Tid: 11 oktober 2017, kl 13-14
Plats: B705


Item calibration is a technique to estimate characteristics of questions (called items) in achievement tests. In computerized adaptive tests (CAT), item calibration is an important tool for maintaining, updating and developing new items for an item bank. To efficiently sample examinees with specific ability levels for this calibration, we use optimal design theory where we assume that the probability to answer correctly to an item follows a two-parameter logistic model. A locally D-optimal unrestricted design has two design points for ability to calibrate an item. In practice it is hard to sample examinees from a population with these specific ability levels due to unavailability or limited availability of examinees. To counter this problem, we use the concept of optimal restricted designs and show that this concept naturally fits to item calibration. Locally D-optimal restricted designs provide us with two intervals of ability levels for optimal calibration of an item. Several scenarios for item calibration with optimal restricted designs are presented here. These scenarios recommend us that the naive way to sample examinees around unrestricted design points is not the optimal way to calibrate an item.