Title: Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
Abstract:Linear combinations of dependent variables that maximize separation among groups are called discriminant functions, and the study of such functions is useful as follow-ups to conducting a multivariate...Linear combinations of dependent variables that maximize separation among groups are called discriminant functions, and the study of such functions is useful as follow-ups to conducting a multivariate analysis of variance (MANOVA). This chapter begins its discussion with an introduction to MANOVA, and surveys some of its technical basis before demonstrating the technique in R. It follows up with a discussion and demonstration of discriminant function analysis. The chapter explores the need for conducting MANOVA and demonstrates MANOVA in R. When reporting any research finding, an estimate of the effect size should also be provided along with the significance test. The chapter discusses the evaluation of assumptions in MANOVA, and shows how to compute the Box-M test to evaluate the equality of covariance matrix assumption. It also demonstrates how to compute discriminant scores manually.Read More
Publication Year: 2020
Publication Date: 2020-03-27
Language: en
Type: other
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot