Optimum examinee samples for item parameter estimation in item response theory: A multi-objective programming approach

This paper proposes a multi-objective programming method for determining samples of examinees needed for estimating the parameters of a group of items. In the numerical experiments, optimum samples are compared to uniformly and normally distributed samples. The results show that the samples usually recommended in the literature are well suited for estimating the difficulty parameters. Furthermore, they are also adequate for estimating the discrimination parameters in the three-parameter model, butnot for the guessing parameters.     

A response to "On token reinforcement and stuttering therapy: Another view on findings reported by Howie and Woods (1982)"

Ingham's (1983) critique of our research is based on the unwarranted assumption that it claimed to be a replication of Ingham and Andrews' (1973a) study. Our report did not claim to be a replication. Procedural differences between treatments do not preclude the possibility of drawing general conclusions that may apply to related treatments, or suggesting possible confounding variables that might be operating in another study. We have nevertheless dealt with each of Ingham's methodological objections. In general, we believe that we struck an acceptable compromise between the needs of clients and theoretical and research demands. We stand by our original conclusions, and note with satisfaction that Ingham concurs with our emphasis on systematic structure rather than the presence or absence of rewards as the crucial component of this type of stuttering treatment.     

Binary programming and test design

An algorithmic approach to test design, using information functions, is presented. The approach uses a special branch of linear programming, i.e. binary programming. In addition, results of some benchmark problems are presented. Within the same framework, it is also possible to formulate the problem of individualized testing.I would like to thank my colleagues N. Veldhuijzen, H. Verstralen and M. Zwarts for their suggestions and comments. Furthermore, I would like to thank Professor W. van der Linden, Department of Educational Measurement and Data Analysis, Technological University Twente, for offering facilities at his department; Ellen Timminga of the same department and S. Baas, department of Operational Research at the same University for their efforts in linear programming.     

Optimal sequential designs for on-line item estimation

Replenishing item pools for on-line ability testing requires innovative and efficient data collection designs. By generating localD-optimal designs for selecting individual examinees, and consistently estimating item parameters in the presence of error in the design points, sequential procedures are efficient for on-line item calibration. The estimating error in the on-line ability values is accounted for with an item parameter estimate studied by Stefanski and Carroll. LocallyD-optimaln-point designs are derived using the branch-and-bound algorithm of Welch. In simulations, the overall sequential designs appear to be considerably more efficient than random seeding of items.This report was prepared under the Navy Manpower, Personnel, and Training R&D Program of the Office of the Chief of Naval Research under Contract N00014-87-0696. The authors wish to acknowledge the valuable advice and consultation given by Ronald Armstrong, Charles Davis, Bradford Sympson, Zhaobo Wang, Ing-Long Wu and three anonymous reviewers.     

Effects of serial versus concurrent task sequencing an acquisition, maintenance, and generalization

Effects of serial and concurrent task presentation on skill acquisition, generalization, and maintenance were compared. Two severely retarded females participated. During serial training, items of one response class, tracing, were trained to mastery before those of a second task, vocal imitation. In the concurrent method, training on two different tasks, tracing and vocal imitation, alternated within sessions for fixed periods of time. There were no major differences between the serial and concurrent methods of instruction in the number of steps attained per behavior or in the number of trials required to reach criterion levels of performance. It was found however, that concurrent training resulted in more generalization. Retention results were not consistently related to training method.     

PROGRAMMING GENERALIZATION OF IN-CLASS TRANSITION SKILLS: TEACHING PRESCHOOLERS WITH DEVELOPMENTAL DELAYS TO SELF-ASSESS AND RECRUIT CONTINGENT TEACHER PRAISE

We investigated a self-management intervention package that taught preschoolers with developmental delays to self-assess performance of targeted skills and to recruit teacher praise. Self-assessment alone resulted in increases in active engagement across all participants during the training sessions, but generalization to classrooms was sporadic and short-lived. When recruitment of contingent praise was added to the training package, treatment effects generalized to the participants' classrooms and teachers' rates of contingent praise increased. The implications of combining self-assessment and recruitment of contingent teacher praise are discussed.     

A parametric characterization of mean–variance efficient solutions for general feasible action sets

In this paper we show how the mean–variance efficient solution problem may be converted to a parametric efficient solution problem with objective functions affine in the mean and second moment of the random variable of concern. Some potential uses are considered, the relationship with some other work is examined and two potential applications are given. © 1998 John Wiley & Sons, Ltd.     

Compact integer-programming models for extracting subsets of stimuli from confusion matrices

This paper presents an integer linear programming formulation for the problem of extracting a subset of stimuli from a confusion matrix. The objective is to select stimuli such that total confusion among the stimuli is minimized for a particular subset size. This formulation provides a drastic reduction in the number of variables and constraints relative to a previously proposed formulation for the same problem. An extension of the formulation is provided for a biobjective problem that considers both confusion and recognition in the objective function. Demonstrations using an empirical interletter confusion matrix from the psychological literature revealed that a commercial branch-and-bound integer programming code was always able to identify optimal solutions for both the single-objective and biobjective formulations within a matter of seconds. A further extension and demonstration of the model is provided for the extraction of multiple subsets of stimuli, wherein the objectives are to maximize similarity within subsets and minimize similarity between subsets.