The assumption of a reliable instrument and other pitfalls to avoid when considering the reliability of data.

The purpose of this article is to help researchers avoid common pitfalls associated with reliability including incorrectly assuming that (a) measurement error always attenuates observed score correlations, (b) different sources of measurement error originate from the same source, and (c) reliability is a function of instrumentation. To accomplish our purpose, we first describe what reliability is and why researchers should care about it with focus on its impact on effect sizes. Second, we review how reliability is assessed with comment on the consequences of cumulative measurement error.

Tools to support interpreting multiple regression in the face of multicollinearity.

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights.

Applying the bootstrap to the multivariate case: bootstrap component/factor analysis.

The bootstrap method, which empirically estimates the sampling distribution for either inferential or descriptive statistical purposes, can be applied to the multivariate case. When conducting bootstrap component, or factor, analysis, resampling results must be located in a common factor space before summary statistics for each estimated parameter can be computed. The present article describes a strategy for applying the bootstrap method to conduct either a bootstrap component or a factor analysis with a program syntax for SPSS.