In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…

A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…

(See also TM 501 087, TM 501 088, TM 501 090.)

(See also TM 501 087, TM 501 088, and TM 501 089.)

The purpose of this study was to compare five predictor models, including two least-square procedures, two probability weighting (semi-Bayesian) methods, and a Bayesian model developed by Lindley. (See also TM 501 088, TM 501 089, and TM 501 090) (Author/NE)

(See also TM 501 087, TM 501 089 and TM 501 090.)

This paper discusses MacCrimmon's general theoretical framework for collective decisions, reveals the modeling for collective decisions, and presents selected descriptive research in the general area of collective decisionmaking. The intent is to stimulate research or provide insight that could have practical implications for management…

The theoretical rationales and procedures of five adaptive computer-based instruction models were reviewed: the mathematical model, the regression model, the Bayesian probabilistic model, the testing and branching model, and artificially intelligent instructional systems. Each model is assessed for contrast of methods and forms, identifiable…