A common question asked by researchers using regression models is, What sample size is needed for my study? While there are formulae to estimate sample sizes, their assumptions are often not met in the collected data. A more realistic approach to sample size determination requires more information such as the model of interest, strength of the…

This paper shows how the application of simple statistical methods can reveal to students important insights from climate data. While the popular press is filled with contradictory opinions about climate science, teachers can encourage students to use introductory-level statistics to analyze data for themselves on this important issue in public…

In gifted education research, it is common for outcome variables to exhibit strong floor or ceiling effects due to insufficient range of measurement of many instruments when used with gifted populations. Common statistical methods (e.g., analysis of variance, linear regression) produce biased estimates when such effects are present. In practice,…

Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

The logarithmic transformation is a commonly applied procedure in regression analysis when two or more variables have a nonlinear relationship. When the response variable is logarithmically transformed, confidence intervals for conditional means and predictions may actually be wider than their counterparts obtained from the model with the original…

Describes the strengths and weaknesses of a new type of multivariate technique, covariance structure analysis (LISREL). Provides a detailed example which shows how the use of covariance structure analysis can improve research sophistication and theory development in counseling psychology. (Author/ABB)

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable with "multiple" covariates. The performance of RTA is compared to the classical…

The number of regression studies being employed in sex discrimination cases is increasing. The need for caution in the case of multiple regression must be emphasized. Statistical concerns in sex discrimination cases are highlighted. (MLW)

Rectifies the unsatisfactory textbook treatment of the finite-sample proprieties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. Maintains that the bias of the ordinary least squares estimator is determined by the dynamic and correlation effects. (MJP)