   Item purification does not always improve DIF detection: a counter-example with Angoff’s Delta plotMagis, David  ; in Educational & Psychological Measurement (2013), 73 Item purification is an iterative process that is often advocated as improving the identification of items affected by differential item functioning (DIF). With test-score based DIF detection methods ... [more ▼] Item purification is an iterative process that is often advocated as improving the identification of items affected by differential item functioning (DIF). With test-score based DIF detection methods, item purification iteratively removes the items currently flagged as DIF from the test scores in order to get purified sets of items, unaffected by DIF. The purpose of this paper is to highlight that item purification is not always useful and that a single run of the DIF method may return equally suitable results. Angoff’s Delta plot is considered as a counter-example DIF method, with a recent improvement to the derivation of the classification threshold. Several possible item purification processes may be defined with this method, and all of them are compared through a simulation study and a real data set analysis. It appears that none of these purification processes clearly improves the Delta plot performance. A tentative explanation is drawn from the conceptual difference between the modified Delta plot and the other traditional DIF methods. [less ▲] Detailed reference viewed: 9 (1 ULg)A robust outlier approach to prevent Type I error inflation in DIFMagis, David ; in Educational & Psychological Measurement (2012), 72 The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxys for the ability trait level. One of the most popular ... [more ▼] The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxys for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source of potential Type I error inflation, especially in the presence of DIF but also when DIF is absent and in the presence of impact. The purpose of this paper is to present an alternative statistical inference approach based on the same measure of DIF but such that the Type I error inflation is prevented. The key notion is that for DIF items, the measure has an outlying value which can be identified as such with inference tools from robust statistics. Although we use the MH log-odds ratio as a statistic, the inference is different. A simulation study is performed to compare the robust statistical inference with the classical inference method, both based on the MH statistic. As expected the Type I error rate inflation is avoided with the robust approach, while the power of the two methods is similar. [less ▲] Detailed reference viewed: 13 (5 ULg) |
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