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Lasso Regularization for DIF Detection in Graded Response ModelsAvila Alejo, Denisse 05 1900 (has links)
Previous research has tested the lasso method for DIF detection in dichotomous items, but limited research is available on this technique for polytomous items. This simulation study compares the lasso method to hybrid ordinal logistic regression to test performance in terms of TP and FP rates when considering sample size, test length, number of response categories, group balance, DIF proportion, and DIF magnitude. Results showed better Type I error control with the lasso, with smaller sample sizes, unbalanced groups, and weak DIF. The lasso also exhibited more stable Type I error control when DIF was weak, and groups were unbalanced. Lastly, low DIF proportion contributed to better Type I error control and higher TP rates with both methods.
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