Covering models assume that a point is covered if it is within a certain distance from a facility and not covered beyond that distance. In gradual cover models it is assumed that a point is fully covered within a given distance from a facility, then cover gradually declines, and the point is not covered beyond a larger distance. Gradual cover models address the discontinuity in cover which may not be the correct approach in many situations. In the stochastic gradual cover model presented in this article it is assumed that the short and long distances employed in gradual cover models are random variables. This refinement of gradual cover models provides yet a more realistic depiction of actual behavior in many situations. The maximal cover model based on the new concept is analyzed and the single facility location cover problem in the plane is solved. Computational results illustrating the effectiveness of the solution procedures are presented.

Generalizability theory is a measurement theory that provides a framework for examining the dependability of behavioral measurements. When limited resources are available determining the appropriate number of conditions to use in a measurement design is not a simple task. This paper presents a methodology for determining the optimal number of observations to use in a measurement design when resource constraints are imposed.

This paper presents an efficient search procedure for determining the optimal number of observations of facets in a design that maximize generalizability when resource constraints are imposed. The procedure is illustrated for a three- and four-facet, fully crossed design, and extensions are presented for other design configurations.

Abstract. Many students in quantitative business courses are struggling. One technique designed to support such students is Supplemental Instruction (SI), which is most popular in the science, technology, engineering, and mathematics (STEM) disciplines. In this paper, we show the positive impact of SI on student performance in two bottleneck business courses in a large university. Our evaluation results establish that (i) SI has a statistically significant effect on students' likelihood of passing both courses (after controlling for background variables), (ii) SI is more helpful for students identified as at risk than for those who are not, and (iii) it is important to consistently attend SI sessions for greater success. We also present models to predict consistent student attendance based on background factors with 90% accuracy and conclude with a brief qualitative study about students' self-perception of SI and the professional development attained by SI leaders.