Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…

School-effectiveness research is constrained by ambiguous factors of effectiveness and a lack of theory. This paper presents findings of a study that used simulation to improve school-effectiveness theory. Simulation is also used to explore the direct effects of schools on individual learning. After introducing simulation models, the paper…

Diagnostic score reports linking assessment outcomes to instructional interventions are one of the most requested features of assessment products. There is a body of interesting work done in the last 20 years including Tatsuoka's rule space method (Tatsuoka, 1983), Haertal and Wiley's binary skills model (Haertal, 1984; Haertal & Wiley, 1993),…

Web-based statistical instruction, like all statistical instruction, ought to focus on teaching the essence of the research endeavor: the exercise of reflective judgment. Using the framework of the recent report of the American Psychological Association (APA) Task Force on Statistical Inference (Wilkinson and the APA Task Force on Statistical…

Describes a circulation model for academic research libraries which uses the mixed Poisson model, incorporating ageing of library materials, to predict future use of monographs and to suggest weeding procedures based on frequency of circulation. Longitudinal studies are examined and statistical details are appended. (Author/LRW)

Assessing fit of psychometric models has always been an issue of enormous interest, but there exists no unanimously agreed upon item fit diagnostic for the models. Bayesian networks, frequently used in educational assessments (see, for example, Mislevy, Almond, Yan, & Steinberg, 2001) primarily for learning about students' knowledge and…

In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…