Exam 20: Discriminant, Factor and Cluster Analysis

In both principal components analysis and varimax rotation, the factors are constrained to be uncorrelated or geometrically perpendicular.

One rule of thumb in deciding on the number of factors to retain is to include all factors that explain at least 50 percent of the variance.

If the primary purpose is data reduction one would use a) cluster analysis b) factor analysis c) discriminant analysis d) conjoint analysis

Communality is the percent of a variable's variance which contributes to the correlation with other variables or is common to other variables.

Factor loadings and correlations are identical if each variable has its mean subtracted and is divided by its standard deviation.

The percent of variance explained is a summary measurement indicating how much of the total original variance of all the respondents is represented by the factor.

While analyzing and interpreting consumer perception data using factor analysis, a researcher found the factor loading on Factor 1 to be high.However, he could not interpret the factor meaningfully.A probable cause for this situation is computation error or shortsightedness in his interpretation, since a high loading ensures meaningfulness.

Regression and Discriminant analyses are computationally similar

In the hierarchical approach, the commonly used methods are single linkage, complete linkage, average linkage, Ward's method, and the centroid method.

Rotation of factors changes the interpretation of the factors while retaining the principal component patterns of loadings.

The analysis technique used to identify variables that contribute to differences in the a prior defined groups is

Factor is observable that is why it is a variable.

Which of the following statements is not true of Wilks' Lamba? a) it is the ratio of within-group variance to total variance b) it takes values between 0 and 1 c) larger values indicate that group means do not appear to be different d) none of the above

An attractive feature of principal components analysis is the easy interpretability of the factors.

The amount of variance in the original variables that is associated with a factor is represented by

In discriminant analysis, predictors with a large coefficient contribute more to the discriminating power of the function.

For discrimination to be based on all predictors the most appropriate function estimation method is a) sequential b) direct c) pooled d) stepwise

If a clustering procedure starts with one cluster and subdivides until all objects are in their own single-object cluster, the procedure is termed top-down hierarchical clustering.

Factor analysis is usefully employed when it is desirable to combine several questions, thereby creating a new variable.

The simple correlation between the independent variable and the discriminant function is represented by a) discriminant loading b) structure correlation c) total correlation matrix d) centroid