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| Home > JCE Print > Journal of Chemical Education > Issues >
2001
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August
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Research: Science and Education
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Chemical Education Research
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Analysis of Success in General Chemistry Based on Diagnostic Testing Using Logistic Regression
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Margaret J. Legg
Department of Chemistry, St. Ambrose University, Davenport, IA 52803
Jason C. Legg
Department of Statistics, Iowa State University of Science & Technology, Ames, IA 50011
Thomas J. Greenbowe*
Department of Chemistry, Iowa State University of Science & Technology, Ames, IA 50011
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August 2001
Vol. 78 No. 8
p. 1117
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| Abstract |
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Several chemistry diagnostic and placement exams are used to help place chemistry students in an appropriate course or to determine strengths and weaknesses for specific topics in chemistry or math. The purpose of obtaining pre-course measurements is to increase students' academic success. Often these tests are used to predict the chance a student has in passing a course. This paper discusses the statistical methods of logistic regression applied to predicting the probability of passing a course, based on the scores on the California Chemistry Diagnostic Test at two different institutions with two different instructors over multiple years. This technique describes the relation of a test score (a continuous variable) to the probability of passing the class (a binary variable). Many papers in the Journal of Chemical Education have used a simple linear regression technique to correlate placement test scores with the proportion of students passing a course. The model assumptions are difficult to satisfy when using simple linear regression. Simple linear regression is useful when continuous predictor variables predict a continuous response, whereas logistic regression is useful when continuous predictor variables predict a binary response. Differences between simple linear regression and logistic regression and methods for evaluating linear regression model assumptions are discussed in detail. The fundamental concepts behind regression are described, with the caveats of using regression equations for predictions. By using logistic regression, instructors will be able to provide students with an estimate of their probability of passing the course.
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| More Information |
 Citation |
Legg, Margaret J.; Legg, Jason C.; Greenbowe, Thomas J. J. Chem. Educ. 2001 78 1117.
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Keywords |
CER Statistics; Chemical Education Research; General Chemistry; Teaching/Learning Theory/Practice
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History |
Created:
Last Updated: |
July 10, 2001
August 31, 2005
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| Home > JCE Print > Journal of Chemical Education > Issues >
2001
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August
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