, 84(2), 259–275.

  • Stanga, K. G., and R. A. Turpen. (1991). Ethical judgments on selected accounting issues: An empirical study. Journal of Business Ethics, 10(10), 739–747.

  • Welton, R. E., R. Lagrone, and J. R. Davis. (1994). Promoting the moral development of accounting graduate students: an instructional design and assessment. Accounting Education, 3(1), 35–50.

Statistical measures needed for meta-analysis not reported

  • Cooper, B. J., P. Leung, S. Dellaportas, B. Jackling, and G. Wong. (2008). Ethics education for accounting students: A toolkit approach. Accounting Education, 17(4), 405–430.

  • Hiltebeitel, K. M., and S. K. Jones. (1991). Initial evidence on the impact of integrating ethics into accounting education. Issues in Accounting Education, 6(2), 262–275.

  • Kite, D., and R. R. Radtke. (1997). The effect of moral reasoning levels and political ideology on environmental accounting education. Research on Accounting Ethics, 3, 173–189.

  • Koeplin, J. P. (1998). A comparison of cognitive moral development of accounting students at a Catholic University with secular university accounting students. Unpublished doctoral dissertation, University of North Texas, Denton, TX.

  • Lampe, J. C. (1996). The impact of ethics education in accounting curricula. Research on Accounting Ethics, 2, 187–220.

  • Pope, K. R. (2005). Measuring the ethical propensities of accounting students: Mach IV versus DIT. Journal of Academic Ethics, 3(2–4), 89–111.

  • Schatzberg, J. W., G. R. Sevcik, and B. P. Shapiro. (1996). Exploratory experimental evidence on independence impairment conditions: Aggregate and individual results. Behavioral Research in Accounting, 8, 173–195.

Used same subjects or data as other study in sample (other study shown in [])

  • Abdolmohammadi, M. J., and C. Baker. (2007). The relationship between moral reasoning and plagiarism in accounting courses: A replication study. Issues in Accounting Education, 22(1), 45–55. [Abdolmohammadi, M. J., and C. Baker. (2008). Moral reasoning and questionable behavior. CPA Journal 78 (11), 58–61]

  • Nkenke, G. R. (2010).The impact of moral reasoning on ethical perception, intention, and orientation of upper level accounting students. Unpublished doctoral dissertation, Minneapolis, MN. [Bancroft, P. C. (2002). An investigation of moral reasoning as a predictor of ethical awareness, ethical intention, and ethical orientation, Unpublished doctoral dissertation, Nova Southeastern, Ft. Lauderdale, FL.].

  • Porco, B. M. (2003). Factors affecting the cognitive moral development of undergraduate accounting students: Ethics education, internships, volunteerism, and Beta Alpha Psi. Unpublished doctoral dissertation, Fordham University, New York, NY. [Brown-Liburd, H. L., and B. M. Porco. (2011). It’s what’s outside that counts: Do extracurricular experiences affect the cognitive moral development of undergraduate accounting students? Issues in Accounting Education 26 (2), 439–54].

  • Sweeney, J. T., and D. G. Fisher. (1999). Politics, faking, and self-presentation: How valid is the P score of the DIT. Research on Accounting Ethics, 5, 51–75. [Fisher, D. G., and J. T. Sweeney. (1998). The relationship between political attitudes and moral judgment: Examining the validity of the DIT. Journal of Business Ethics 17(8), 905–916].

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Christensen, A.L., Cote, J. & Latham, C.K. Insights Regarding the Applicability of the Defining Issues Test to Advance Ethics Research with Accounting Students: A Meta-analytic Review. J Bus Ethics 133, 141–163 (2016). https://doi.org/10.1007/s10551-014-2349-7

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