How does the CAT Percentile Predictor work?

How does the CAT Percentile Predictor work?

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The Common Admission Test, commonly known as CAT, is an online test conducted by the Central agency. The exam is conducted to provide aspirants a chance to aspirants for secure admission into MBA programs. The paper has 3 sections: Verbal Ability and Reading Comprehension (VARC), Data Interpretation (DI) and Logical Reasoning (LR), and Quantitative Ability (QA). The exam time is three hours, with one-hour sectional time. The Indian Institutes of Management (IIMs) set up the CAT exam to allow students to take a fair chance at business administration programs (MBA or PGDM). The merit list is percentile-based, and admissions are conducted based on the percentile scored by a student. The Examination is conducted in November every year, and the results are published in January.

During this duration, it is natural that the aspirants get anxious about their performance, and how well they scored. There are many online CAT Percentile Predictors for the aspirants to ease their anxiety, but the question that arises is, HOW DOES THE CAT PERCENTILE PREDICTOR WORKS?

The parameters on which CAT Predictor works

Now, the CAT percentile depends on 3 things:

  • Your Performance compared to other candidates
  • Difficulty Level of Particular slot and section
  • Total number of candidates appeared

An Online CAT Percentile Predictor uses these 3 parameters to give you an estimate of your expected percentile. An Online CAT Percentile Predictor follows the stepwise method to generate a scaled score, assign sectional ranks to the candidate, and then rounding the percentile score to arrive at a final rank. Now, the scaled score is first calculated for each section of each slot individually. 

How CAT Percentile Predictor Calculates the Score

For a better understanding, let us we’ll take an example. Let’s say the section is Data Interpretation and Logical Reasoning (DILR):

  • First, the predictor calculates the mean (M1) and the standard deviation (S1) of all the candidates’ raw scores in the DILR section morning session. Now, G1 = M1+S1
  • Similarly for the afternoon session and the evening session G2 = M2+S & G3 = M3+S3 respectively.
  • Next, it calculates the mean (M) and standard deviation (S) of the raw scores in DILR sections of all candidates appearing appeared in the CAT, G = M+S.
  • Then, it calculates the mean raw score for the top 0.1 % candidates in the DILR section for each slot individually.

Now, when a candidate enters his raw score (R) in the DILR section for the morning section, then the scaled score (R’) is:

                                    R’ = ((R – G1) * ((M0.1 – G)/(M10.1 – G1))) + G

Similarly, for the afternoon session,

                                    R’ = ((R – G2) * ((M0.1 – G)/(M20.1 – G2))) + G

And for the evening session,

                                    R’ = ((R – G3) * ((M0.1 – G)/(M30.1 – G3))) + G

Then the same process is followed for the other sections, and a total scaled score is obtained. Next, it calculates the percentile score from the obtained total scaled score as follows:

  • The A total number of candidates (N).
  • Then arises a rank (r) based on the obtained total scaled score. Also, the same rank is given to candidates with the same total scaled score.
  • Then it calculates the percentile score (P) using:
    • P = ((N-r)/N) * 100
  • Then it rounds off the calculated percentile score up to two decimal points.

The candidate enters the raw score section-wise and selects the slot in which he/she appeared. The predictors calculate his scaled score for each section, and then calculate the CAT percentile predictor score using the total scaled score and give the candidate’s percentile score.

It is usually advised to use a predictor quickly after giving the exam for better accuracy. The accuracy of percentile predictors has been questioned time and again, but they have proved fairly accurate over time. Many students get similar final results. Often there is a difference of a maximum of 1 percentile, which can arise due to various factors in a predictor. We hope this helps you to understand how the CAT percentile predictor works. 

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