RIM: A Random Item Mixture Model to Detect Differential Item Functioning

In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized p -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life data set.     

Interracial children

Changing lifestyles are having an impact on programs for young children. Recently these programs have adjusted to meet the needs of children from one-parent homes, broken homes, and teen homes. Now children of mixed racial and ethnic backgrounds are an ever-increasing phenomenon in programs. He is a parent of four interracial children and lectures on this topic     

Feedback complexity and corrective efficiency

College undergraduates read a 2400-word passage, responded to 16 multiple-choice questions, and received one of four types of feedback following their responses. Complexity of feedback was inversely related to both error correctability and criterion efficiency taken as a ratio of feedback study time to post-test corrects. The results were discussed in terms of depth of processing and instructional comprehension.