In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…

Every set of alternate weights (i.e., nonleast squares weights) in a multiple regression analysis with three or more predictors is associated with an infinite class of weights. All members of a given class can be deemed "fungible" because they yield identical "SSE" (sum of squared errors) and R[superscript 2] values. Equations for generating…

In a multiple regression analysis with three or more predictors, every set of alternate weights belongs to an infinite class of "fungible weights" (Waller, Psychometrica, "in press") that yields identical "SSE" (sum of squared errors) and R[superscript 2] values. When the R[superscript 2] using the alternate weights is a fixed value, fungible…

Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time,…

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among "n" objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors.…

A procedure that utilizes the sample multiple correlation to form a lower bound for the level of predictive precision of a fitted regression equation is suggested. The procedure is shown to yield probability statements which are true at determinable rates. (Author/JKS)

The underlying statistical models for multiple regression analysis are typically attributed to two types of modeling: fixed and random. The procedures for calculating power and sample size under the fixed regression models are well known. However, the literature on random regression models is limited and has been confined to the case of all…

Kruskal has proposed two modifications of monotone regression that can be applied if there are ties in nonmetric scaling data. In this note Kruskal's conjecture that his algorithms give the optimal least squares solution of these modified monotone regression problems is proven. Also, an additional approach is proposed. (Author/JKS)

By applying a general procedure for analyzing a correlation coefficient into components, the lens model equation is extended to (a) analyze the effects of different types of variation and (b) analyze the relations between judgmental systems that are not based on the same set of cues. (Author)

A measure of multiple rank correlation is proposed for the situation of no tied observations in the variables. The measure is a weighted average of two squared Kendall taus. The measure is equivalent to one proposed by Moran. (Author/JKS)

It is known that for trivariate distributions, if two correlations are fixed, the remaining correlation is constrained. If just one is fixed, the remaining two are constrained. Both results are extended to the case of a multivariate distribution. (Author/JKS)

A method is discussed which extends canonical regression analysis to the situation where the variables may be measured as nominal, ordinal, or interval, and where they may be either continuous or discrete. The method, which is purely descriptive, uses an alternating least squares algorithm and is robust. Examples are provided. (Author/JKS)

Cross-validation procedures are proposed as a supplement to significance testing for external analysis of models relating stimulus preferences to known multidimensional scale values (Preference models). Examples of the usefulness of the validation procedures are provided. (Author/JKS)