Logistic Regression (LR) has been a technique used for the detection of items exhibiting differential item functioning (DIF). When it was introduced in 1990, the LR was conceptualized as strictly a test of statistical significance. This led to the over-identification of items as DIF, generally not exhibiting practically (psychometrically) significant differences. The use of blended decision rules – where effect sizes are used in addition to statistical significance in the decision-making process – was proposed to address this issue. Previous work in the literature attempted to align a decision rule grounded in the Mantel-Haenszel (M-H) technique to LR. However, this work is unable to replicate previously recommended cut-offs, through the use of the same methodology on a different data set. It is possible that cut-off values may be dataset specific, which also opens the question of whether universal cut-off values for effect sizes for DIF are a realistic expectation. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/54871 |
| Date | 11 1900 |
| Creators | Gesicki, Adam |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivs 2.5 Canada, http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ |
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