Item Response Theory (IRT) is a statistical framework used to analyze test items and assess individual abilities in education and psychology. It includes models like 1PL, 2PL, and 3PL, which account for item difficulty, discrimination, and guessing. IRT's precision enhances test fairness and adaptability, making it crucial for standardized tests and adaptive testing systems.
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IRT is a statistical framework used in educational and psychological assessment to evaluate test items and test-taker abilities
IRT surpasses CTT by considering the interaction between test-taker abilities and item characteristics
IRT uses mathematical models to estimate the probability of a correct response based on test-taker abilities and item parameters
The 1PL model, also known as the Rasch model, considers only item difficulty
The 2PL model incorporates both item difficulty and discrimination
The 3PL model includes a guessing parameter and is useful in settings where guessing could impact test scores
IRT is particularly advantageous in adaptive testing scenarios, where the difficulty of test items is adjusted based on the test-taker's ability level
IRT is useful in diagnostic testing and progress monitoring, providing tailored instructional strategies and immediate feedback for educators and learners
IRT is extensively used in standardized tests like the SAT and GRE, as well as in computerized adaptive testing
Implementing IRT requires careful calibration and consideration of sample size to ensure precise parameter estimation
Parameter invariance must be verified across different groups to avoid biased outcomes
Implementing IRT also requires attention to fairness, test-taker motivation, and testing conditions