We have prepared this article to
help Medical PG aspirants understand ‘Normalization’ and what can be done to
counter stress arising from it. Many people offers explanation about this but
it is way too technical and complicated for majority of students, so we have
tried to make it as easy as possible to understand.
Just last year paper-pencil test was
the method for conducting exams like AIPMEE and AIIMS, when the same or a
similar test papers (with reordering of the questions) were given to all test
takers and it was relatively easier to compare student’s performance as they
only had to create a merit list of scores of students. With the introduction of
an online exam by MCI which is conducted by NBE (as of now), the test and the
questions vary across different test days and slots. Hence normalization is
needed – scaling up/down of scores using psychometrics, statistics and test
takers’ responses to a standard set of questions. This helps evaluate students
on a common standard and is necessary because the difficulty level of the test
varies across test days.
Why is normalization
needed?
Let us consider a simple
hypothetical scenario: Assume one student is attempting NEET this year along
with a friend. Both of us are equally good in medicine. But while my strength
lies in Anatomy, his strength is Biochemistry. Since we gave the exams on
different days and slots our test papers were different. We both got
considerable questions from our respective strong areas and performed equally
well. But does that mean that we are equally good? No. Say on a scale of 1-5, I
got questions of difficulty level 4 from Anatomy. Being my strong area I did
well. On the other hand he got questions of difficulty level 5 from
Biochemistry. Now the only way to benchmark his and my performance is by
looking at the relative scores of people who gave the test with us. In my slot
out of 80/100 students were able to work out the questions from Anatomy. On the
other hand, in my friend’s slot only 50/100 students were able to work out
questions from Biochemistry. That means when we take a large enough sample size
(students), my friend’s performance is much better than average as compared to
mine (even when we are scoring equally well).
Personal choices that bring in
subjectivity – a student might not like anatomy at all but might be strong in
Physiology or Pharmacology. This is countered by taking a large sample size.
Also there is something called ‘an
equating block’. Now there are questions in papers which are same in many
papers which are called ‘an equating block’. Now suppose there are 24 questions
out of 240 in a paper which are same in papers of many days and slots. If I
answer all 24 of them correctly, when most of the students have answered only
say 20 on average then psychometric analysis will consider my high score as
superior compared to other students. So for example I have answered 200/240
questions correctly my raw score will be same as all the students who scored
200 (as there is +1 for right answer and +0 for wrong) with same number of
wrong answers. But my scaled score will be higher.
Also let us consider ‘no negative
marking’ scenario. If anyone attempted 240 and he/she got 200 right and there
is a student who attempted 230 but he/she also got 200 right then his
percentile score will be put above mine as NBE has said in their TIE – BREAKER
CRITERIA that ‘In the event of two or more candidates obtaining same
percentile, the merit position shall be determined by the number of wrong
responses of such candidates. Candidate with less number of wrong responses
shall be placed at higher merit.’
Also as another example if a
candidate scores 200/240 in a paper in which all students performed poorly in
equating block of questions (hence a difficult paper) will be placed higher
than candidate scoring 200/240 in a paper in which all candidates have
performed well in equating block of questions (hence an easy paper).
So conclusion is that a student who
answers more number of questions correctly, scores higher in equating block of
questions and also has less number of wrong answer will be put above all
candidates.
Now lot of candidates where
complaining after the results that their score was much lower than what it
should have been due to normalization. It has also led to widespread debate
about whether the technique is even reliable.
How to deal with normalization then?
Since normalization or some other form of standardization is here to stay as
even AIIMS is going to conduct online exams, it is important that we make peace
with the idea and try to deal with it. My advice is to ignore the entire
concept of normalization. And we have good reason for the same: one cannot
really predict how others in your same slot are going to perform in the same
test (over 3000 students had taken NEET in a single slot). You cannot even
predict whether the question you have attempted/left are going to be branded as
easy or difficult based on statistics – in short, there is no way to know
whether a question is that ‘equating block’ question that one needs to attempt
and get right at all costs. Therefore it makes sense to attempt as many
questions as possible just the same way as one would have done in a normal
exam. Yes there might be some loophole with the laws of statistics, but then
which law in this cosmos is without its own set of cracks and glitches.
Do not link your mock test scores to
NEET scores and blame normalization – dip in scores can be due to exam stress
too. There is no point in shooting arrows in the dark and creating unnecessary
anxiety unless you want to pin the blame of your performance on normalization.
Give it your best shot and forget the rest.
The other point to note is that the
sample size taken for standardization in this case is very large (over 0.9 Lac
students giving NEET) – thus low chances of statistical selection error.
Also these king of normalization is
done is most international level competitive exams like GMAT and MBA exams like
CAT. MBA aspirants have been complaining about it since years but it hasn’t
helped scraping the exam.
So just work hard and forget about
the rest.
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