Performances of MIMIC and Logistic Regression Procedures in Detecting DIF

Yıl 2020, Cilt: 11 Sayı: 1, 1 - 12, 24.03.2020

Seçil Uğurlu , Burcu Atar

https://doi.org/10.21031/epod.531509

Cited By: 2

Öz

In this
study, differential item functioning (DIF) detection performances of multiple
indicators, multiple causes (MIMIC) and logistic regression (LR) methods for
dichotomous data were investigated. Performances of these two methods were
compared by calculating the Type I error rates and power for each simulation
condition. Conditions covered in the study were: sample size (2000 and 4000
respondents), ability distribution of focal group [N(0, 1) and N(-0.5, 1)], and
the percentage of items with DIF (10% and 20%). Ability distributions of the
respondents in the reference group [N(0, 1)], ratio of focal group to reference
group (1:1), test length (30 items), and variation in difficulty parameters
between groups for the items that contain DIF (0.6) were the conditions that
were held constant. When the two methods were compared according to their Type
I error rates, it was concluded that the change in sample size was more
effective for MIMIC method. On the other hand, the change in the percentage of
items with DIF was more effective for LR. When the two methods were compared
according to their power, the most effective variable for both methods was the
sample size.

Anahtar Kelimeler

Differential item functioning, MIMIC model, Logistic regression, Uniform DIF, Type I error rate and power

Kaynakça

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