In order to explore PD in educational assessments, mixed-effects models (De Boeck and Wilson 2004) have been proposed to analyze item position effects by comparing the probabilities of a correct response when the item is presented in different positions (Debeer and Janssen 2013).
In addition, we only considered subscale scores and did not analyze item-level data.
Finally, we applied the unidimensional Rasch model to analyze item responses in the PedsQL™ 4.0 Generic Core Scales.
Multiple-Indicator Multiple-Cause modeling (MIMIC) [ 20] is one of DIF methods that help analyze item response function and group difference.
They cannot provide in-depth understanding of the analyzed items due to the inherently reductive nature of classification.
In the fourth model, we analyzed items by combining both content and tier so every combination of content and tier represents its own dimension.
In the second model, we analyzed items according to tier: one dimension for tier-1 items to represent knowing, and another dimension for tier-2 items to represent reasoning.
According to the analyzed items, the irregularities that were found were quality (84.4%), privacy (46.2%), honesty (18.5%), informed consent (15.6%), responsibility and origin (13.9%), transparency (12.1%), medical ethics (2.3%).
We then analyzed items based on item response theory (IRT).
The internal dropout rate was 1-51-5%thin 22 of the 30 analyzed items.
