{'fraction_non_alphanumeric': 0.022095509622238062, 'fraction_numerical': 0.0042765502494654314, 'mean_word_length': 4.899159663865547, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of Grades for University Students Using Administrative Data and the IRT Procedure

This study is the first to implement item response theory on an entire educational program rather than on a single test. I use the new IRT procedure, introduced in SAS/STAT® 13.1, to compare the difficulty and use of grading scale across compulsory courses at the bachelor’s program in economics at the University of Copenhagen. Further, the latent ability traits estimated for all students in the sample by PROC IRT are used as predictors in a logistic regression model. The hypothesis is that students who have a lower ability trait will have a greater probability of dropping out than students with a higher ability. In the paper, three unidimensional item response models, two dichotomous and one polytomous, are applied on data from one cohort of students (n=236). The results suggest that in order to receive the highest possible grade, the highest level of student ability is needed for the course exam in the first-year course Descriptive Economics A. In contrast, the third-year course Econometrics C is the easiest course in which to receive a top grade. Additionally, I find that as student ability decreases, the probability of a student dropping out of the bachelor's program increases drastically. However, contrary to expectations, some students with high ability levels also end up dropping out.