After reading this chapter, you should understand . . .
How to classify and select multivariate techniques.
How multiple regression predicts a metric dependent variable from a set of metric independent variables.
How discriminant analysis classifies people or objects into categorical groups using several metric predictors.
How multivariate analysis of variance assesses the relationship between two or more metric dependent variables and independent classificatory variables.
How structural equation modeling explains causality among constructs that cannot be directly measured.
How conjoint analysis assists researchers to discover the most important attributes and levels of desirable features.
How principal components analysis extracts uncorrelated factors from an initial set of variables and how (exploratory) factor analysis reduces the number of variables to discover underlying constructs.
How cluster analysis techniques identify homogenous groups of objects or people using a set of variables to compare their attributes and/or characteristics.
How perceptions of products or services are revealed numerically and geometrically by multidimensional scaling.