Question 1

Statisticians often call the clusters obtained through cluster analysis "latent" because they need to be discerned via analysis and are not directly observable.

Accepted Answer

With cluster analysis,the clusters are formed through analysis and are not defined a priori.

Question 2

Cluster analysis clarifies the underlying structure of multivariate data in a way that makes it the best complement to regression analysis.

Accepted Answer

Factor analysis is the perfect complement because it will reduce a data set to a smaller set of variables that are not highly correlated.

Question 3

Discriminant analysis and factor analysis are examples of interdependence methods.

Accepted Answer

Although factor analysis is an interdependence method,discriminant analysis is a dependence method.

Question 4

Factor analysis can be used for data transformations by identifying covariates that are exactly uncorrelated and make good inputs for dependence methods.

Accepted Answer

Through factor analysis,a large group of variables can be reduced to a smaller set of factors that are not correlated.

Question 5

In hierarchical cluster analysis,once a group of objects is clustered together,they are together for life,i.e.the program never separates them during the analysis.

Accepted Answer

This is one of the disadvantages of hierarchal clustering.It keeps an object in the original group for the duration of the program,even though the cluster might be better off in another cluster.

Question 6

If the eigenvalue of the first factor on a factor analysis is 5.3,this could be interpreted that this first factor is doing the work of about 5.3 of the original variables.

Accepted Answer

The eigenvalue is the proportion of the variance explained relative to the average.

Question 7

Factor analysis tells us which variables are similar to one another and how they should be grouped.

Accepted Answer

Factor analysis provides this information about variables,whereas cluster analysis tells us which cases are similar and how they should be grouped.

Question 8

Varimax rotation involves an oblique rotation of the factors.

Accepted Answer

Varimax rotation involves an orthogonal rotation of the factors.

Question 9

Hierarchical clustering works well if a researcher needs a fixed number of clusters,whereas nonhierarchical clustering is more useful when one needs to compare different clustering solutions.

Accepted Answer

It is just the opposite; nonhierarchical clustering works well if a researcher needs a fixed number of clusters,whereas hierarchical clustering is more useful when one needs to compare different clustering solutions.

Question 10

The principal .reason for rotating a factor analysis solution is to increase the amount of "explained" variance in the variables.

Accepted Answer

The principal reason for rotating a factor analysis solution is to aid in the interpretation of the factors.

Question 11

Varimax rotation in factor analysis will tell researchers the optimal number of factors they should have in a data reduction situation.

Accepted Answer

Researchers have to use their own judgment to determine the number of factors,based on the principal components analysis.The varimax rotation is used after the number of factors has been decided.

Question 12

In dependence models,no variable or variables are designated as being predicted by others,because the researcher is interested in the interrelationships among all of the variables taken together.

Accepted Answer

This is a description of interdependence methods.

Question 13

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

Accepted Answer

Researchers must choose both a distance metric and a clustering technique.

Question 14

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.

Accepted Answer

The purpose of a z-transform is to convert all of the variables to scales with a mean of 0 and standard deviation of 1.

Question 15

In cluster analysis it is especially dangerous if the ranges of the variables are dramatically different.

Accepted Answer

For cluster analysis to be effective,the ranges of the variables cannot be drastically different; otherwise,the variables with the largest range will dominant the solution.

Question 16

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.

Accepted Answer

The principal components methodology is used with factor analysis,not discriminant analysis,and it is to explain variance in the correlation matrix,not between correlation matrices.

Question 17

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

Accepted Answer

The MinEigen criterion retains only those factors with eigenvalues greater than 1 (i.e.,the "bigger than average" factors).

Question 18

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

Accepted Answer

Cluster analysis is excellent for segmentation projects,because the program is able to locate clusters within a data set.

Question 19

One of the primary problems with the principal components analysis of factor analysis is that it can take most of the variance to explain the first factor and leave little variance for other factors to explain.

Accepted Answer

Unfortunately,principal components analysis will try to explain as much of the variance as possible in that first factor,thus sometimes leaving little for other factors.

Question 20

Unlike regression analysis,cluster analysis does not have any independent variables.

Accepted Answer

Cluster analysis does not have any dependent variables; all are independent variables.

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