Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

In some linear regression problems samples may be unwittingly drawn from a heterogeneous population. The exploratory procedure described in this paper can be used to detect heterogeneity in a regression problem associated with the intercept. (Author)

Forty studies of mailed surveys are reviewed in terms of 11 variables which affect response rates: sponsorship, respondents, salience, follow-up contacts, incentives, length, anonymity, personalization, deadline, types of appeals, and postage. (BW)

Although methods for using ordinary least squares regression computer programs to calculate a ridge regression are available, the calculation of a stepwise ridge regression requires a special purpose algorithm and computer program. The correct stepwise ridge regression procedure is given, and a parallel FORTRAN computer program is described.…

Examines the consequences of regarding Deviation Quotients (DQs) as estimates of Full Scale IQs. Offers a new formula and shows the increase in error to be negligible for short forms with typical validities, which supports linear scaling as an acceptable alternative to linear regression. (LLL)

Computed correlations between the subscales of the Wechsler Intelligence Scales for Children-Revised (WISC-R) and the Peabody Picture Vocabulary Test-Revised (PPVT-R) for 72 male deliquents. Significant relationships were obtained for 10 subtests. The signficance and the implications of this relationship were discussed. (JAC)

Background: An extensive body of researches has favored the use of regression over other parametric analyses that are based on OVA. In case of noteworthy regression results, researchers tend to explore magnitude of beta weights for the respective predictors. Purpose: The purpose of this paper is to examine both beta weights and structure…

This paper presents a method designed to assist practitioners in the interpretation of the practical significance of a statistically significant logistic regression coefficient is presented. To avoid the interpretation problems encountered when using the traditionally reported change in either the log odds or odds values, this method centers the…

This study examined research in training and development to determine effect size reporting practices. It focused on the reporting of corrected effect sizes in research articles using multiple regression analyses. When possible, researchers calculated corrected effect sizes and determine if the associated shrinkage could have impacted researcher…

Prior to conducting a statistical analysis, sufficient data screening methods should be used for all research variables to identify miscoded, missing, or otherwise messy data. The primary purpose of these exercises was to demonstrate the role of data screening techniques and their potential to improve the performance of statistical methods. A…

This paper reviews issues involved in converting continuous variables to nominal variables to be used in the OVA techniques. The literature dealing with the dangers of dichotomizing continuous variables is reviewed. First, the assumptions invoked by OVA analyses are reviewed in addition to concerns regarding the loss of variance and a reduction in…

Linear regression examines the relationship between one or more independent (predictor) variables and a dependent variable. By using a particular formula, regression determines the weights needed to minimize the error term for a given set of predictors. With one predictor variable, the relationship between the predictor and the dependent variable…

A method for the least squares regression of one squared variable on a second squared variable when the relationship between the original variables is linear is given. The problem arises in multidimensional scaling algorithms. (Author/JKS)

The solution of weakly constrained regression problems typically requires the iterative search, in a given interval, of a point where a certain function has a zero derivative. This note deals with improved bounds for the interval to be searched. (Author)

Used Johnson's transformation (N. Johnson, 1978) with approximate test statistics to test the homogeneity of simple linear regression slopes in the presence of nonnormality and Type I, Type II or complete heteroscedasticity. Computer simulations show that the proposed techniques can control Type I error under various circumstances. (SLD)