The purpose of the present study is to investigate the predictive validity of the BUEPE through the use of the three-parameter IRT model& / #8217 / s ability estimates.
The study made use of the BUEPE September 2000 data which included the responses of 699 students. The predictive validity was established by using the departmental English courses (DEC) passing grades of a total number of 371 students.
As for the prerequisite analysis the best fitted model of IRT was determined by first, checking the assumptions of IRT / second, by analyzing the invariance of ability parameters and item parameters and thirdly, by interpreting the chi-square statistics.
After the prerequisite analyses, the best fitted model& / #8217 / s estimates were correlated with DEC passing grades to investigate the predictive power of BUEPE on DEC passing grades.
The findings indicated that the minimal guessing assumption of the one- and two-parameter models was not met. In addition, the chi-square statistics indicated a better fit to the three-parameter model. Therefore, it was concluded that the best fitted model was the three-parameter model. The findings of the predictive validity analyses revealed that the best predictors for DEC passing grades were the three-parameter model ability estimates. The second best predictor was the ability estimates obtained from sixty high information items. In the third place BUEPE total scores and the total scores obtained from sixty high information items followed with nearly the same correlation coefficients. Among the three sub-tests, the reading sub-test was found to be the best predictor of DEC passing grades.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/1013507/index.pdf |
| Date | 01 January 2003 |
| Creators | Yegin, Oya Perim |
| Contributors | Berberoglu, Giray |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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