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Negative marking
     has two advantages:

1) it yields scores that are
   not inflated by guesswork,

2) it discourages random
   guesses, and therefore
   the scores are more reliable.
A good test should yield a score that is
   proportional to each test taker's knowledge,
comprehension, or aptitude; whatever it is the test
           has been designed to assess.
In traditional multiple-choice tests where there is no
negative marking, test takers will normally make a guess
whenever they are unable to identify the correct answer
                       to a question.
If each question has four answer options, then the
 expected score for a test taker who has zero knowledge,
comprehension, or aptitude will be 25%. This is an average
 score, since the test taker's actual score will be variable.
A test taker who knows the answers to 20% of the
questions has an expected score of 40%, since on average
  they will guess the right answers to a quarter of the
             remaining 80% of the questions.
Let's look at it another
way.

Consider a collection of    50 Qs   (3 marks per Q)
50 test questions.
                             ✔
Suppose that a particular   10 Qs
test taker knows the
right answer to the first
10 questions, and that
they make random
guesses for the remaining    ?
40 questions.               40 Qs
If each question is worth
3 marks, the test taker
will get 30 marks for the
first 10 questions.          50 Qs   (3 marks per Q)

(You'll soon see why we      ✔
need to have 3 marks per    10 Qs          10 x 3 = 30 marks
question rather than just
one mark.)


                             ?
                            40 Qs
If they make random
guesses for the remaining
40 questions then on
average they will get 10    50 Qs   (3 marks per Q)
of them right, which will
give them another 30         ✔
marks.
                            10 Qs          10 x 3 = 30 marks




                             ?
                            40 Qs          10 x 3 = 30 marks
This will give them 60
marks altogether, on
average, out of the 150
marks that are available.    50 Qs   (3 marks per Q)

So their expected score       ✔
is 60 out of 150, which is   10 Qs          10 x 3 = 30 marks
2/5, or 40%.

                                            Score = 60 out of 150
                                          (expected)        = 40%
                              ?
                             40 Qs          10 x 3 = 30 marks
If we deduct one mark
for every question
answered incorrectly, this
will cancel out the marks    50 Qs   (3 marks or -1 mark per Q)
due to lucky guesses.
                              ✔
The idea is that for each    10 Qs          10 x 3 = 30 marks
lucky guess that adds 3
marks to the score there
will be (on average) 3                      Score = 30 out of 150
                                          (expected)        = 20%
unlucky guesses that
each deducts one mark         ?
from the score.
                             40 Qs          10 x 3 = 30 marks

So the expected score is                    30 x -1 = -30 marks
now 20%, which is in line
with the profile of a
"good" test (which we
saw in the second slide).
To conclude, here’s the first slide again...

        Negative marking
       has two advantages:
1) it yields scores that are
   not inflated by guesswork,
2) it discourages random
   guesses, and therefore
   the scores are more reliable.

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Negative marking

  • 1. Negative marking has two advantages: 1) it yields scores that are not inflated by guesswork, 2) it discourages random guesses, and therefore the scores are more reliable.
  • 2. A good test should yield a score that is proportional to each test taker's knowledge, comprehension, or aptitude; whatever it is the test has been designed to assess.
  • 3. In traditional multiple-choice tests where there is no negative marking, test takers will normally make a guess whenever they are unable to identify the correct answer to a question.
  • 4. If each question has four answer options, then the expected score for a test taker who has zero knowledge, comprehension, or aptitude will be 25%. This is an average score, since the test taker's actual score will be variable.
  • 5. A test taker who knows the answers to 20% of the questions has an expected score of 40%, since on average they will guess the right answers to a quarter of the remaining 80% of the questions.
  • 6. Let's look at it another way. Consider a collection of 50 Qs (3 marks per Q) 50 test questions. ✔ Suppose that a particular 10 Qs test taker knows the right answer to the first 10 questions, and that they make random guesses for the remaining ? 40 questions. 40 Qs
  • 7. If each question is worth 3 marks, the test taker will get 30 marks for the first 10 questions. 50 Qs (3 marks per Q) (You'll soon see why we ✔ need to have 3 marks per 10 Qs 10 x 3 = 30 marks question rather than just one mark.) ? 40 Qs
  • 8. If they make random guesses for the remaining 40 questions then on average they will get 10 50 Qs (3 marks per Q) of them right, which will give them another 30 ✔ marks. 10 Qs 10 x 3 = 30 marks ? 40 Qs 10 x 3 = 30 marks
  • 9. This will give them 60 marks altogether, on average, out of the 150 marks that are available. 50 Qs (3 marks per Q) So their expected score ✔ is 60 out of 150, which is 10 Qs 10 x 3 = 30 marks 2/5, or 40%. Score = 60 out of 150 (expected) = 40% ? 40 Qs 10 x 3 = 30 marks
  • 10. If we deduct one mark for every question answered incorrectly, this will cancel out the marks 50 Qs (3 marks or -1 mark per Q) due to lucky guesses. ✔ The idea is that for each 10 Qs 10 x 3 = 30 marks lucky guess that adds 3 marks to the score there will be (on average) 3 Score = 30 out of 150 (expected) = 20% unlucky guesses that each deducts one mark ? from the score. 40 Qs 10 x 3 = 30 marks So the expected score is 30 x -1 = -30 marks now 20%, which is in line with the profile of a "good" test (which we saw in the second slide).
  • 11. To conclude, here’s the first slide again... Negative marking has two advantages: 1) it yields scores that are not inflated by guesswork, 2) it discourages random guesses, and therefore the scores are more reliable.