AI Summary & Key Takeaways
AI InsightThe equating procedure was thus adjusted to eliminate these differences and thereby allow the tests to be used interchangeably.
📌 3 Key Takeaways
Abstract
Educational assessment tests are designed to measure the same psychological constructs over extended periods of time. This feature is important considering that test results are often used in the selection process for admittance to university programs. To ensure fair assessments, especially for those whose results weigh heavily in selection decisions, it is necessary to collect evidence demonstrating that the assessments are not biased, and to confirm that the scores obtained from different test forms have statistical equality. For this purpose, test equating has important functions, as it prevents bias generated by differences in the difficulty levels of different test forms, allows the scores obtained from different test forms to be reported on the same scale, and ensures that the reported scores communicate the same meaning. In this study, these important functions were evaluated using real college admission test data from different test administrations. The kernel equating method under the non-equivalent groups with covariates design was applied to determine whether the scores obtained from different time periods but measuring the same psychological constructs were statistically equivalent. The non-equivalent groups with covariates design was specifically used because the test groups of the admission test are non-equivalent and there are no anchor items. Results from the analyses showed that the test forms had different score distributions, and that the relationship was non-linear. The equating procedure was thus adjusted to eliminate these differences and thereby allow the tests to be used interchangeably.
Özet
Educational assessment tests are designed to measure the same psychological constructs over extended periods. This feature is important considering that test results are often used for admittance to university programs. To ensure fair assessments, especially for those whose results weigh heavily in selection decisions, it is necessary to collect evidence demonstrating that the assessments are not biased and to confirm that the scores obtained from different test forms have statistical equality. Therefore, test equating has important functions as it prevents bias generated by differences in the difficulty levels of different test forms, allows the scores obtained from different test forms to be reported on the same scale, and ensures that the reported scores communicate the same meaning. In this study, these important functions were evaluated using real college admission test data from different test administrations. The kernel equating method under the non-equivalent groups with covariates design was applied to determine whether the scores that were obtained from different periods and measured the same psychological constructs were statistically equivalent. The non-equivalent groups with covariates design was specifically used because the test groups of the admission test are non-equivalent and there are no anchor items. Results from the analyses showed that the test forms had different score distributions and that the relationship was non-linear. Thus, the equating procedure was adjusted to eliminate these differences and thereby allowing the tests to be used interchangeably.