Exam 20: Discriminant, Factor and Cluster Analysis

Discriminant analysis can only be used for description and not for prediction purposes.

The coefficients that link the factors to the variables are called

A factor is a variable or construct that is not directly observable but needs to be inferred from the input variables.

All of the following are true about factor analysis except

The basic task in cluster analysis is to uncover competing explanations for a causal phenomenon.

A factor score is a measurement of how closely related each input variable is to a derived factor.

In discriminant analysis, with M groups and p predictor variables, the number of discriminant functions is given by

Discriminant analysis involves the maximization of the between-group variance relative to the within-group variance

Which of the following is not true about cluster analysis?

The first factor accounts for more of the variation in the data than the second factor.

Discriminant analysis techniques are used to classify into one of two or more alternate groups based on a set of measurements.

Initial starting points in nonhierarchical clustering is represented by a) cluster membership b) cluster seeds c) cluster centurions d) none of the above

Common factor analysis focuses on shared variance, hence communalities are used in the diagonal of the matrix

The underlying assumption in a discriminant analysis is that the independent variables are assumed to be normally distributed.

Simple Euclidean distance is a common measurement of similarity on a perceptual map.

Each respondent has a factor score on each factor in addition to the respondent's rating on the original variables.

Factor loadings are a measurement of the correlations between the factors and the original variables.

A major advantage of cluster analysis is the availability of standard statistical tests to ensure that the output does not represent pure randomness.