Question 1

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. 
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. C

Question 2

A factor correlation matrix is produced from an orthogonal rotation.

Accepted Answer

A) True 
 B)False  False

Question 3

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

Accepted Answer

A) True 
 B)False  True

Question 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.

Accepted Answer

A) True 
 B)False

Question 5

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

Accepted Answer

A) True 
 B)False

Question 6

Oblique rotation results in factors being uncorrelated with each other.

Accepted Answer

A) True 
 B)False

Question 7

Varimax is the most commonly used oblique rotation procedure.

Accepted Answer

A) True 
 B)False

Question 8

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 9

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

Accepted Answer

A) True 
 B)False

Question 10

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). 
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).

Question 11

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

Accepted Answer

A) True 
 B)False

Question 12

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

Accepted Answer

A) True 
 B)False

Question 13

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. 
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.

Question 14

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

Accepted Answer

A) True 
 B)False

Question 15

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. 
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.

Question 16

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

Accepted Answer

A) True 
 B)False

Question 17

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 18

Principle components analysis analyzes covariance.

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

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

In exploratory factor analysis, the goal is to:

A factor correlation matrix is produced from an orthogonal rotation.

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

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.

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

Oblique rotation results in factors being uncorrelated with each other.

Varimax is the most commonly used oblique rotation procedure.

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

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

Confirmatory factor analysis often used to:

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

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

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

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

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:

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

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

Principle components analysis analyzes covariance.

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

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

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Discriminant Analysis

Logistic Regression

Filters

Exam 9: Factor Analysis

In exploratory factor analysis, the goal is to:

A factor correlation matrix is produced from an orthogonal rotation.

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

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.

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

Oblique rotation results in factors being uncorrelated with each other.

Varimax is the most commonly used oblique rotation procedure.

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

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

Confirmatory factor analysis often used to:

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

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

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

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

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:

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

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

Principle components analysis analyzes covariance.

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

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

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Discriminant Analysis

Logistic Regression

Filters