Item Purification Does Not Always Improve DIF Detection

Abstract

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 to get purified sets of items, unaffected by DIF. The purpose of this article 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 counterexample 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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.