Probing for Bias: Comparing Populations Using Item Response Curves

Bayesian Estimation of Item Response Curves

We will consider dichotomous responses to a set of test items which are designed to measure the abilities of individuals. We assume that each item is characterized by an item response curve, a function of ability which is indexed by unknown parameters called item parameters. We consider a Bayesian method for estimating these parameters when the abilities of the individuals are assumed to have a...

Item Bias Detection Using Loglinear Irt

A method is proposed for the detection of item bias with respect to observed or unobserved subgroups. The method uses quasi-loglinear models for the incomplete subgroup x test score x Item 1 × • • • x item k contingency table. If subgroup membership is unknown the models are Haberman's incomplete-latent-class models. The (conditional) Rasch model is formulated as a quasi-loglinear model. The pa...

Comparing Curves Using Additive Models

Advances in technology have increased dramatically the amount of data measured in industrial processes. Thousands of measurements are available nowadays in operations where previously only a single measurement, at a given point in time or space, was taken. These measurements allow the reconstruction of the whole profile or "signature" of the operation over time or space. Examples are the tonnag...

An Item Response Curves Analysis of the Force Concept Inventory

Comparing Two Numerical Response Populations: Independent Samples

Chapter 19 is very much like Chapter 15. The major—and obvious—difference is that in the earlier chapter the response was a dichotomy, but in this chapter the response is a number. If you revisit the material on the four types of studies in Section 15.2, you can see that the fact that the response was a dichotomy is irrelevant. In other words, everything you learned earlier about how the interp...