Field Ch. 18 - Exp. Factor Analysis Flashcards
Why use Exploratory Factor Analysis?
to measure latent variables
3 uses for EFA and PCA
- understand structure of a set of variables
- construct a questionnaire that measures latent variable
- reduce data set & maintain meaningful data (identify & combine colinear variables)
What is Kaiser-Meyer-Olkin?
measure of sample adequacy
.50 = adequate .90+ = marvellous
How do you find sample adequacy (KMO) for individual variables?
- run an Anti-image matrix
- diagonals should be > .50
- off-diagonals should be very small
What is Common Variance?
amount of variance shared among set of items
What is Communality?
proportion of common variance found in a particular variable
- h^2
- between 0 and 1
What is Unique Variance?
any portion of variance that’s not common
What are eigenvalues?
they represent the total variance explained by a given component
> 0 = top notch
close to or < 0 = bad news: multicollinearity
(found in ‘Total Variance Explained’ table)
What are eigenvectors?
they represent the weight for eigenvalues (strength of correlation between item and factor/component)
“correlation of item 1 with component 1 is…”
(found in ‘Component Matrix’ table)
After analysis, which factors do you retain?
- any factors with eigenvalue > 1
or - factors to the left of the scree plot elbow
Why use rotation?
to improve interpretability
Why use orthogonal rotation?
it assumes factors are independent or uncorrelated
Varimax
Why use Oblique rotation?
when factors aren’t independent and are correlated
SPSS step 1: outer menu choices
Analyze > Dimension Reduction > Factor
SPSS step 2: ‘Descriptives’ button
- initial solution
- coefficients
- sig levels
- anti-image
- KMO & Bartletts
(less about memorizing and more about familiarity with boxes to check during analysis)