An Application of Multivariate Generalizability in Selection of Mathematically Gifted Students
Sungyeun Kim 1 * , Dan Berebitsky 2
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1 Incheon National University, REPUBLIC OF KOREA2 Southern Methodist University, USA* Corresponding Author


Valid and reliable identification of gifted students is one of the most important issues in gifted education. Identification of gifted students commonly includes several instruments with their own relative weight. In many cases, however, the selection decision is arbitrarily made based on national policy or a priori judgment, not based on educational measurements from empirical research. Hence this study determines error sources and the effects of each error source, and investigates optimal weights of composite score in teacher recommendation letters and self-introduction letters using multivariate generalizability theory.

Material and methods:
Teacher recommendation letters and self-introduction letters were collected from 90 students applying to the gifted education program in one science education institute for the gifted in Korea.

First, error sources for the students were relatively large. It suggests that the score variances explained the differences in the giftedness among the students. Second, based on the maximum generalizability coefficient for teacher recommendation letter and self-introduction letter, the optimal weight should adjust to 0.7:0.3 from 0.5:0.5 in the original institution weights.

These results are necessarily specific to the selection assessment instruments considered in this study. However, the methodology applied can be utilized in other selection instruments developed by many institutions.


This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2016, Volume 12, Issue 9, 2587-2598

Publication date: 29 Jun 2016

Article Views: 1667

Article Downloads: 808

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