DIF Trees Using Classification Trees to Detect Differential Item Functioning
This paper proposed method of decision tree analysis to detect uniform DIF item, comparing with two conventional MH and logistic regression methods. The algorithm of MH is based on a three way contingency table of frequencies for reference group and focal group against all score levels, thus common odds ratio for each item can be obtained. One can find uniform DIF if main effect coefficient is significant and nonuniform DIF if coefficient of interaction term is significant through the LR method. The tree method in the present study adopted the deviance of Venables and Ripley (2002). A series of simulation were conducted and all aforementioned methods were considered to detect DIF. Results indicated that high power of all three methods if sample size is large; the tree method has the best performance on power and the identical type one error with the other two. The limitation of the paper is we should find a way to choice criterion of the tree method.