Exploratory Factor Analysis Quiz

What is the primary goal of exploratory factor analysis?

• To increase the number of variables in a dataset

• To uncover hidden structures in data by grouping correlated variables

• To eliminate all correlations between variables

• To predict future data trends

In the context of EFA, what is a 'factor'?

• A visible variable that can be directly measured

• A method to increase data variance

• A hidden variable that influences observed variables

• A statistical error in data analysis

Which personality traits were grouped under the factor 'extraversion' in the example?

• Hardworking and dutiful

• Warm-hearted and helpful

• Outgoing and sociable

• Courteous and reserved

What is the purpose of the correlation matrix in EFA?

• To calculate the mean of all variables

• To determine the strength of correlation between variables

• To eliminate outliers from the dataset

• To predict future trends in data

How is the number of factors determined using the eigenvalue criterion?

• By counting the number of eigenvalues greater than zero

• By counting the number of eigenvalues greater than one

• By counting the number of eigenvalues less than one

• By counting the number of eigenvalues equal to zero

What does a factor loading indicate in EFA?

• The number of factors in a dataset

• The total variance explained by all factors

• The correlation between a variable and a factor

• The number of variables in a dataset

Why is rotation used in EFA?

• To eliminate all correlations between variables

• To increase the number of factors

• To ensure variables load as high as possible on one factor and low on others

• To decrease the variance explained by factors

Which rotation method is commonly used in EFA?

• Orthogonal rotation

• Varimax rotation

• Oblique rotation

• Direct rotation

What is the role of communalities in EFA?

• To measure the total variance of a single variable

• To determine the number of factors needed

• To indicate how much variance of the variables can be explained by the factors

• To calculate the mean of all variables

What is the final step in interpreting EFA results?

• Assigning variables to factors based on the rotated component matrix

• Calculating the mean of all variables

• Eliminating all correlations between variables

• Predicting future data trends