Erasure Analyses: Reducing the Number of False Positives.
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| Title: | Erasure Analyses: Reducing the Number of False Positives. |
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| Authors: | McClintock, Joseph Clair (AUTHOR) |
| Source: | Applied Measurement in Education. Jan-Mar2015, Vol. 28 Issue 1, p14-32. 19p. |
| Subjects: | Grading of students, Multiple choice examinations, Examination answer sheets, False positive error, Educators' attitudes, Corruption |
| Abstract: | Erasure analysis is the study of the pattern or quantity of erasures on multiple-choice paper-and-pencil examinations, to determine whether erasures were made post-testing for the purpose of unfairly increasing students’ scores. This study examined the erasure data from over 1.4 million exams, taken by more than 600,000 students. Three different methods of calculating erasures and two methods for analyzing erasures were explored. In the present study’s dataset, the distribution of the mean number of erasures was positively skewed, and student ability and student race were associated with higher percentages of erasures. Techniques that do not consider these factors are likely to be biased toward flagging more classes than would be expected by chance. A simple technique for reducing the number of false positive flags is proposed. [ABSTRACT FROM PUBLISHER] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Erasure analysis is the study of the pattern or quantity of erasures on multiple-choice paper-and-pencil examinations, to determine whether erasures were made post-testing for the purpose of unfairly increasing students’ scores. This study examined the erasure data from over 1.4 million exams, taken by more than 600,000 students. Three different methods of calculating erasures and two methods for analyzing erasures were explored. In the present study’s dataset, the distribution of the mean number of erasures was positively skewed, and student ability and student race were associated with higher percentages of erasures. Techniques that do not consider these factors are likely to be biased toward flagging more classes than would be expected by chance. A simple technique for reducing the number of false positive flags is proposed. [ABSTRACT FROM PUBLISHER] |
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| ISSN: | 08957347 |
| DOI: | 10.1080/08957347.2014.973563 |
