方法对比

	Standardized Test Analysis	Differential Item Functioning in Educational Testing
领域	Education	Education
方法族	Latent structure	Latent structure
起源年份	2014	1993
提出者	Educational measurement profession (AERA/APA/NCME Standards; Lord; Cronbach)	Educational measurement / test-fairness tradition (Holland, Wainer, Dorans, Thissen)
类型	Psychometric evaluation of items, reliability, validity, and fairness of standardized tests	Test-fairness analysis detecting items that function differently across groups
开创性文献	American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for Educational and Psychological Testing. AERA. ISBN: 9780935302356	Holland, P. W., & Wainer, H. (Eds.). (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 9780805809725
别名	Standardized Testing Analysis, Test Score Analysis, Item and Test Analysis, Educational Test Psychometrics	Educational DIF Analysis, Item Bias Detection in Tests, Test Fairness DIF, Mantel-Haenszel DIF
相关	3	4
摘要	Standardized test analysis is the body of psychometric methods used to evaluate and score standardized educational tests: analyzing how items perform, estimating reliability and the standard error of measurement, scaling scores via classical or item response theory, and assembling validity and fairness evidence. Governed by the professional Standards for Educational and Psychological Testing and rooted in test theory synthesized by Lord and others, it is the disciplined work that turns a set of test questions into defensible scores carrying meaning, precision, and fairness.	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.