EDAS and Data Imputation

This project is aimed at developing tools for generating national norms when a subset of examination items is used from an ACS Exam. To achieve this project, we developed a method to impute missing data necessary for calculating subset norms. Complete data sets are evaluated for the number of items that can be removed such norm stability is maintained. Item removal studies include easiest items, hardest items, random items, and items by content area. This work is creating new opportunities for course and programmatic assessment by content area rather than singly based on complete examination scores.

References

• Brandriet, A., & Holme, T. (2015). Development of the Exams Data Analysis Spreadsheet as a Tool To Help Instructors Conduct Customizable Analyses of Student ACS Exam Data. Journal of Chemical Education, 92(12), 2054-2061.
• Brandriet, A., & Holme, T. (2015). Methods for Addressing Missing Data with Applications from ACS Exams. Journal of Chemical Education, 92(12), 2045-2053.
• Marek, K., Raker, J.R., & Murphy, K.L. (2019) Development of a methods for imputation of missing data using ACS Exams as a prototype. submitted to Journal of Chemical Education.