Question 1

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

Accepted Answer

A) True 
 B)False

Question 2

Factor analysis is used to describe the underlying structure that explains a set of variables.

Accepted Answer

A) True 
 B)False

Question 3

In exploratory factor analysis, the goal is to:

Accepted Answer

A)   Describe data by grouping together variables that are correlated.
B)   Summarize data by grouping together variables that are uncorrelated.
C)   Describe and summarize data by grouping together variables that are correlated.
D)   Test a theory about latent processes that might occur among variables.E) A) and D)
F) None of the above C

Question 4

A factor correlation matrix is produced from an orthogonal rotation.

Accepted Answer

A) True 
 B)False  False

Question 5

The underlying hypothetical (unobservable) variables in factor analysis are called factors.

Accepted Answer

A) True 
 B)False

Question 6

Confirmatory factor analysis often used to:

Accepted Answer

A)   Test a theory about underlying, unobservable processes that might occur among variables.
B)   Confirm or disconfirm a theory post hoc.
C)   Neither (a)  nor (b) .
D)   Both (a)  and (b) .E) A) and D)
F) A) and B)

Question 7

Orthogonal rotation is a rotation of factors that results in factors being correlated with each other.

Accepted Answer

A) True 
 B)False

Question 8

It is recommended that the following two assumptions be evaluated and any necessary transformations be made to ensure the quality of data and improve the quality of the resulting factor or component solution:

Accepted Answer

A)   All variables, as well as all linear combinations of variables, must be normally distributed.
B)   The relationships among all variables must be linear.
C)   The relationships among all pairs of variables must be linear.
D)   Both (a)  and (c)  are correct.E) A) and B)
F) None of the above

Question 9

Oblique rotation results in factors being uncorrelated with each other.

Accepted Answer

A) True 
 B)False

Question 10

Principal components analysis may be used as a variable reducing scheme for further analysis.

Accepted Answer

A) True 
 B)False  True

Question 11

The underlying, mathematical objective in principal components analysis is to obtain:

Accepted Answer

A)   Correlated linear combinations of the original variables that account for as much of the total variance in the original variables as possible.
B)   Uncorrelated linear combinations of the original variables that account for some of the total variance in the original variables.
C)   Uncorrelated linear combinations of the original variables that account for as much of the total variance in the original variables as possible.
D)   Uncorrelated combinations of the original variables that account for as much of the total variance in the original variables as possible.E) None of the above
F) B) and C)

Question 12

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

Accepted Answer

A) True 
 B)False

Question 13

Varimax is the most commonly used oblique rotation procedure.

Accepted Answer

A) True 
 B)False

Question 14

In principal components analysis, only unique variability is analyzed for each observed variable.

Accepted Answer

A) True 
 B)False

Question 15

In factor analysis, unique, shared, and error variability is analyzed for each observed variable.

Accepted Answer

A) True 
 B)False

Question 16

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.

Accepted Answer

A) True 
 B)False

Question 17

Principle components analysis analyzes covariance.

Accepted Answer

A) True 
 B)False

Question 18

When interpreting or naming components, one should pay particular attention to the size and direction of each loading.

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

A bipolar factor refers to a component that contains both high positive and high negative loadings.

Accepted Answer

A) True 
 B)False

Exam 9: Factor Analysis

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

Correct AnswerverifiedShow Answer

Factor analysis is used to describe the underlying structure that explains a set of variables.

Correct AnswerverifiedShow Answer

In exploratory factor analysis, the goal is to:

Correct AnswerverifiedC

A factor correlation matrix is produced from an orthogonal rotation.

Correct AnswerverifiedFalse

The underlying hypothetical (unobservable) variables in factor analysis are called factors.

Correct AnswerverifiedShow Answer

Confirmatory factor analysis often used to:

Correct AnswerverifiedShow Answer

Orthogonal rotation is a rotation of factors that results in factors being correlated with each other.

Correct AnswerverifiedShow Answer

It is recommended that the following two assumptions be evaluated and any necessary transformations be made to ensure the quality of data and improve the quality of the resulting factor or component solution:

Correct AnswerverifiedShow Answer

Oblique rotation results in factors being uncorrelated with each other.

Correct AnswerverifiedShow Answer

Principal components analysis may be used as a variable reducing scheme for further analysis.

Correct AnswerverifiedTrue

The underlying, mathematical objective in principal components analysis is to obtain:

Correct AnswerverifiedShow Answer

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

Correct AnswerverifiedShow Answer

Varimax is the most commonly used oblique rotation procedure.

Correct AnswerverifiedShow Answer

In principal components analysis, only unique variability is analyzed for each observed variable.

Correct AnswerverifiedShow Answer

In factor analysis, unique, shared, and error variability is analyzed for each observed variable.

Correct AnswerverifiedShow Answer

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.

Correct AnswerverifiedShow Answer

Principle components analysis analyzes covariance.

Correct AnswerverifiedShow Answer

When interpreting or naming components, one should pay particular attention to the size and direction of each loading.

Correct AnswerverifiedShow Answer

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

Correct AnswerverifiedShow Answer

A bipolar factor refers to a component that contains both high positive and high negative loadings.

Correct AnswerverifiedShow Answer

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C

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False

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True

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