Microeconomics, dataset, bunching, histogram, data analysis
Bunching grades suggest that instructors do not really grade on a curve, nor do students' distribution of grades fit the Gaussian distribution. Nevertheless, it is quite possible to assume for instance that bunching may be accounted for by the fact that most students in higher education and learning can get decent grades for their exams, while a small minority fares disastrously. As a result, the distribution would have a large mode (bunching) around "standard" grades, with a right-hand side tail with extreme grades on the lower end. If grading on a curve is indeed the standard, failing students at similar grades would be provided with the same final grade as those who failed at higher levels. Out of fairness, the instructor may be inclined to create a contrast between these two categories.
[...] Assuming uniformity, we predict that the modal grade - where the bunching occurs- is midway between 90 and 60, so 70. 2.a. There are 1606 observations in the dataset. We count 11 courses following their ID numbers. Table below reports the breakdown of courses according to semesters. courseID Winter Summer Total 1 38 0 38 2 39 0 39 3 6 35 41 4 86 0 86 5 0 106 106 6 40 0 40 7 194 112 306 8 169 277 446 9 72 125 197 10 86 171 257 11 0 50 50 Total 730 876 1,606 b. [...]
[...] Applied Microeconomics - Bunching in the Classroom 1.a. Bunching grades suggest that instructors do not really grade on a curve, nor do students' distribution of grades fit the Gaussian distribution. Nevertheless, it is quite possible to assume for instance that bunching may be accounted for by the fact that most students in higher education and learning can get decent grades for their exams, while a small minority fares disastrously. As a result, the distribution would have a large mode (bunching) around "standard" grades, with a right-hand side tail with extreme grades on the lower end. [...]
[...] To that effect, we generate two dummy variables that take 1 in the following conditions: cutoff=1 if the grade is close to 60. (between 59 and 61) bunching=1 if the grade is between 70 and 78. And 0 other wise. Points are then regressed on the number of students, as well as the two dummy variables. Results are reported below: All estimated coefficients are statistically significant. We check for the behavior of residuals from the regression, and the results are reported below: The shape is as expected, in that the residuals decline steeply around the predicted values for cut-offs and bunching. [...]
[...] Figure A reports all figures, whereas figure B excludes those with a zero grade. We can indeed observe that there is bunching around grades of 60 and above, as well as a steep jump in the distribution between 55 and 60 - an indication of the assumptions made earlier about instructors and their grading criterions tend to reward students that were close to passing with additional grades to get them through, whereas students with very low grade are far few and between. [...]
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