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A statistical technique for the development of an alternate list when using constrained optimization to make
Clarence Kreiter1, Catherine Solow
1Department of Family Medicine, Office of Consultation and Research in Medical Education, University of Iowa College of Medicine, 1-204 MEB, Iowa City, IA 52242-1008, USA. clarence-kreiter@uiowa.edu
Teaching and Learning in Medicine
|February 28, 2002
Summary
Discriminant analysis can model constrained optimization procedures for applicant ranking, accounting for 70% of decision variance. This method offers an alternative for admissions, though criteria may differ from initial selections.
Area of Science:
- Educational Administration
- Data Analysis
- Admissions Processes
Background:
- Constrained optimization accurately translates admission goals but lacks applicant rank ordering.
- Existing admissions procedures often require applicant rank ordering, a limitation of constrained optimization.
Purpose of the Study:
- To describe and evaluate discriminant analysis procedures for generating weights to model constrained optimization.
- To obtain a rank order list of applicants using discriminant analysis.
- To evaluate an additional method that bypasses rank ordering.
Main Methods:
- Discriminant analysis was employed to model a dichotomous group classification selection variable.
- Weights were generated and applied to calculate discriminant function values.
- Success was evaluated using rank orders, correlation, and R-square statistics.
Main Results:
- Discriminant analysis explained 70% of the decision variance from the constrained optimization procedure.
- Real data enabled estimation of student numbers affected by inconsistent outcomes.
Conclusions:
- Discriminant analysis can manage an alternate applicant list, though criteria may differ from initial selection.
- The choice between methods depends on the college's valued outcomes, as each has pros and cons.