This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R[squared] contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria…

Problems encountered in teaching multiple linear regression forecasting are described, and methods of dealing with such problems are outlined. Problems discussed include: (1) the irrelevance of instructional exercises, (2) data base sizing as a major area of regression "unreality", (3) difficulties in locating or using actual data, (4) neglect of…

The importance of the method given in the body of this article is that it presents Tukey's test in a greatly simplified form for the applied researcher. (Author)

Hierarchial causal models are described as pictorial representations of multiple regression equations. These models are particularly helpful for three reasons: (1) the formulation of problems in a path analytic framework forces a degree of explicitness that is often not present in research reports that rely solely on regression; (2) they provide a…

A relatively new area of psychological investigation is the identification of biographical and psychological variables which contribute to an individual's decision to move from or to stay in a geographical area. This study is an attempt to utilize biographical and psychological data on 50 college students in a multiple linear regression to predict…

Correlation matrices involving linear dependencies are common in educational research. In such matrices, there is no unique solution for the multiple regression coefficients. Although computer programs using iterative techniques are used to overcome this problem, these techniques possess certain disadvantages. Accordingly, a modified Gauss-Jordan…

Illustrates the use of multiple regression analysis for computing conditional probabilities of occurrence of events based on the functional relationship between a dependent response variate and one or more independent predictors. (RC)

Commentary is presented on the preceding articles in this issue of the journal. Critical commentary is made article by article, and some general recommendations are made. (See TM 503 664 through 670). (JKS)

The problem of selecting regression variables using cost criteria is considered. A method is presented which approximates the optimal solution of one of several criterion functions which might be employed. Examples are given and the results are compared with the results of other methods. (Author/JKS)

Researchers have generally assumed overall job satisfaction to be an additive function of weighted job satisfaction facet scores. This paper considers the linear compensatory model as well as two nonlinear alternatives. Available from: Ramon J. Aldag, University of Wisconsin, 1155 Observatory Drive, Madison, Wisconsin 53706. (Author)

The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative…

The causal model approach used in this study tested the cumulative hierarchical structure of the six major taxonomic levels of Bloom's Taxonomy by measuring the strengths of the linear relationships ( links'') between levels. (Editor)