Creative Education
2012. Vol.3, Special Issue, 923-930
Published Online October 2012 in SciRes (
Copyright © 2012 SciRes. 923
Validity, Reliability and Equivalence of Parallel Examinations
in a University Setting*
Bunmi S. Malau-Aduli, Justin Walls, Craig Zimitat
School of Medicine, University of Tasmania, Hobart, Australia
Received July 9th, 2012; revised August 10th, 2 01 2 ; ac c ep te d A ug us t 2 2nd, 2012
A key issue to address in the design and implementation of any assessment system is ensuring its reliabil-
ity and validity. University assessment policies often require staff to prepare parallel examinations for
students who are unable to sit the initial examination. There is little published literature to give confidence
to staff or students that these examinations are indeed reliable or equivalent. This study was conducted to
determine the validity, reliability and equivalence of two parallel examinations that have been developed
under highly defined quality assurance (QA) processes in a university setting. Collated assessment results
for all the 76 participants who sat the parallel examinations were subjected to statistical and correlational
analysis to test for significant differences between mean scores and their associated standard deviations.
Item analysis was conducted for each assessment by computing the difficulty index (DIF), discrimination
index (DI) and Kuder-Richardson 20 (KR-20) reliability using classical test theory. Results indicated
comparative proportions of difficulty, functional distractors and internal consistency of the assessment
items on both examinations. Comparison of student performances in both examinations revealed that
there was no significant difference in mean scores. However, a highly positive and significant correlation
(r = 0.82) between student total scores in both examinations was evident. Approximately two thirds (62.5
%) of students with low scores in the first examination also achieved low scores in the second examina-
tion. Furthermore, two thirds of the students were ranked in the same order based on performance in both
examinations. The established QA processes for assessment in the school provided a strong basis for the
generation of multiple sources of data to support arguments for the validity of examinations. It is possible
to develop valid, reliable and equivalent parallel tests in university settings with the presence of
well-defined QA processes.
Keywords: Parallel Examinations; Quality Assurance; Assessment
Universities place considerable emphasis on the development
of policies and guidelines that govern examination processes.
Well documented policies, strategies and processes, blueprint-
ing to facilitate adequate sampling, feedback to students and
assessors and evaluation of the overall process are important
elements of any assessment enterprise, however Fowell et al.
(1999) argue that insufficient attention is paid to the evaluation
of assessment. The latter can be usefully elaborated to include
psychometric or statistical analysis of components of the ex-
amination or items, establishment of measures of reliability and
benchmarking to review pass/fail standards for the examination.
Kane (2006) and Schuwirth et al. (2011) identify key questions
in support of validity arguments, and highlight the importance
of validity data in supporting consistent decision making as a
result of assessment. The collation of evaluation data is neces-
sary to provide supporting evidence, and hence confidence in
the inferences that will be drawn from assessment in higher
education (Kane, 2006). In this case study, we demonstrate the
value of quality assurance processes in the generation of evi-
dence to support validity of assessment activities in a medical
In Australian universities, assessment policies hold to tenets
of reliability, validity and fairness. Fairness includes notions of
reliability, validity, transparency and ethical decision making,
but it also means that students should be given equal opportu-
nity to demonstrate their learning, free of any disadvantage
through identification, language, disability or illness. In the
latter case, assessors prepare two or three equivalent versions of
each examination (i.e. parallel forms of the test (Tavakol &
Dennick, 2011)—the ordinary examination (OE) that most
students sit, a deferred ordinary examination (DO) for students
who were ill or unable to sit the ordinary examination, and a
supplementary (Supp) examination for students with borderline
scores. (The names vary across Australian medical courses). It
is assumed that each of these examinations is valid, reliable and
equivalent (Norcini et al., 2011), although there is usually in-
sufficient psychometric or statistical data to support such as-
sumptions. This paper aims to provide evidence of the validity,
reliability and equivalence of such parallel examinations/tests.
Contemporary assessment theory considers the primacy of
construct validity, which draws upon theory and evidence to
give meaning to assessment. Typically evidence for validity is
drawn from five areas to support confidence in the inferences
made from assessment: curriculum content; data management;
statistical analyses of test data; correlational analyses; and ef-
fects of assessment (Kane, 2006; Downing & Haladyna, 2009).
These are not mutually exclusive evidence categories. The spe-
*Declaration of Interest: The a u t h ors report no conflicts of interest.
cific mix of evidence needed for validation depends on the
inferences being drawn and the assumptions being made (Mes-
sick, 1989), and extends beyond the validity of the assessment
instruments that generate test score data. As assessment re-
gimes become more complex and the stakes related to assess-
ment outcomes increase, the greater the need for multiple
sources of data to support the validity of assessment. Our as-
sessment quality assurance (QA) processes were conceptualised
in line with current unitary validity theory (Kane, 2006; Down-
ing & Haladyna, 2009) to generate evidence for medium stakes
examinations. The requirement for validity evidence for as-
sessment through the early years of our medical course is mod-
est; however it peaks at the preclinical-clinical transition and
graduating examinations.
Academic staff is typically focused on writing questions
from their discipline, usually at the expense of the “bigger pic-
ture” and question quality. Curriculum content evidence for
validity relates to the selection of assessment instruments,
alignment of assessment tasks/items with intended learning
outcomes, sampling of items across domains of the curriculum,
examiner training and the quality of test items. Blueprinting
serves to guide the selection of specific assessment instruments,
strategies, and more importantly, their development through the
specification of the content to be assessed (Hamdy, 2006).
Blueprinting mitigates against two significant threats to validity,
“construct under-representation” (CU), the biased or under-
sampling of course content and “construct irrelevance” (CI). CI
may arise from a “systematic” error as a result of the poor
choice of assessment instrument for the outcomes being as-
sessed (Downing, 2002). But it may also affect a small propor-
tion of students if it arises from poor training of assessors and
role player/standardised patients such that students are not ex-
posed to the same test stimuli (e.g. at different sites) or assessed
in the same manner. In this case study, each of the examina-
tions were blueprinted to ensure representative and consistent
sampling across content domains and alignment with learning
outcomes (Jozefowicz et al., 2002; Hamdy, 2006; Hays, 2008),
assessors were trained, and internal peer review processes
(Malau-Aduli & Zimitat, 2011) were used to evaluate coverage
and to refine test items.
Ensuring the integrity of data arising from the administration
of assessment is key for any validity argument. In the first in-
stance, identity and fraud management tag assessment data to
the correct individual. The use of double data entry methods
and optical scanning forms with checking by software algo-
rithms improves the accuracy of data transfers. Software was
developed to automate some processes, particularly those re-
lated to generating psychometric reports and student feedback
increasing efficiency and decreasing chances for human error to
affect data management. The introduction of quality assurance
processes, assessment training manuals and automated report-
ing have all provided significant improvement in consistency of
data handling and greater confidence in systems.
Statistical and correlational analyses of assessment data pro-
vide important evidence to support or refute validity claims.
Item analyses—difficulty, discrimination and internal consis-
tency of the test, inter-rater reliability etc.—are routinely per-
formed as part of quality assurance processes to provide indices
of reliability (Tavakol & Dennick, 2011). Reliability refers to
the stability of test scores upon re-testing of examinees which is
a fundamental requirement for making meaningful interpreta-
tions of test data. Correlational validity evidence may be used
to assert positive relationships between performance on two
tests of similar abilities (e.g. as students progress through the
course), or conversely negative correlations between scores on
tests of different abilities. High stakes examinations have high
reliability thresholds, whereas it is moderate for many medical
course examinations except the final clinical examinations. In
our case study, statistical and correlational validity evidence are
routinely generated by the QA process.
The final type of data for validity evidence is drawn from the
decision making process and its consequences for examinees
and others. The documentation of standard setting processes,
standard error of measurement associated with cut scores and
use of coded candidate details all contribute to this evidence.
However it can also extend to correlation of assessment out-
comes with later assessments (specialty examinations) or ele-
ments of professional practice. Benchmarking of assessment
processes and graduate outcomes are more recent types of evi-
dence collected to support assertions of course quality as well
as assessment. Some of this data can be collected immediately,
and is part of our quality assurance processes, whereas data
relating to postgraduate activity falls within the realm of course
Context of the Case Study
The Tasmanian School of Medicine (TSoM) offers a five-
year case-based undergraduate medical degree. Vertical inte-
gration of the curriculum is promoted through a thematic struc-
ture usage in all the five years of the program. The first two
years of the course provide a systems-based introduction to the
foundations of medicine, with an early opportunity to develop
communication and clinical skills. Assessment involves forma-
tive and summative elements, with an emphasis on end of se-
mester examinations. Well-developed QA processes around
assessment (Malau-Aduli et al., 2011) were implemented at the
TSoM in 2009 by the Medical Education Unit (MEU). These
processes included blueprinting of educational objectives, se-
lecting appropriate test formats and applying assessment strate-
gies to achieve adequate levels of reliability. They also included
the implementation of appropriate standard-setting, assessor
and role-player training, decision-making procedures and peer
review of assessment items to minimise item writing flaws
prior to being administered to students (Malau-Aduli et al.
This case study refers to an examination at the end of the
second year of the medical course. QA processes were followed
and the three parallel written examination papers (OE, DO and
Supp) were developed by faculty at the same time. The univer-
sity central Examination Office set up new processes to facili-
tate automated printing of examination papers at the same time
as the TSoM established the new QA processes around assess-
ment. The independent and simultaneous introduction of the
two new systems in the TSoM and the Examination Office
resulted in the automated printing of the examination papers
(OE) with answers on them. The examination was re-adminis-
tered to all students using the DO paper. Students were also
offered the opportunity to “resit” the examination in 3 days,
using the Supp paper. On the basis of fairness, their result was
based upon the highest score achieved on either examination.
This rare occasion of administering a “repeat” examination to
the same cohort of students provided an ideal opportunity to
evaluate the School’s assessment practice, using parallel forms
Copyright © 2012 SciRes.
reliability estimates. Therefore, the objective of this paper is to
evaluate the validity, reliability and equivalence of these paral-
lel tests that were developed under well-defined quality assur-
ance (QA) processes.
Participants and Procedures
Second year medical students (N = 114) sitting an integrated
basic and clinical science (OE) examination were inadvertently
administered a MCQ examination paper (OE) which included
answers. The examination was re-administered to all students,
using the parallel (DO) paper. However, only seventy-six (76)
out of the one hundred and fourteen (114) students in the cohort
sat the equivalent examination (Supp) three days later. The
scores of these 76 students from the dataset were used for
evaluation of these examinations. Clearance was obtained from
the relevant ethics committee for this study.
Data Analysis
Collated assessment results for all the 76 participants who sat
both examinations were subjected to statistical analysis using
general linear model (GLM) procedure in SAS (SAS, 2009) in
a multivariate least squares analysis of variance to test for sig-
nificant differences between mean scores, their associated
standard deviations and descriptive statistics of all the variables.
Student scores were compared in the parallel examinations.
Significance at the 5% level was established using the least
significant difference technique, while Duncan’s multiple range
tests was used for mean separation where significant differ-
ences were detected. Item analysis was conducted for each
assessment by computing the difficulty index (DIF), discrimi-
nation index (DI) and Kuder-Richardson 20 (KR-20) reliability
using the classical test theory as provided in IDEAL 4.1, an
Item Analysis Program (Precht et al., 2003). Details of the qual-
ity criteria chosen for each of these quality indicators have been
described previously (Malau-Aduli & Zimitat, 2011). Means of
item difficulty, discrimination index and number of functioning
distracters per item for all the MCQs were also computed.
Comparative Analysis of the Two Examinations
Descriptive statistics for the MCQ examinations are por-
trayed in Table 1. A comparative appraisal of student perform-
ances in both examinations revealed no significant difference in
mean scores (Table 1). However, there was a highly significant
(p < 0.001) and positive correlation (r = 0.82) between total
student scores in both examinations (not shown). Although the
differences were not statistically significant, absolute mean
scores were observed to be higher in the first examination than
in the second examination (36.4 vs. 33.8). However, there were
higher minimum and maximum scores in the second examina-
tion (19.0 vs. 14.0 and 46.0 vs. 45.0, respectively). The first
examination recorded a higher reliability index compared to the
second examination (0.71 vs. 0.68). Similar trends were ob-
served in student performances in the different disciplines as-
sessed in both examinations.
Significantly higher (p < 0.01) mean scores were achieved in
Pathology and Pharmacology (75.34% vs. 75.58%; 69.91% vs.
Table 1.
Descriptive statistics for both examinations.
Criteria First Exam Second Exam
Number of Items in Exam 50 50
Number of Examinees 76 76
Mean Score 36.4 33.2
Minimum Score 14.0 19.0
Maximum S core 45.0 46.0
Standard D eviation 5.3 4.8
Reliabilit y Index 0.71 0.68
70.88% respectively) compared to the other assessed disciplines
in both examinations (Figure 1). Students performed somewhat
better in the first set of Biochemistry questions than the second
and vice versa in the Gross Anatomy questions.
In accordance with the University regulations, the pass mark
for each examination was set at 50%. Individual student per-
formances revealed that one student failed both examinations
with 30.27% in the first examination and 37.57% in the second
examination. Approximately two thirds (62%) of the students
with low scores in the first examination also achieved low
scores in the second examination. Figure 2 shows the linear
regression of student scores in the parallel examinations. The
coefficient of determination (R2 = 0.67) is indicative of the
precision accuracy that explains 67% of the observed variation
between students’ scores in both examinations. Two thirds of
the students were ranked in the same order based on perform-
ance in both examinations. Compared with performance on the
first examination, 5% of students ranked higher on the second
examination, while 30% ranked lower in the second exam
(Figure 3).
Item Analysis
Table 2 shows the item analysis results for both examina-
tions. Mean difficulty of the test items on the two examinations
was similar (74% vs. 67%). On the first examination, twenty-
four items lay outside the reference range for difficulty level
(DIF): all the 24 items appeared to be too easy. Seventeen items
on this examination showed very low discrimination (0 - 0.15)
between students who achieved scores in the highest and lowest
quartiles. Sixty six percent (n = 33) of the items had signifi-
cantly high discrimination indices with a mean discrimination
index of 0.26. One third of the items on this examination had
distractors that were not functioning effectively in their role.
The reliability coefficient (KR-20), which is a measure of the
internal consistency of the test, was 0.71.
On the second examination, seventeen items lay outside the
reference range for DIF; sixteen of the items appeared to be too
easy, whilst one appeared too difficult. Thirteen items on this
examination showed very low discrimination between students
who achieved scores in the highest and lowest quartiles. Sev-
enty percent (n = 35) of the items had high discrimination indi-
ces with a mean discrimination index of 0.21. One third of the
items on this examination had distractors which were not func-
tioning effectively in their role. The reliability coefficient
(KR-20) was 0.68.
Distractor analyses were completed for the test items on both
Copyright © 2012 SciRes. 925
Copyright © 2012 SciRes.
Figure 1.
Comparison of students’ performances in both ex aminations across the different disciplines.
Figure 2.
Comparison of students’ performances in both ex aminations across the different disciplines.
examinations to identify non-functional distractors within each
MCQ item. Two hundred distractors associated with the fifty
(50) MCQs were assessed in each of the two examinations.
Similar patterns were observed in both examinations. In the
first examination, 66% (n = 132) of the distractors were func-
tional in comparison to 65% (n = 130) in the second examina-
tion. In the first examination, 9.5% of the distractors were not
chosen by any examinee (i.e. the answer key was obvious)
compared to 10.5% in the second examination (Table 3). The
mean number of functioning dstractors per item was 2.64 in i
Figure 3.
Change in decile ranking of students in both examinations.
Table 2.
Item analysi s for both examin at io n s.
Criteria First Exam Second Exam
Number of It ems in Examination 50.00 50.00
Number of Examinees 76 76
Mean Difficulty % ( S D) 74.28 (18.05) 66.46 (20.77)
Mean Discrimination Index (SD) 0.26 (0.1 5) 0.21 (0.1 3)
Reliabilit y Index 0.71 0.68
Easy Items (%) 24 (48) 16 (32)
Difficult Items (%) 0 (0) 1 (2)
Items with Negative
Discrimination Indices (%) 0 (0) 0 (0)
Items with Ze ro
Discrimination Indices (%) 0 (0) 2 (4)
Items with Low
Discrimination indices (%) 17 (34) 13 (26)
Total No of Discrim i nating
Items (%) 33 (66) 35 (70)
the first examination and 2.60 in the second examination. There
was a similar pattern in the distribution of functioning distrac-
tors per item in both examinations, with an increase in the per-
centage of items with two and three functional distractors (28%
& 16% for the first examination; 34% & 12% for the second
examination. In the first examination, there were 28% of items
with one functional distractor and 8% of items with four func-
tional distractors vs. 24% and 4% respectively in the second
Table 3.
Distractor analysis for both examinations.
Criteria First Exam Second Exam
Number of It ems in examination 50 50
No of distractors assessed 200 200
Distractors with frequ ency = 0% n (% ) 19 (9.5) 21 (10.5)
Distractors with frequ ency < 5% n (% ) 49 (24.5) 49 (24.5)
Functioning distrac tors per test n (%) 132 (66) 130 (65)
Functioning distractors per item M (SD) 2.64 (1.21) 2.6 (1.14)
Functioning distractors per item n (%)
None 10 (20) 6 (12)
One 14 (28) 12 (24)
Two 14 (28) 17 (34)
Three 8 (16) 6 (12)
Four 4 (8) 2 (4)
This case study illustrates the value of QA processes in the
generation of validity evidence for parallel forms tests typically
used in university assessment. These processes generated qual-
itative and quantitative data in the areas of curriculum content,
data management, and statistical and correlational analyses in
the face of a major incident affecting our examinations. Since
the QA processes were applied in the development of all the
test items, we have drawn upon data generated through QA
processes to test that assertion and the validity of the two paral-
lel examinations (DO, Supp).
Copyright © 2012 SciRes. 927
The QA processes provided qualitative evidence to support
argument for curriculum content validity. Developing, blue-
printing and editing all the test items for both examinations at
the same time ensured that the test items on both examinations
covered similar content domains and were aligned to the learn-
ing outcomes underscoring the reports by Jozefowicz et al.
(2002), Hamdy (2006), Malau-Aduli & Zimitat (2011). This is
indicated in the similar overall mean scores and disciplines/
subcategories scores obtained by students in both examinations.
The comparative disciplines/subcategories results also echo
equal levels of quality and difficulty of the questions in both
examinations. The high correlation between the mean scores on
both examinations (r = 0.82) indicates that the sets of test items
measured the same content area/construct. Results from this
study show the comparative proportions of easy, difficult, recall,
non-discriminating and non-functional items in both examina-
tions. The observed difference in scores in Biochemistry and
Gross Anatomy may be a reflection of how students studied for
the second examination (Supp), after the experience of sitting
the first examination (DO). In this case, routine statistical data
arising from QA processes also support the content equivalence
of the two examinations. Data management for the two exami-
nations was undertaken according to the QA process with
mechanisms such as key validation, double entry and accuracy
of scores.
Statistical and correlational analyses of examination data are
routinely conducted to provide evidence of reliability as part of
QA processes. The observed high reliability indic es and simi lar
student ranking in both examinations indicate that with the
second examination, examinees obtained similar scores on re-
testing (Supp examination) as they did on the first (DO exami-
nation). The summary statistics (item analysis) indicated similar
trends in performance prompts, discrimination indices, and
functionality of distractors and internal consistency reliability
of both examinations. Reliability coefficients allow the quanti-
fication and estimation of the random errors of measurement in
assessment (Downing, 2004). The resulting high prediction
accuracy (67%), and correlation coefficient (0.82) in the com-
parison of both examinations in this study is an indication of
the convergence of validity evidence (Downing, 2003). This
indicates that the assessment items measured the same abilities,
establishing some commonalities between the constructs as-
sessed in both examinations. The high correlation between
student scores in both examinations confirms that the test items
in both examinations measured the same construct/content ar-
eas derived from blueprints. The students obtained similar
scores (and in seven instances, the same scores) on retesting as
they received the first time. These data confirm that with the
implementation of QA processes, it is possible to generate
equivalent examinations that reproduce test scores with a high
level of certainty (Downing & Haladyna, 2009).
Consequential validity evidence relates to the impact of as-
sessment on teaching and learning (Downing & Haladyna,
1997). The reproducibility of the pass-fail decision is also a
very important source of validity evidence (Downing, 2003;
Downing & Haladyna, 2009). The parallel examinations have
reproduced assessment outcomes for students with a high level
of certainty as both examinations identified the same poorly
achieving student, who failed in both examinations and about
two thirds (62%) of the low achieving students in the first ex-
amination, also scored poorly in the second examination. Al-
though there was an administrative error, the results of the par-
allel examinations and the outcomes of the assessment on stu-
dent scores have indicated no adverse consequences for the
students. The examinations are equivalent in this sense. This
suggests that, under the current assessment regime, the School
should have confidence in the decision to allow students to
“resit” the examination and achieve the “best score” based upon
performance on either examination.
The QA processes developed by the School were initially
focused on managing internal validity threats. The University
typically manages external validity threats through Examination
Office processes. On this occasion, the problem arose at the
School/Examinations Office interface—a communication fail-
ure. A debriefing with staff identified further communications
concerns, and subsequently an Accountability Matrix (Appen-
dix 1) was developed to provide greater clarity about roles and
responsibilities internally, and formalised relationships with the
Examinations Office. There was also an opportunity to consider
emergent sources of error; as a result training for the use of
optical mark recognition (OMR) scanner was introduced for
new staff and new software flags were developed to automati-
cally check accuracy of data entry. Clearly potential sources of
error and new threats for validity arise all the time, and QA
processes need to be reviewed regularly.
The QA processes generate significant volume of data, but
how much is needed for a moderate stakes examination? Schu-
wirth et al. (2011) suggest that three major inferences are re-
quired to quantify the consistency of an assessment instrument,
as well as provide validity evidence for the observed scores.
These inferences are: 1) would the students obtain the same
score on the parallel test as they did on the actual test? 2) would
the students take the same place in the rank ordering from best
to worst performing student on the parallel test as they did on
the actual test? 3) would the students obtain the same pass-fail
decisions as they did on the actual test? The high positive cor-
relation (r = 0.82) and the 67% precision accuracy of the ob-
served variation between student scores in both examinations
confirm that majority of the students have obtained similar
scores (seven of them obtained exactly the same score in both
examinations) in the second examination as they did in the first
examination. Most of the students (65%) have also taken the
same place in the decile ranking order from best to worst per-
forming student on both examinations. The students have also
obtained the same pass/fail decisions, with the same student
failing in both examinations. On this basis, there is strong evi-
dence in support of the validity, reliability and equivalence of
the two examinations and that no group of students has been
advantaged in this process.
Caution should be taken in any generalisations drawn from
this study. Different institutions have different policies and
guidelines and QA processes for assessment which may affect
the development of “equivalent” examinations for students. The
group of students “resitting” the examination may not have
been representative of the whole cohort which could make a
difference to the outcomes of the study. Not all sources of error
may have been identified and accounted for in this study,
though we believe sufficient evidence has been marshaled in
support of our conclusions about a medium stakes examination.
Medical educators need to devote more time to evaluating
their assessment regimes to generate strong evidence of validity
Copyright © 2012 SciRes.
Copyright © 2012 SciRes. 929
so that resulting data and grades are defensible. Statistical and
correlational data in this case study support the view that well-
defined QA processes reduce the threats to validity of assess-
ment. The establishment of QA processes in the development of
examinations can ensure content representativeness of the test
materials, the reproducibility and generalisability of the scores,
the statistical characteristics of the assessment questions and
consistency of pass-fail decisions made from the assessment
scores. Developing parallel examinations to address notions of
fairness in university assessment policies is possible when
strong QA is in place, and does not appear to advantage any
group of students. The detection of errors and validity threats
and revision of QA processes should be an ongoing activity.
The authors thank Dr Lisa Foa (the Unit Co-ordinator) and
all the teaching staff who participated in developing the as-
sessment items.
Downing, S. M. (2002). Threats to the validity of locally developed
multiple-choice tests in medical education: Construct-irrelevance
variance and construct under-representation. Advances in Health Sci-
ences Education, 7, 235-241. doi:10.1023/A:1021112514626
Downing, S. M. (2003). Validity: On the meaningful interpretation of
assessment data. Medical Education, 37, 830-837.
Downing, S. M. (2004). Reliability: On the reproducibility of assess-
ment data. Medical Education, 38, 1006-1012.
Downing, S. M., & Haladyna, T. M. (1997). Test item development:
Validity evidence from quality assurance processes. Applied Meas-
urement in Education, 10, 61-82. doi:10.1207/s15324818ame1001_4
Downing, S. M., & Haladyna, T. M. (2009). Validity and its threats. In
S. M. Downing, & R. Yudkowsky (Eds.), Assessment in health pro-
fessions education (pp. 2 1-55). London: R o ut l ed g e .
Fowell, S. L., Southgate, L. J., & Bligh, J. G. (1999). Evaluating as-
sessment: The missing link? Medical Education, 33, 276-281.
Hamdy, H. (2006). Blueprinting for the assessment of health profess-
sionals. The Clinical Teacher, 3, 175-179.
Hays, R. (2008). Assessment in medical education: Roles for clinical
medical educators. The Clinical Teacher, 5, 23-27.
Jozefowicz, R. F., Koeppen, B. M., Case, S. M., Galbraith, R., Swanson,
D. B., & Glew, R. H. (2002). The quality of in-house medical school
examinations. Academic Medicine, 77, 156-161.
Kane, M. (2006). Content-related validity evidence in test development.
In S. M. Downing, & T. M. Haladyna (Eds.), Handbook of tes t devel-
opment (pp. 131-153). Mahwah, NJ: Lawrence Erlbaum Associates.
Malau-Aduli, B. S., Zimitat, C., & Malau-Aduli, A. E. O. (2011). Qual-
ity assured assessment processes: Evaluating staff response to change.
Journal of Higher Education Management & Policy, 23, 1-23.
Malau-Aduli, B. S., & Zimitat, C. (2011). Peer review improves the
quality of MCQ examinations. Assessment & Evaluation in Higher
Education, 34, 1-13. doi:10.1080/02602938.2011.586991
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational meas-
urement (3rd ed., pp. 13-104). New York: American Council on
Education and Macmillan.
Norcini, J., Anders on, B., Bollela, V., Burch, V., Costa, M. J., Duviv ier,
R., Galbraith, R., Hays, R., Kent, A., Perrott, V., & Roberts, T.
(2011). Criteria for good assessment: Consensus statement and rec-
ommendations from the Ottawa 2010 Conference. Medical Teacher,
33, 206-214. doi:10.3109/0142159X.2011.551559
Precht, D., Hazlett, C., Yip, S., & Nicholls, J. (2003). Item analysis
user’s guide. Hong Kong: International Database for Enhanced As-
sessments and Learning (ID E A LHK).
SAS (2009). Statistical Analysis System Institute, North Carolina USA
Schuwirth, L., Colliver, J., Gruppen, L., Kreiter, C., Mennin, S., Onishi,
H., Pangaro, L., Ringsted, C., Swanson, D., Van der Vleuten, C. P.
M., & Wagner-Menghin, M. (2011). Research in assessment: Con-
sensus statement and recommendations from Ottawa 2010 Confer-
ence. Medical Teacher, 33, 224-233.
Tavakol, M., & Dennick, R. (2011). Post examination analysis of ob-
jective tests. Medical Teacher, 33, 447-458.
Appendix 1.
Accountability Matrix for Written Examinations.
This matrix defines the tasks to be completed for the development of written exam papers in the MBBS (ordinary, deferred ordinary and supplemen-
tary exam papers). It denotes the person accountable for specific actions (1); those involved in the decision making processes (2) and those who will
be inform ed of the outcomes o f decisions (3).
TASK Unit Coordinator MEU PDA
Assessment timelines and Excel workboo k for entry of all assessment results 2 1 3
Blueprints and weightings of questions for each ass essment 1 2 3
Initial Contacting of qu estion writers 1 2 3
Follow-up with quest ion writers 2 1 3
First draft o f questions prepared 2 1 3
Format Questions 3 2 1
First draft of exam paper in KB 3 1 3
First check of questi on s 1 2 3
Peer revie w of questions 1 2 3
Final draft of exam pape r 2 1 3
Format exa m paper 3 2 1
Final exam paper in KB for QA 3 1 2
Sign-off on exam paper 1 3 3
PDF copy of exam pape r t o Exams Office—InSite/print locally 3 2 1
Shading of correct r esponse on MCQ Answer Sheet 2 2 1
Collection and collation of exam papers 3 3 1
Scanning of M CQ answer sheets 3 2 1
SAQs to examiners for marking 2 3 1
Entry of studen t s’ results into excel workboo k 2 2 1
Sign-off on results 1 2 3
Results to exams office 2 3 1
Feedback to students & staff 2 1 3
Note: 1 = Acc ountable; 2 = D e cision making team; 3 = Information network; MEU = Med ical Educati on Unit; PDA = Program delivery and asse ssment te am.
1. Evaluation of assessment is a neglected area of academic practice.
2. The use of well-defined quality assurance (QA) processes in the development of assessment items/examinations in medical education contributes to the
generation of data in support o f v alidity arguments for assessment.
3. The use o f QA processes contributes positively to giving conf idence f o r t h e validity, reliability and fairness of parallel examinations in university settings.
4. New threats to validity of assessment arise continuall y, and need ongoing monitoring and management.
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