A Meta-View of Multivariate Statistical Inference Methods in European Psychology Journals

Abstract

We investigated the extent and nature of multivariate statistical inferential procedures used in eight European psychology journals covering a range of content (i.e., clinical, social, health, personality, organizational, developmental, educational, and cognitive). Multivariate methods included those found in popular texts that focused on prediction, group difference, and advanced modeling: multiple regression, logistic regression, analysis of covariance, multivariate analysis of variance, factor or principal component analysis, structural equation modeling, multilevel modeling, and other methods. Results revealed that an average of 57% of the articles from these eight journals involved multivariate analyses with a third using multiple regression, 17% using structural modeling, and the remaining methods collectively comprising about 50% of the analyses. The most frequently occurring inferential procedures involved prediction weights, dichotomous p values, figures with data, and significance tests with very few articles involving confidence intervals, statistical mediation, longitudinal analyses, power analysis, or meta-analysis. Contributions, limitations and future directions are discussed.