Question

1. Principle components analysis analyzes covariance.

T

F

2. Principal components analysis is usually the preferred method of factor extraction, especially when the focus of an analysis searching for an underlying structure is explanatory.

T

F

3. Kaiser’s rule states that only those components in principal components analysis whose eigenvalues are greater than 1 should be retained.

T

F

4. An eigenvalue is defined as the amount of total variance explained by each factor, with the total amount of variability in the analysis equal to the number of original variables in the analysis.

T

F

5. A scree plot is a graph of the magnitude of each eigenvalue (vertical axis) plotted against its ordinal numbers (horizontal axis).

T

F

6. A general rule of thumb is to retain the factors that account for at least 70% of the total variability.

T

F

7. A final criterion for retaining components is the assessment of model fit.

T

F

Answer 1

Principle components analysis analyzes covariance.

False (It analyses variance)

Principal components analysis is usually the preferred method of factor extraction, especially when the focus of an analysis searching for an underlying structure is explanatory.

True

Kaiser’s rule states that only those components in principal components analysis whose eigenvalues are greater than 1 should be retained.

True

An eigenvalue is defined as the amount of total variance explained by each factor, with the total amount of variability in the analysis equal to the number of original variables in the analysis.

True

A scree plot is a graph of the magnitude of each eigenvalue (vertical axis) plotted against its ordinal numbers (horizontal axis).

True

A general rule of thumb is to retain the factors that account for at least 70% of the total variability.

True

7. A final criterion for retaining components is the assessment of model fit.

True