Linganisha mbinu

	Differential Item Functioning in Educational Testing	Differential Distractor Functioning
Nyanja	Education	Education
Familia	Latent structure	Latent structure
Mwaka wa asili	1993	2008
Mwanzilishi	Educational measurement / test-fairness tradition (Holland, Wainer, Dorans, Thissen)	Item-bias methodology (Green, Crone & Folk; Penfield)
Aina	Test-fairness analysis detecting items that function differently across groups	Group-difference analysis of the incorrect options (distractors) of multiple-choice items
Chanzo asilia	Holland, P. W., & Wainer, H. (Eds.). (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 9780805809725	Penfield, R. D. (2008). An odds ratio approach for assessing differential distractor functioning effects under the nominal response model. Journal of Educational Measurement, 45(3), 247–269.
Majina mbadala	Educational DIF Analysis, Item Bias Detection in Tests, Test Fairness DIF, Mantel-Haenszel DIF	DDF, Distractor-Level DIF, Differential Option Functioning, Distractor Functioning Analysis
Zinazohusiana	4	4
Muhtasari	Differential item functioning (DIF) analysis is the central statistical tool for evaluating the fairness of test items in education. An item shows DIF when examinees of equal ability but different group membership — for example by gender, race/ethnicity, or language background — have unequal probabilities of answering it correctly. By conditioning on ability before comparing groups, DIF analysis separates genuine item bias from real group differences in proficiency, and flags items for expert review before they affect high-stakes decisions.	Differential distractor functioning (DDF) extends test-fairness analysis from the correct answer to the wrong ones. It asks whether examinees of equal ability but different group membership are differentially attracted to particular distractors (incorrect options) of a multiple-choice item. By analyzing option-level rather than just right/wrong responses, DDF can detect bias that ordinary differential item functioning misses and, crucially, help explain why an item functions differently — pointing to the specific wrong option luring one group. Penfield's odds-ratio approach under the nominal response model is a standard tool.