Use a decision template weighing assumption fit, DIF uniformity, simultaneity, significance testing, and ease of use when choosing a DIF method for polytomous performance assessments¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article offers its method comparison as "a useful template for test analysts to use when deciding which techniques to employ when conducting differential item functioning analyses." Analysts should check whether the assessment violates each method's assumptions, whether DIF is uniform or nonuniform, and weigh LDF's simultaneous multi-category determination, built-in significance measure, and ease of use against ICC's sample independence and model specificity. The authors argue this supports simultaneous estimation for polytomously scored items and may improve DIF detection in performance assessments.

Design Implications¶

Context¶

Requirements¶

• Knowledge of the assessment's item structure (content mix, item clustering, administration format) to evaluate assumption violations

Constraints¶

• The article concludes the best method is not obvious from its results alone and recommends a Monte Carlo study with manufactured data to eliminate confounding design aspects

Target Learners¶

• test analysts and testing programs evaluating polytomously scored performance assessments

Target Learning Goals¶

• selecting and applying appropriate DIF detection methods for performance assessments

Affordances¶

• Icc Unsigned Area Dif Method

Related Strategies¶

• Use context-specific fit questions to decide whether a specific measure suits your setting
• Before applying a normal-distribution stabilization model, agencies should thoroughly explore indicator data distributions with visual and numeric checks
• Use DFIT indices with empirically established cutoffs and iterative linking to screen polytomous tests for DIF and DTF

Examples¶

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Key Sources¶

• Holweger, Nancy; Weston, Timothy. (1998). Differential Item Functioning: An Applied Comparison of the Item Characteristic Curve Method with the Logistic Discriminant Function Method. https://eric.ed.gov/?id=ED422362