SCRC 2005 / FIM XII
   Hosted by Auburn University

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Bachhofer-Gupta-Sobel Memorial Symposium on
Ranking and Selection and Multiple Comparison Procedures Methodologies

 

Madhuri S. Mulekar is a Professor of Statistics in the Department of Mathematics & Statistics. Her research areas of interest are selection and ranking procedures, sequential testing procedures, and classical inference. She is recipient of several National Science Foundation, USARC, and Alabama EPSCoR awards. She directed undergraduate research program (REU) in statistics which was attended by students from colleges and universities all over United States. She has directed over 40 undergraduate research projects, most of which were externally funded. She has published 35 articles, a book and a book chapter. One of her book is currently being reviewed. She has extensively collaborated with USA faculty and faculty from other institutions on numerous projects. She also worked as a Statistician for the University of South Alabama - Comprehensive Sickle Cell Center. She has served on various national committees, including Advisory Committee on Continuing Education and Advanced Placement Test Development Committee for Statistics, to promote statistics research and education. She was awarded Scholar of the Year Award by Phi Kappa Phi, and Chapter Recognition Award by Council of Chapters, American Statistical Association. She was also elected a Fellow of the American Statistical Association.

Selecting a grade with the most (or least) overlapping in assessment categories

It is a common practice to use standardized testing procedures to assess the students’ knowledge through different assessment categories. For example, students’ reading levels are assessed through their scores in vocabulary and comprehension; or students’ knowledge of mathematics is assessed through their scores in problem-solving and procedures. The educators are interested in estimating the amount of overlap in the information provided by two assessment categories and identifying a group showing the most (or least) amount of overlap. A procedure will be presented for selecting a population with the largest (or smallest) overlap coefficient under the assumption that scores in two categories follow a bivariate normal distribution. The proposed procedure will be described using data collected by one school system.

 


 

12th Annual Conference of the Forum for Interdisciplinary Mathematics (FIM XII)