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 offer
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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