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A note on the effect of cognate studies on performance in a second-level university course in management accounting
British ilrcourhg
Review (1990) 22, 237-246
A NOTE ON THE EFFECT OF COGNATE STUDIES ON PERFORMANCE A SECOND-LEVEL UNIVERSITY COURSE MANAGEMENT ACCOUNTING*
IN IN
STEPHEN P. KEEF Victoria Urziversity of Wellingfon
BHAGWAN
S. KHANNA
Victoria Urriversity of Wellinglon
This paper examines the impact of the prior or concurrent study in the cognate subjects of Finance, Financial Accounting and Accounting Information Systems on performance in a second-level university course in Management Accounting. An Analysis of Covariance was used to test five hypotheses. There was insufficient evidence that prior or concurrent study of Finance and Financial Accounting had any influence on the performance in the Management Accounting course. There was some weak evidence (P = 0.04) that the study of Accounting Information Systems carried a positive benefit.
INTRODUCTION Studies into the influence of prior study of Accounting on first-level university courses in Financial Accounting have been widely reported in the literature (Baldwin & Howe, 1982; Bergin, 1983; Buehlmann & Techavichit, 1984; Mitchell, 1985; Canlar, 1986; Schroeder, 1986). In contrast, the complementary subject of Management Accounting has been scantily studied, although Mitchell (1985, p. 86) recommends it merits attention. The object of this study was to determine whether prior or concurrent study in the cognate second-level’ university courses of Finance, Financial Accounting and Accounting Information Systems affected performance in a corresponding course in Management Accounting.* Keef (1988) examined the factors that affected performance in the Management Accounting section of a first-level university course in Accountancy.3 Performance, after correcting for the effects of proxies for academic ability was: (a) positively related to the highest level of previous *The helpful advice and assistance by Raghavan J. Iyengar is gratefully appreciated and acknowledged. Correspondence: Bhagwan S. Khanna & Stephen P. Keef, Faculty of Commerce and Administration, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand.
089(M939/90/030237 + 10 $03.00/O
~6 1990 Academic
Press Limited
238
S. P.
KEEF
AND
TABLE Sample sizes and deviatimfrotn
B.
S. KHANNA
1
grand mean by level ojjactor (final mark in the course without adjustmentfir academic ability) Course
Level of Factor 1 = Not attempted the course 2 = Concurrent attempt 3 = Prior attempt
Financial Accounting 36/ - 0.09 122/-0.85 43/+2.49
Finance
Accounting Information Systems
128/-0.67 n/a* 73/+1,18
138-1.10 35/+2.15 28/+2,73
*n/a = not applicable
study in Economics and Mathematics; and (b) independent of university experience and whether Accounting was studied at school. It is unclear, however, as to whether these results can be generalised to the second-level course in Management Accounting. A literature search yielded only one other empirical study of direct relevance. Mitchell (1985) examined a first-level university course in Scotland with a 60/40 division between Financial Accounting and Management Accounting. He found that prior study of Accounting at school provided a positive advantage in the technical and computational aspects of the course. However, it provided virtually no advantage in the degree examination which consisted of essay questions. It could be argued that the concurrent or prior study of one subject could provide a comparative advantage in the performance in another subject. The degree of benefit would depend upon the commonality between the two subjects. That is, the degree that their sets of concepts, fields of knowledge and methods of communication overlap. The commonality between Management Accounting and the subjects of Financial Accounting and Accounting Information Systems is clearly evident from the inspection of the content of Accounting degrees [Perks & Morrell, 1981; Lyall, 1985 (in the UK); Hadley & Balke, 1979 (in the USA)]. The argument for believing that the prior study of Finance will carry a positive benefit in the Management Accounting course is based on the observation that both subjects share similar material (William & Swanson, 1988, p. 27). Student choice can provide another insight into the importance of the cognate subjects. The proportion of students who had no exposure (prior or concurrent) to the cognate subjects was (Table 1): Finance (64%), Accounting Information Systems (69%) and Financial Accounting (18%). Apparently, student choice attached a significantly higher priority to the
EFFECT
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Financial Accounting course but showed indifference between the other two courses.4 However, students may not choose their course timings solely for reasons of academic performance. The Financial Accounting course is a crucial prerequisite for the advanced accounting courses that are necessary to meet the academic requirements for associate membership of the New Zealand Society of Accountants. The other courses were compulsory but were not prerequisites for advanced compulsory courses. Thus, potential chartered accountants face a delay in the achievement of their goal if they do not pass the second-level Financial Accounting course early in their studies. THE
COURSE
Management Accounting (ACCY 203) is a second-level course at Victoria University of Wellington. The course, as did the cognate courses, entailed three one-hour lectures and a one-hour tutorial over a semester of twelve teaching weeks. There was little reason to suggest that the content of the course (Table 2) was atypical of its genre. The text ‘Cost Accounting: Accounting Data for Management Decisions’ by Dopuch, Birnberg & Demski (1982) had been used for the last five years. In 1987, performance in the course was based on two one-hour tests ((4) 2/9 each) an d a t wo-hour final examination (@ 5/9). All three examinations emphasised the computational and technical aspects of the curriculum. Essay questions formed only a small part of the assessment. METHOD The Appendix presents a full description of the methodology. This section provides a non-technical description of the analysis that was carried out. In testing for the effects of cognate study it is necessary to acknowledge that spurious results may arise due solely to sampling errors. Consider, for example, the situation where those that had taken a cognate course had, for some reason, a higher academic ability than those who had not taken the course. Even if the study of the cognate subject did not actually carry a positive benefit, the results would show that prior study apparently carried a benefit. This would be a false result attributable to sampling errors. To guard against this distinct possibility it was necessary to obtain measures of the academic ability of the students. Performances in a number of university examinations were used to obtain estimates of academic ability and experience in the university system. Using the equivalent of a regression procedure, an analysis of covariance, the systematic element of
240
S. P.
KEEF
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TABLE
B.
S. KHANNA
2
Come Outline, ACCY 203: Manugrment Accorrtiting, 1987 Week of
Reading
Topic
July 6
(I) Introduction review
and
July 13
(2) Income effects of
Chapters I to 4 Chapter 4
Problems l-9,2-21,
3-24,4-l 1
pp. 107-126
July 20
(3)
July 27 August 3 August 3
(4)
August 10 & August 17 September 7
(6)
(5)
September 14 September 14 September 21 September 28 October
29
(7)
(8)
alternating product costing methods Job order and process cost systems Budgetary planning Examination-l Cost analysis and capital budgeting Budgetary control and performance evaluation Transfer pricing and decentralisation Examination-2 Analysis of variance
(9) Variance investigation (10) Current assets: planning and control Final examination
Chapter
11
11-12
Chapter
12
12-12,12-19
Chapter
13
13-20
Chapter
14
14-13
Chapter 5 and 6 Chapter 10
5-17 6-32 l(t-15
Chapter 7 (excluding Appendix) Chapter 8 Chapter 16 (excluding Appendix)
7-34 8-7 16-11
performance in the Management Accounting associated with these variables was eliminated. The residual or unexplained errors were then used to test for the effects of cognate studies. This was effected using an Analysis of Variance procedure. DISCUSSION The results are discussed in the Appendix. The study provided no evidence to suggest that the prior or concurrent study of either Financial Accounting or Finance carried any significant benefit. An explanation for these results may be attributable to the manner in which the Management Accounting course was taught. It effectively started from first principles and was essentially self-contained. There was some weak evidence that the study of Accounting Information Systems, either prior or concurrent, carried a positive benefit. However, care needs to be exercised in concluding that the course was truly beneficial. The differences in marks was relatively small (Table 1).
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This, together with the low level of statistical support (P = 0.04), would warrant this result being given a tentative status. Further research is necessary to determine the importance of this result. This study is not without limitations. Results based on only one institution for merely one year may not be generalised to other institutions. The study could be improved in a variety of ways. Firstly, the level ofprior study in the subjects of Mathematics and Economics could be included in the design. In this study we were denied the opportunity to adequately test and control for the effects of these potentially important variables. Secondly, there are a wide variety of other factors that may affect student performance. Some examples are: parental occupation (Kornbrot, 1987) and self-concept (Smart & Pascarella, 1986). The question as to how these influence performance in Management Accounting courses needs to be addressed. Given these results we would advise our students that the timing of the Finance and Financial Accounting courses is best left to personal preferences or circumstances. We would tentatively suggest that there appears to be some advantage associated with prior (or concurrent) study of Accounting Information Systems. We would point out that if our advice were based on sampling error, then such study would not be of disadvantage. The Faculty has recently increased the scope of the Financial Accounting prerequisite for the Management Accounting course. Essentially, the Management Accounting and Financial Management content of the firstlevel course in Accountancy has been replaced by Financial Accounting material. The results would indicate that the decision will not result in an increase in student performance in the course, but on the other hand, it will not cause any harm.
NOTES
1. All undergraduate courses at the University are given a code of 100,200, or 300 which are equivalent to first, second and third-level courses at other institutions. Given the wide choice of courses that can contribute towards a degree, the connotation of first-, second- and third-year courses must be used with care. 2. It was also intended to investigate the influences of the level of study in Mathematics and Economics. Given the overlap between these two subjects and Management Accounting, there is a good reason to expect that they would accrue some benefit. However, due to insufficient differentiation between students, occasioned by the compulsory core of the degree, it was not possible to make a valid test of hypotheses based on these subjects. 3. ACCY 101: Accountancy was the academic prerequisite for the Management Accounting course. It was an introduction to Financial Accounting, Financial Management and Management Accounting.
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4. To test for differences in the proportion of students who had prior (or concurrent) cxposurc to the three courses, the data was rccodcd as 1 = no attempt at the course. otherwise 0. A Friedman two-way analysts of vartance by ranks, for the courses of Finance, Financial Accounting and Accounting Information Systems produced a calculated chi-square of70.91 on 2 degrees of freedom (P < 0.01). A Wilcoxon matchedpairs signed-rank test comparing Finance and Accounting Information Systems, produced a calculated r-value of 1.14 (P = 0.26).
REFERENCES
Baldwin, B. A. & Howe, K. R. (1982). ‘Secondary-level study of accounting and subsequent performance in the first college course’, The Accounting Review, July, pp. 619626. Bergin, J. L. (1983). ‘The effects of previous accounting study on student performance in the first college-level financial accounting course’, Isslres in Accounting Education, American Accounting Association, pp. 19-28. Buehlmann, D. M. & Techavichit, J. V. (1984). ‘Factors influencing final examination performance in large versus small sections of accounting principles’,]ournal ofAccounting Education, Vol. 2, No. 1, Spring, pp. 127-136. Canlar, M. (1986). ‘College-level exposure to accounting study and its effect on student performance in the first MBA-level financial accounting course’, lrslres in Accounting Education, Vol. 1, No. 1, Spring, pp. 13-23. Hadley, G. D. 81 Balke, T. E. (1979). ‘A comparison of academic and practitioner views of content levels in the undergraduate accounting curriculum’, The Accounting Review, Vol. 54, No. 2, April, pp. 383-389. Hull, C. H. 81 Nie, N. H. (1981). SPSS Update 7-9, New York: McGraw-Hill Keef, S. P. (1988). ‘Preparation for a first level university accounting course: the experience in New Zcaland’,]ournal ofAccounting Education, Vol. 6, No. 2, pp. 293307. Kornbrot, D. E. (1987). ‘Degree performance as a function of discipline studied, parental occupation and gender’, Higher Education, Vol. 16, pp. 513-534. Lyall, D. (1985). ‘Content levels in undergraduate accounting courses-views from industry and the profession’, British Accounting Review, Spring. Mitchell, F. (1985). ‘School accounting qualifications and student performance in the first level university accounting examinations’, Accounting and Blrsiness Research, No. 58, Spring, pp. 81-86. Pearson, E. S. & Hartley, H. 0. (1958). Biometrika Tablesfor Statisticians, 2nd edn, Vol. 1, Cambridge: Cambridge University Press. Perks, R. W. & Morrell, J. B. (1981). ‘M ana g ement accounting in degree courses in UK universities and polytechnics’, British Accounting Review, Vol. 13, No. 1, Spring, pp. 3241 Schroeder, N. W. (1986). ‘Previous accounting education and college-level accounting exam performance’, Issues in Accounting Education, Vol. 1, No. 1, Spring, pp. 37-47. Smart, J. C. & Pascarella, E. T. (1986). ‘Self-concept development and educational degree attainment’, Higher Education, Vol. 15, pp. 3-15. Tiller, M. G. 81 White, C. (1983). ‘Are accounting majors really better? Evaluating admissions and retention standards for undergraduate accounting programs’, Journal of Accounting Education, Vol. 1, No. 1, Spring, pp. 19-27. William, N. C. & Swanson, G. A. (1988). ‘Topical interface between managerial finance
EFFECT OF COGNATE
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PERFORMANCE
and managerial accounting’, &ma/ of Education for Business, Vol. 66, No. 1, October, pp. 2527. Winer, B. J. (1971). Statistical Principles in Experimental Design, 2nd edn, New York: McGraw-Hill. Dates: Received 28 September 1988; fnml version received 5 May lY8Y
APPENDIX The detailed rtatisticnl analysis METHOD The research questions were addressed with the following
five null hypotheses.’
H, Performance was independent of whether the Financial Accounting course was attempted (prior or concurrent). Hz Performance was independent of whether the Accounting Infomation Systems course was attempted (prior or concurrent). H, Performance was independent of whether the Finance course was attempted. H, Performance was independent of whether the Financial Accounting course was attempted either prior or concurrent. H, Performance was independent of whether the Accounting Information course was attempted either prior or concurrent. To control for bias associated with different samples of academic ability it was decided to factor the seven variables described in Table Al. These were expected to capture the important elements of student ability. Factor analysis was chosen for three reasons. Firstly, the variables would be expected to exhibit multicolinearity. Secondly, there was some uncertainty as to whether they were sampled from normal populations. Thirdly, a few
TABLE
Al
Varimax rotatedfactor matrix Factor Variable Mark in first course in Economics Mark in first course in Accountancy Mean mark, first semester Mean mark, previous year Number of years at University Number of courses attempted prior to 1987 Percentage pass rate at University Eigenvaluc Cumulative variance explained* Name of factor
1 0.76 0.75 0.73 0.87 -0.14 - 0.09 0.58 3.10 49.7 Ability
2 -0.17 - 0.05 0.04 -0.14 0.91 0.92 -0.26 1.51 73.8 Experience
*The initial two factors with eigenvalues greater than unity, prior to rotatzon, explained 734% of the variation in the seven variables.
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missing values were encountered, as not all the students had taken the first course in Economics. A priori, two factors were expected to be of importance. The first was a measure of the academic ability of the student. The second was a measure of the students’ experience in the university system. It was decided to test the hypotheses, expressed in their null form, using an analysis of covariance (ANCOVA). The dependent variable was the weighted average performance in the course. The covariates were the ‘ability’ and ‘experience’ variables generated by the factor analysis. The non-orthogonal design necessitated the use of a hierarchical or sequential sums of squares approach (Tiller 81 White, 1983; Hull & Nie, 1981). By running separate analyses it was possible to obtain estimates of the sums of squares for each factor, and its contrasts, after corrections had been made for the effects of the other factors and covariates. RESULTS The factor analysis generated two factors of importance. The first, with an eigenvalue of 3.10, explained half of the variation in the seven descriptive variables. It has been labelled ‘ability’ since it loads heavily on the variables measuring the student’s previous examination performance. The second factor has been named ‘experience’. It is a measure ofthe student’s experience at the University. The eigenvalue was 1.51 and the factor explained just under a quarter of the variation of the initial variables. The assumptions necessary for the use of ANOVA tests are that the observations within the cells were independently sampled from normal populations having the same variance. The normality assumption was not denied for the independent variable and the covariate of ‘ability’.* However, the test is accepted as being robust to deviations from normality and homogeneity of variance (Winer, 1971). A further assumption of homogeneity of slopes is necessary when covariates are introduced to form an ANCOVA. After correcting for the effects of the factors and covariates, each factor by ability covariate interaction was not important (P > 0.26). Thus, we infer that the homogeneity of slopes assumption was not denied for the ability covariate. Since the experience covariate was found to be inefficient, the homogeneity of slopes test was not conducted. The ‘experience’ covariate was essentially useless in explaining variations in performance in the course. It was initially expected that increased experience in the university system would give the student a ‘headstart’ over their less experienced peers. However, there was evidence of a saturation effect. All the students in the study had spent at least one semester at the University and it is apparent that this was sufficient for them to ‘learn how to study well for the examination’. The ‘ability’ covariate exhibited the expected positive correlation with performance in the course. The proportion of systematic bias eliminated (ra = 0.41) was slightly better than achieved by others (Baldwin & Howe, 1982; Schroeder, 1986). A partial explanation centres around the observation that the independent variable and the ‘ability’ covariate were both weighted averages of examination performances. Thus in both cases, random error had effectively been diversified away. Table A2 presents the ANCOVA results which test the null hypotheses and seek to determine whether there was a statistical difference between the levels of the three factors. It was apparent that the study in Finance (P = 0.26) provided no significant benefit in explaining variations in performance. Thus, hypothesis H, was not rejected. Strictly interpreted, there was insufficient evidence to reject null hypotheses H, and H, which tested for the effects of study of Financial Accounting. The sum of squares of the Financial Accounting factor was only 211.1. Even if these were attributed to only one degree of freedom, any differences would not be statistically significant (P = 0.14). This is confirmed by inspection of the contrasts.
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PERFORMANCE
TABLE A2 Analysis of covariance table
of squares
Sum
Source of variation
- -
Covariates ‘Ability’ ‘Experience’
Degrees of freedom
Mean square
F ratio
Significance of F ratio
< 0.001 0.68
13,727.5 16.5
1 1
13,727.5 16.5
142.50 0.17
211.1 20.4 210.6
2 1 1
105.6 20.4 210.6
1.10 0.21 2.20
0.34 0.64 0.14
Finance (F)
123.0
1
123.0
1.28
0.26
Accounting Information Systems (AIS) Contrast (a) Contrast (b)
440.4 426.0 3.7
2 1 1
220.2 426.0 3.7
2.29 4.47 0.04
0.11 0.04 0.84
FA by F FA by AIS F by AIS
57.8 386.4 38.5
2 4 2
28.9 96.6 19.2
0.30 1.00 0.82
0.74 0.41 0.82
Financial Accounting Contrast (a) Contrast (b)
(FA)
FA by F by AIS Explained
297.5
2
148.8
1.54
0.22
15,520.6
17
913.0
9.48
< 0.001
Residual
17,628.9
183
96.33
Total
33,149.6
200
165.75
Note: The contrasts were specified as follows: Contrasts Level of Factor
b
‘I
1 = Not attempted the course 2 = Concurrent attempt 3 = Prior attempt
The Accounting Information of differences between the levels that the absence of study of the The practical difference between grey area. Whether the subject (H;, P = 0.84).
-2
0 1 1
-1 I
Systems factor indicated the strongest statistical evidence (P = 0.11).3 Contrast (a), testing hypothesis H,, indicated subject penalised the students by 3.51 marks (P = 0.04). the means and the level of statistical significance are in a was studied prior or concurrently made little difference
NOTES
TO
APPENDIX
I. These hypotheses are a reflection of the timing of the courses at the University. Finance is taught in the first semester whereas the Management Accounting and other courses are taught in the
S. P.
KEEF
AND
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S. KHANNA
recond semester. Thus, the timing of the courses introduces the confounding of prior and concurrent studies for the subjects of Financial Accounting and Accounting information Systems. 2. Kolmogorov-Smirnov one-sample tests, using the sample mean and standard deviation as the reference, were used to test for normality. The assumption was denied for the ‘experience’ covariate (P < OU~l) but not for either the independent variable (P = 0.82) or the ‘ability’ covariate (P = 0.47). The result for the ‘expenencc covariate is most likely of limited importance since its explanatory power was essentially zero. 3. The statistical importance of the contrasts can be approached in two ways. The first is only to examine the contrasts if the factor meets the desired level of statistical significance. The second is to essentially ignore the statistical significance of the factor and to concentrate on the contrasts individually. Opinion, we believe, is equally divided on the appropriateness of these two approaches. WC have chosen to adopt the latter approach.
Review (1990) 22, 237-246
A NOTE ON THE EFFECT OF COGNATE STUDIES ON PERFORMANCE A SECOND-LEVEL UNIVERSITY COURSE MANAGEMENT ACCOUNTING*
IN IN
STEPHEN P. KEEF Victoria Urziversity of Wellingfon
BHAGWAN
S. KHANNA
Victoria Urriversity of Wellinglon
This paper examines the impact of the prior or concurrent study in the cognate subjects of Finance, Financial Accounting and Accounting Information Systems on performance in a second-level university course in Management Accounting. An Analysis of Covariance was used to test five hypotheses. There was insufficient evidence that prior or concurrent study of Finance and Financial Accounting had any influence on the performance in the Management Accounting course. There was some weak evidence (P = 0.04) that the study of Accounting Information Systems carried a positive benefit.
INTRODUCTION Studies into the influence of prior study of Accounting on first-level university courses in Financial Accounting have been widely reported in the literature (Baldwin & Howe, 1982; Bergin, 1983; Buehlmann & Techavichit, 1984; Mitchell, 1985; Canlar, 1986; Schroeder, 1986). In contrast, the complementary subject of Management Accounting has been scantily studied, although Mitchell (1985, p. 86) recommends it merits attention. The object of this study was to determine whether prior or concurrent study in the cognate second-level’ university courses of Finance, Financial Accounting and Accounting Information Systems affected performance in a corresponding course in Management Accounting.* Keef (1988) examined the factors that affected performance in the Management Accounting section of a first-level university course in Accountancy.3 Performance, after correcting for the effects of proxies for academic ability was: (a) positively related to the highest level of previous *The helpful advice and assistance by Raghavan J. Iyengar is gratefully appreciated and acknowledged. Correspondence: Bhagwan S. Khanna & Stephen P. Keef, Faculty of Commerce and Administration, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand.
089(M939/90/030237 + 10 $03.00/O
~6 1990 Academic
Press Limited
238
S. P.
KEEF
AND
TABLE Sample sizes and deviatimfrotn
B.
S. KHANNA
1
grand mean by level ojjactor (final mark in the course without adjustmentfir academic ability) Course
Level of Factor 1 = Not attempted the course 2 = Concurrent attempt 3 = Prior attempt
Financial Accounting 36/ - 0.09 122/-0.85 43/+2.49
Finance
Accounting Information Systems
128/-0.67 n/a* 73/+1,18
138-1.10 35/+2.15 28/+2,73
*n/a = not applicable
study in Economics and Mathematics; and (b) independent of university experience and whether Accounting was studied at school. It is unclear, however, as to whether these results can be generalised to the second-level course in Management Accounting. A literature search yielded only one other empirical study of direct relevance. Mitchell (1985) examined a first-level university course in Scotland with a 60/40 division between Financial Accounting and Management Accounting. He found that prior study of Accounting at school provided a positive advantage in the technical and computational aspects of the course. However, it provided virtually no advantage in the degree examination which consisted of essay questions. It could be argued that the concurrent or prior study of one subject could provide a comparative advantage in the performance in another subject. The degree of benefit would depend upon the commonality between the two subjects. That is, the degree that their sets of concepts, fields of knowledge and methods of communication overlap. The commonality between Management Accounting and the subjects of Financial Accounting and Accounting Information Systems is clearly evident from the inspection of the content of Accounting degrees [Perks & Morrell, 1981; Lyall, 1985 (in the UK); Hadley & Balke, 1979 (in the USA)]. The argument for believing that the prior study of Finance will carry a positive benefit in the Management Accounting course is based on the observation that both subjects share similar material (William & Swanson, 1988, p. 27). Student choice can provide another insight into the importance of the cognate subjects. The proportion of students who had no exposure (prior or concurrent) to the cognate subjects was (Table 1): Finance (64%), Accounting Information Systems (69%) and Financial Accounting (18%). Apparently, student choice attached a significantly higher priority to the
EFFECT
OF
COGNATE
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ON
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PERFORMANCE
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Financial Accounting course but showed indifference between the other two courses.4 However, students may not choose their course timings solely for reasons of academic performance. The Financial Accounting course is a crucial prerequisite for the advanced accounting courses that are necessary to meet the academic requirements for associate membership of the New Zealand Society of Accountants. The other courses were compulsory but were not prerequisites for advanced compulsory courses. Thus, potential chartered accountants face a delay in the achievement of their goal if they do not pass the second-level Financial Accounting course early in their studies. THE
COURSE
Management Accounting (ACCY 203) is a second-level course at Victoria University of Wellington. The course, as did the cognate courses, entailed three one-hour lectures and a one-hour tutorial over a semester of twelve teaching weeks. There was little reason to suggest that the content of the course (Table 2) was atypical of its genre. The text ‘Cost Accounting: Accounting Data for Management Decisions’ by Dopuch, Birnberg & Demski (1982) had been used for the last five years. In 1987, performance in the course was based on two one-hour tests ((4) 2/9 each) an d a t wo-hour final examination (@ 5/9). All three examinations emphasised the computational and technical aspects of the curriculum. Essay questions formed only a small part of the assessment. METHOD The Appendix presents a full description of the methodology. This section provides a non-technical description of the analysis that was carried out. In testing for the effects of cognate study it is necessary to acknowledge that spurious results may arise due solely to sampling errors. Consider, for example, the situation where those that had taken a cognate course had, for some reason, a higher academic ability than those who had not taken the course. Even if the study of the cognate subject did not actually carry a positive benefit, the results would show that prior study apparently carried a benefit. This would be a false result attributable to sampling errors. To guard against this distinct possibility it was necessary to obtain measures of the academic ability of the students. Performances in a number of university examinations were used to obtain estimates of academic ability and experience in the university system. Using the equivalent of a regression procedure, an analysis of covariance, the systematic element of
240
S. P.
KEEF
AND
TABLE
B.
S. KHANNA
2
Come Outline, ACCY 203: Manugrment Accorrtiting, 1987 Week of
Reading
Topic
July 6
(I) Introduction review
and
July 13
(2) Income effects of
Chapters I to 4 Chapter 4
Problems l-9,2-21,
3-24,4-l 1
pp. 107-126
July 20
(3)
July 27 August 3 August 3
(4)
August 10 & August 17 September 7
(6)
(5)
September 14 September 14 September 21 September 28 October
29
(7)
(8)
alternating product costing methods Job order and process cost systems Budgetary planning Examination-l Cost analysis and capital budgeting Budgetary control and performance evaluation Transfer pricing and decentralisation Examination-2 Analysis of variance
(9) Variance investigation (10) Current assets: planning and control Final examination
Chapter
11
11-12
Chapter
12
12-12,12-19
Chapter
13
13-20
Chapter
14
14-13
Chapter 5 and 6 Chapter 10
5-17 6-32 l(t-15
Chapter 7 (excluding Appendix) Chapter 8 Chapter 16 (excluding Appendix)
7-34 8-7 16-11
performance in the Management Accounting associated with these variables was eliminated. The residual or unexplained errors were then used to test for the effects of cognate studies. This was effected using an Analysis of Variance procedure. DISCUSSION The results are discussed in the Appendix. The study provided no evidence to suggest that the prior or concurrent study of either Financial Accounting or Finance carried any significant benefit. An explanation for these results may be attributable to the manner in which the Management Accounting course was taught. It effectively started from first principles and was essentially self-contained. There was some weak evidence that the study of Accounting Information Systems, either prior or concurrent, carried a positive benefit. However, care needs to be exercised in concluding that the course was truly beneficial. The differences in marks was relatively small (Table 1).
EFFECT
OF
COGNATE
STUDIES
ON
COURSE
PERFORMANCE
241
This, together with the low level of statistical support (P = 0.04), would warrant this result being given a tentative status. Further research is necessary to determine the importance of this result. This study is not without limitations. Results based on only one institution for merely one year may not be generalised to other institutions. The study could be improved in a variety of ways. Firstly, the level ofprior study in the subjects of Mathematics and Economics could be included in the design. In this study we were denied the opportunity to adequately test and control for the effects of these potentially important variables. Secondly, there are a wide variety of other factors that may affect student performance. Some examples are: parental occupation (Kornbrot, 1987) and self-concept (Smart & Pascarella, 1986). The question as to how these influence performance in Management Accounting courses needs to be addressed. Given these results we would advise our students that the timing of the Finance and Financial Accounting courses is best left to personal preferences or circumstances. We would tentatively suggest that there appears to be some advantage associated with prior (or concurrent) study of Accounting Information Systems. We would point out that if our advice were based on sampling error, then such study would not be of disadvantage. The Faculty has recently increased the scope of the Financial Accounting prerequisite for the Management Accounting course. Essentially, the Management Accounting and Financial Management content of the firstlevel course in Accountancy has been replaced by Financial Accounting material. The results would indicate that the decision will not result in an increase in student performance in the course, but on the other hand, it will not cause any harm.
NOTES
1. All undergraduate courses at the University are given a code of 100,200, or 300 which are equivalent to first, second and third-level courses at other institutions. Given the wide choice of courses that can contribute towards a degree, the connotation of first-, second- and third-year courses must be used with care. 2. It was also intended to investigate the influences of the level of study in Mathematics and Economics. Given the overlap between these two subjects and Management Accounting, there is a good reason to expect that they would accrue some benefit. However, due to insufficient differentiation between students, occasioned by the compulsory core of the degree, it was not possible to make a valid test of hypotheses based on these subjects. 3. ACCY 101: Accountancy was the academic prerequisite for the Management Accounting course. It was an introduction to Financial Accounting, Financial Management and Management Accounting.
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4. To test for differences in the proportion of students who had prior (or concurrent) cxposurc to the three courses, the data was rccodcd as 1 = no attempt at the course. otherwise 0. A Friedman two-way analysts of vartance by ranks, for the courses of Finance, Financial Accounting and Accounting Information Systems produced a calculated chi-square of70.91 on 2 degrees of freedom (P < 0.01). A Wilcoxon matchedpairs signed-rank test comparing Finance and Accounting Information Systems, produced a calculated r-value of 1.14 (P = 0.26).
REFERENCES
Baldwin, B. A. & Howe, K. R. (1982). ‘Secondary-level study of accounting and subsequent performance in the first college course’, The Accounting Review, July, pp. 619626. Bergin, J. L. (1983). ‘The effects of previous accounting study on student performance in the first college-level financial accounting course’, Isslres in Accounting Education, American Accounting Association, pp. 19-28. Buehlmann, D. M. & Techavichit, J. V. (1984). ‘Factors influencing final examination performance in large versus small sections of accounting principles’,]ournal ofAccounting Education, Vol. 2, No. 1, Spring, pp. 127-136. Canlar, M. (1986). ‘College-level exposure to accounting study and its effect on student performance in the first MBA-level financial accounting course’, lrslres in Accounting Education, Vol. 1, No. 1, Spring, pp. 13-23. Hadley, G. D. 81 Balke, T. E. (1979). ‘A comparison of academic and practitioner views of content levels in the undergraduate accounting curriculum’, The Accounting Review, Vol. 54, No. 2, April, pp. 383-389. Hull, C. H. 81 Nie, N. H. (1981). SPSS Update 7-9, New York: McGraw-Hill Keef, S. P. (1988). ‘Preparation for a first level university accounting course: the experience in New Zcaland’,]ournal ofAccounting Education, Vol. 6, No. 2, pp. 293307. Kornbrot, D. E. (1987). ‘Degree performance as a function of discipline studied, parental occupation and gender’, Higher Education, Vol. 16, pp. 513-534. Lyall, D. (1985). ‘Content levels in undergraduate accounting courses-views from industry and the profession’, British Accounting Review, Spring. Mitchell, F. (1985). ‘School accounting qualifications and student performance in the first level university accounting examinations’, Accounting and Blrsiness Research, No. 58, Spring, pp. 81-86. Pearson, E. S. & Hartley, H. 0. (1958). Biometrika Tablesfor Statisticians, 2nd edn, Vol. 1, Cambridge: Cambridge University Press. Perks, R. W. & Morrell, J. B. (1981). ‘M ana g ement accounting in degree courses in UK universities and polytechnics’, British Accounting Review, Vol. 13, No. 1, Spring, pp. 3241 Schroeder, N. W. (1986). ‘Previous accounting education and college-level accounting exam performance’, Issues in Accounting Education, Vol. 1, No. 1, Spring, pp. 37-47. Smart, J. C. & Pascarella, E. T. (1986). ‘Self-concept development and educational degree attainment’, Higher Education, Vol. 15, pp. 3-15. Tiller, M. G. 81 White, C. (1983). ‘Are accounting majors really better? Evaluating admissions and retention standards for undergraduate accounting programs’, Journal of Accounting Education, Vol. 1, No. 1, Spring, pp. 19-27. William, N. C. & Swanson, G. A. (1988). ‘Topical interface between managerial finance
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and managerial accounting’, &ma/ of Education for Business, Vol. 66, No. 1, October, pp. 2527. Winer, B. J. (1971). Statistical Principles in Experimental Design, 2nd edn, New York: McGraw-Hill. Dates: Received 28 September 1988; fnml version received 5 May lY8Y
APPENDIX The detailed rtatisticnl analysis METHOD The research questions were addressed with the following
five null hypotheses.’
H, Performance was independent of whether the Financial Accounting course was attempted (prior or concurrent). Hz Performance was independent of whether the Accounting Infomation Systems course was attempted (prior or concurrent). H, Performance was independent of whether the Finance course was attempted. H, Performance was independent of whether the Financial Accounting course was attempted either prior or concurrent. H, Performance was independent of whether the Accounting Information course was attempted either prior or concurrent. To control for bias associated with different samples of academic ability it was decided to factor the seven variables described in Table Al. These were expected to capture the important elements of student ability. Factor analysis was chosen for three reasons. Firstly, the variables would be expected to exhibit multicolinearity. Secondly, there was some uncertainty as to whether they were sampled from normal populations. Thirdly, a few
TABLE
Al
Varimax rotatedfactor matrix Factor Variable Mark in first course in Economics Mark in first course in Accountancy Mean mark, first semester Mean mark, previous year Number of years at University Number of courses attempted prior to 1987 Percentage pass rate at University Eigenvaluc Cumulative variance explained* Name of factor
1 0.76 0.75 0.73 0.87 -0.14 - 0.09 0.58 3.10 49.7 Ability
2 -0.17 - 0.05 0.04 -0.14 0.91 0.92 -0.26 1.51 73.8 Experience
*The initial two factors with eigenvalues greater than unity, prior to rotatzon, explained 734% of the variation in the seven variables.
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missing values were encountered, as not all the students had taken the first course in Economics. A priori, two factors were expected to be of importance. The first was a measure of the academic ability of the student. The second was a measure of the students’ experience in the university system. It was decided to test the hypotheses, expressed in their null form, using an analysis of covariance (ANCOVA). The dependent variable was the weighted average performance in the course. The covariates were the ‘ability’ and ‘experience’ variables generated by the factor analysis. The non-orthogonal design necessitated the use of a hierarchical or sequential sums of squares approach (Tiller 81 White, 1983; Hull & Nie, 1981). By running separate analyses it was possible to obtain estimates of the sums of squares for each factor, and its contrasts, after corrections had been made for the effects of the other factors and covariates. RESULTS The factor analysis generated two factors of importance. The first, with an eigenvalue of 3.10, explained half of the variation in the seven descriptive variables. It has been labelled ‘ability’ since it loads heavily on the variables measuring the student’s previous examination performance. The second factor has been named ‘experience’. It is a measure ofthe student’s experience at the University. The eigenvalue was 1.51 and the factor explained just under a quarter of the variation of the initial variables. The assumptions necessary for the use of ANOVA tests are that the observations within the cells were independently sampled from normal populations having the same variance. The normality assumption was not denied for the independent variable and the covariate of ‘ability’.* However, the test is accepted as being robust to deviations from normality and homogeneity of variance (Winer, 1971). A further assumption of homogeneity of slopes is necessary when covariates are introduced to form an ANCOVA. After correcting for the effects of the factors and covariates, each factor by ability covariate interaction was not important (P > 0.26). Thus, we infer that the homogeneity of slopes assumption was not denied for the ability covariate. Since the experience covariate was found to be inefficient, the homogeneity of slopes test was not conducted. The ‘experience’ covariate was essentially useless in explaining variations in performance in the course. It was initially expected that increased experience in the university system would give the student a ‘headstart’ over their less experienced peers. However, there was evidence of a saturation effect. All the students in the study had spent at least one semester at the University and it is apparent that this was sufficient for them to ‘learn how to study well for the examination’. The ‘ability’ covariate exhibited the expected positive correlation with performance in the course. The proportion of systematic bias eliminated (ra = 0.41) was slightly better than achieved by others (Baldwin & Howe, 1982; Schroeder, 1986). A partial explanation centres around the observation that the independent variable and the ‘ability’ covariate were both weighted averages of examination performances. Thus in both cases, random error had effectively been diversified away. Table A2 presents the ANCOVA results which test the null hypotheses and seek to determine whether there was a statistical difference between the levels of the three factors. It was apparent that the study in Finance (P = 0.26) provided no significant benefit in explaining variations in performance. Thus, hypothesis H, was not rejected. Strictly interpreted, there was insufficient evidence to reject null hypotheses H, and H, which tested for the effects of study of Financial Accounting. The sum of squares of the Financial Accounting factor was only 211.1. Even if these were attributed to only one degree of freedom, any differences would not be statistically significant (P = 0.14). This is confirmed by inspection of the contrasts.
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TABLE A2 Analysis of covariance table
of squares
Sum
Source of variation
- -
Covariates ‘Ability’ ‘Experience’
Degrees of freedom
Mean square
F ratio
Significance of F ratio
< 0.001 0.68
13,727.5 16.5
1 1
13,727.5 16.5
142.50 0.17
211.1 20.4 210.6
2 1 1
105.6 20.4 210.6
1.10 0.21 2.20
0.34 0.64 0.14
Finance (F)
123.0
1
123.0
1.28
0.26
Accounting Information Systems (AIS) Contrast (a) Contrast (b)
440.4 426.0 3.7
2 1 1
220.2 426.0 3.7
2.29 4.47 0.04
0.11 0.04 0.84
FA by F FA by AIS F by AIS
57.8 386.4 38.5
2 4 2
28.9 96.6 19.2
0.30 1.00 0.82
0.74 0.41 0.82
Financial Accounting Contrast (a) Contrast (b)
(FA)
FA by F by AIS Explained
297.5
2
148.8
1.54
0.22
15,520.6
17
913.0
9.48
< 0.001
Residual
17,628.9
183
96.33
Total
33,149.6
200
165.75
Note: The contrasts were specified as follows: Contrasts Level of Factor
b
‘I
1 = Not attempted the course 2 = Concurrent attempt 3 = Prior attempt
The Accounting Information of differences between the levels that the absence of study of the The practical difference between grey area. Whether the subject (H;, P = 0.84).
-2
0 1 1
-1 I
Systems factor indicated the strongest statistical evidence (P = 0.11).3 Contrast (a), testing hypothesis H,, indicated subject penalised the students by 3.51 marks (P = 0.04). the means and the level of statistical significance are in a was studied prior or concurrently made little difference
NOTES
TO
APPENDIX
I. These hypotheses are a reflection of the timing of the courses at the University. Finance is taught in the first semester whereas the Management Accounting and other courses are taught in the
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recond semester. Thus, the timing of the courses introduces the confounding of prior and concurrent studies for the subjects of Financial Accounting and Accounting information Systems. 2. Kolmogorov-Smirnov one-sample tests, using the sample mean and standard deviation as the reference, were used to test for normality. The assumption was denied for the ‘experience’ covariate (P < OU~l) but not for either the independent variable (P = 0.82) or the ‘ability’ covariate (P = 0.47). The result for the ‘expenencc covariate is most likely of limited importance since its explanatory power was essentially zero. 3. The statistical importance of the contrasts can be approached in two ways. The first is only to examine the contrasts if the factor meets the desired level of statistical significance. The second is to essentially ignore the statistical significance of the factor and to concentrate on the contrasts individually. Opinion, we believe, is equally divided on the appropriateness of these two approaches. WC have chosen to adopt the latter approach.