IFTPredictor: Predictions Using Item-Focused Tree Models
This function predicts item response probabilities and item
responses using the item-focused tree model. The item-focused tree model
combines logistic regression with recursive partitioning to detect
Differential Item Functioning in dichotomous items. The model applies
partitioning rules to the data, splitting it into homogeneous subgroups, and
uses logistic regression within each subgroup to explain the data.
Differential Item Functioning detection is achieved by examining potential
group differences in item response patterns. This method is useful for
understanding how different predictors, such as demographic or psychological
factors, influence item responses across subgroups.
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