Exam 10: Multivariate Methods of Marketing Research I: Factor, cluster, and Discriminant Analyses

Among the most common uses of cluster analysis in marketing are segmenting customers and products.

The first thing the clustering routine does is

The MinEigen criterion imposed by computer programs retains only those factors with eigenvalues greater than 1.

When interpreting a factor analysis,factors with eigenvalues slightly greater than 1

When researchers want to divide a set of items into a known number of clusters,the clustering technique that should be used is _______________ clustering.

To use cluster analysis,researchers must choose either a distance metric or a clustering criterion.

In dependence methods

_______________ is a collection of methods that test whether a set of means are the same.

_______________ tells us which cases,or people,or objects are similar and how they should be grouped.

Loadings of -1 or 1 mean the factors are _______________ with those variables.

In factor analysis,the _______________ represents how much variance a factor explains relative to how much it would be expected to explain by chance alone,that is,on the average.

Variables that have drastically different ranges can be standardized for cluster analysis through use of a z-transform,which gives all of the variables equal ranges.

In the factor analysis output,the proportion refers to

Discriminant analysis and factor analysis are examples of interdependence methods.

The very purpose of _______________ is to gauge just how much redundancy there is in a set of variables and to assess which questions or variables best align with others and then to group them together.

Discuss the meaning of "eigenvalue" and the use of these within factor analysis.

Discuss the similarities and differences between factor and cluster analyses.

In the distance or dissimilarity matrix for a cluster analysis using squared Euclidean distance,the larger the number

A _______________ matrix categorizes correct and incorrect predictions.

The principal components methodology used in discriminant analysis determines the values in the linear combination that explains as much variance between correlation matrices as possible.