Summary 1ZM31 Multivariate Data Analysis by Hair [Distinction Level] Questions and Answers

Types of Multivariate Techniques - -1. Principal components and common factor analysis 2. Multiple regression and Multiple correlation 3. Multiple discriminant analysis and logistic regression 4. Canonical correlation analysis 5. Multivaiate analysis of variance covariance 6. Conjoint analysis 7. Cluster analysis 8. Perceptual mapping, AKA Multidimentional scaling 9. Correspondence Analysis 10. Structural equation modeling and confirmatory factor analysis -Factor analysis - -Analyzing interrelationships among large number of variables and to explain those variables in terms of their common underlying dimensions (factor) Objective: condensing information into a smaller set with minimal loss of information -Multiple regression (MR) - -Single metric DV or IV related to 2+ metric IVs Objective: predict the changes in DV in response to changes in IVs Most achieved through "least squares" Ex: DV=sales. IVs: adv. expense, #of salespeople, and # of stores -(Multiple) Discriminant analysis (MDA or DA) and Logistic regression - -MDA- when DV is nonmetric (dichotomous - male vs female OR mulichotomous - high, medium, & low) and IVs are metric. MDA Objective- understand group differences and be able to predict belonging to a certain group based on IVs. Logistic regression- combining MDA and multiple regress