I have a couple of observations about the MGMAT CAT Quantitative section. I'd like to admit that these observations were based only on 2 MGMAT CATs that I took in the last 2 days. I'd like to know - a) Other test-taker perspectives and b) MGMAT's thoughts on my observations.
1. Once you start well, the difficulty-level of questions (call it Q-level) jumps quickly to the 700-800 range. While it's understandable why the algorithm pulls questions out of the 700-800 bin, I found that ALL remaining questions (if you maintain a good strike rate) are of the same 700-800 bin difficulty level. This is unlike GMATPREP, which always randomizes questions, not sticking to the hard bin even though a candidate's strike rate may be high. In fact, I've noticed that the GMATPREP 'settles down' to an average to medium hard level after about 60% of the Quant section. In fact, there are also easy questions mixed throughout the test, even for a strongly performing candidate. This 'diversity' in the Q-level allows the average candidate to complete the real GMAT without rushing through the final questions.
2. Given the above, a candidate's 'mistake-level' is bound to be higher on the MGMAT than the GMATPREP. I found that consistently getting the 700-800 Q-level problems make it difficult to stick to the 2-min average. As a result, towards the end of the test, candidates may have to guess/skim problems. While I agree that this is a great strategy during the preparation-stage, consistently practicing on MGMAT CATs could entail a tendency in the average candidate to attempt problems at a faster than required pace in the real GMAT. This could have an unintended consequence of sacrificing the accuracy for speed on the real GMAT.
3. On the other hand, the 700-800 Q-level problems greatly improve a candidate's understanding of concepts through difficult problems under timed conditions. Hence I feel that the MGMAT CATs are a great learning tool during the prep-stage - however, because of (1) and (2) above, it may not be an accurate predictor of the real GMAT score, unless the algorithm somehow 'compensates' for this difference in testing conditions on the two CATs while evaluating the scaled score.
Thoughts?
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