WK 10 Flashcards
What is the key feature that distinguishes factor analysis and principal component analysis?
Latent variables
What do latent variables do?
Explains correlations between measured variables
In PCA what are the observed measures?
The observed measures are independent variables
In PCA what is the component (z)?
The component is the dependent variable
What does PCA explain?
It explains as much variance in the measures as possible
- goal of PCA is to explain/ account for all possible variance by this reduced set
In PCA what are the components?
The components are determinate
In EFA, what are the observed measures?
They are the dependent variables?
In EFA, what is the factor?
It is the independent variable
What does EFA model?
EFA models the relationship between variables
- In factor analysis, we are not concerned by all variance but rather common variance
In EFA, what are indeterminate?
Factors
What are correlations?
standardized covariates
What does EFA try to explain?
Tries to explain patterns of correlations
What does factor analysis have to distinguish?
Factor analysis has to distinguish between the true and unique variance
What is true variance?
True variance is variance common to an item and at least one other item as well as variance specific to an item that is not shared with any other items
What is unique variance?
Variance specific to an item that is not shared with any other items and error variance
What is the issue with unique variance?
We cannot distinguish between unique variance and error variance in the model -> all we know is that it is not shared
What is the error term?
when we see the error term in factor analysis it comprises of var(specific) + var(error) -> It is both legitimate error and the bit of variance in the item that is not shared
What are the assumptions in EFA?
- the residual/ error terms should be uncorrelated
- the residual/errors should not correlate with factor
- relationships between items and factors should be linear