D-optimal Designs with Ordered Categorical Data

Adaptive Tests for Ordered Categorical Data

Assessing Measurement Equivalence in Ordered-Categorical Data

Assessing measurement equivalence in the framework of the common factor linear models (CFL) is known as factorial invariance. This methodology is used to evaluate the equivalence among the parameters of a measurement model among different groups. However, when dichotomous, Likert, or ordered responses are used, one of the assumptions of the CFL is violated: the continuous nature of the observed...

Some new D-optimal designs

We construct several new (v; r, 8; A) supplementary difference sets with v odd and T' + .5 = A + (v 1) /2. They give rise to D-optimal designs of order 2v. D-optimal designs of orders 158, 194, and 290 are constructed here for the first time. We also give an up to date survey of this class of supplementary difference sets in arbitrary Abelian groups of odd order v < 100. o. Introduction Supplem...

Regression with an ordered categorical response.

A survey on Mseleni joint disease in South Africa involved the scoring of pelvic X-rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading ...

Marginally restricted sequential D-optimal designs

In many experiments, not all explanatory variables can be controlled. When the units arise sequentially, different approaches may be used. The authors study a natural sequential procedure for “marginally restricted” D-optimal designs. They assume that one set of explanatory variables (x1) is observed sequentially, and that the experimenter responds by choosing an appropriate value of the explan...