Item purification made both DIF methods more sensitive, flagging more items¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2024: tool and model accuracy changes quickly

Subclaims¶

q2 i? Applying item purification to Lord's chi-square and Raju's UA analyses tended to flag more items as DIF than analyses without purification. → Soysal 2021

Evidence¶

Soysal 2021¶

Soysal, S., & Yilmaz Kogar, E. (2021). An Investigation of Item Position Effects by Means of IRT-Based Differential Item Functioning Methods. International Journal of Assessment Tools in Education, 8(2), 239-256. https://doi.org/10.21449/ijate.779963

q2 · i? · associational · r2

In the TEOG booklet DIF analysis, tables 4-6 show more flagged items under with-purification conditions for both methods; the authors state purification "tended to be more sensitive than performing without purification."

"Thus, it made us think that performing item purification with Lord's χ2 and Raju's UA methods tended to be more sensitive than performing without purification."

Discussion¶

Related Claims¶

• The ICC and LDF methods identify substantially different sets of items as exhibiting DIF on a state science performance assessment — the general claim this finding bears on, with the studies behind it
• Lord's chi-square identified more DIF items than Raju's unsigned area in the without-purification condition — related
• An iterative two-stage linking procedure improves identification of DIF items — related
• The chi-square significance test for NC-DIF is overly sensitive at large sample sizes, requiring an empirical cutoff value — related