This paper is from Session 3E: Multivariate statistics
which comes under Topic 3: Statistics education at the post-secondary level Full topic list
(Friday 7th, 10:30-12:30)
Basic multivariate themes and methods
Presenter
Abstract
Much of science is concerned with finding latent order in a seemingly complex array of variables. Multivariate methods help uncover this order, revealing a meaningful pattern of relationships. To help illuminate several multivariate methods, a number of questions and themes are presented, asking: How is it similar to other methods? When to use? How much multiplicity is encompassed? What is the model? How are variance, covariance, ratios and linear combinations involved? How to assess at a macro- and micro-level? and How to consider an application? Multivariate methods are described as an extension of univariate methods. Three basic models are presented: Correlational (e.g., canonical correlation, factor analysis, structural equation modeling), Prediction (e.g., multiple regression, discriminant function analysis, logistic regression), and Group Difference (e.g., ANCOVA, MANOVA), with brief applications.
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