PCA and EFA

Question 1

What is the aim of EFA

Answer 1

to understand the variable structure of a dataset, at the expense of original item variences

Question 1

What is the interpretation of loadings in EFA

Answer 1

loadings represent relative contributions of factors to the individual item responses

Question 2

What types of rotation can be used for EFA

Answer 2

orthogonal or oblique

Question 3

what is the error term in EFA

Answer 3

leftover item variences

Question 4

What method would we use if we want to condense a large number of items in a more convenient set of dimentions

Answer 4

PCA

Question 5

What method would we use if we wanted to uncover explanatory relationships behind item responses

Answer 5

PCA

Question 6

what kind of rotation is used in PCA

Answer 6

Orthogonal. default varimax

Question 7

ways to decide how many factors we want for PCA

Answer 7

Eigenvalue>1 or parralel analysis

Question 8

How does parallel analysis work

Answer 8

Uses bootstrapping to resample the data and compares to the oberved data. Factors with Eigenvalues larger than random are kept

Question 9

What function is used for parallel analysis

Answer 9

fa.parallel()