7: Factor Analysis Mechanics, Validity and Reliablity Flashcards
Which output matrices follow principal axis factoring and oblimin rotation?
Factor, pattern and structure matrices.
Which output matrices follow principal components factoring and varimax rotation?
Component and rotation component matrices.
Which output matrices follow principal axis factoring and varimax rotation?
Factor and rotated factor matrices.
Describe simple indicators.
Highs indicate only one factor.
What is acquiescence bias?
Where some (e.g. uninterested) participants response positively to all items, no matter how they are worded.
Which factor extraction includes the full correlation matrix, with error?
Principal component analysis.
Which factor extraction is only interested in the common variance between factors?
Principal axis factoring.
What is the extraction communality?
The estimate of how much is shared by one variable and the extracted factors.
What is the initial communality?
1.0 for PCA and estates for FA.
How is the initial communality estimated for factor analysis?
Using the squared multiple correlation between as given variable and all other variables in a set, and updating it each time through the iteration process to find the optimal extraction.
In FA, when is a large sample needed?
To analyses complex patterns, more items and less clear factors.
What does it mean if a variable has low SMC/initial communality?
It is an outlier and another similar variable must be found, or this one must be dropped.
What does it mean if a variable has low MSA?
It does not share enough variance to be a reliable factor, and more must be found, or this one must be dropped.
What does it mean if a variable has low KMO?
The whole matrix has poor definition of factors and the solution will be poor and unstable.
What do individual cells in the AIC represent?
The negative of the partial correlation of the respective row and column with all other variables partialled out.
How many items should a factor be measured by?
At least 3.
What does MSA summarise?
The AIC elements for each variable.
Is MSA is near 1, what does that mean?
A variable does covary with at least 2 others, and so it belongs.
What does KMO summarise?
The individual items MSAs.
What should you do if KMO is middling to low?
Look at individual MSAs to see if the sample variable is wrong.
What is in a reproduced correlations matrix?
The implied correlations in a factor model and the residuals.
What do small residuals in reproduced correlations mean?
They are close to the original correlations and the factor model captures the constructs well.
What do large residuals in reproduced correlations mean?
Some aspect of the data has not been captured by the factor model.
What 3 things may cause large residuals?
Wrong interpretation, too many unreliable measures, or a methodological problem distorting the solution.
When is PAF most appropriate?
When trying to understand an underlying construct that cannot be directly measured.
When is PCA most appropriate?
When trying to reduce a data set to a smaller number of independent measures it when things of interest can be measured directly.
What are factor scores?
Estimates of what the “score” might be on some latent variable.
What analysts commonly used factor scores?
Principal components.
What is content validity?
Does the variable measure what we think it does?
How can we check content validity?
Face validity and manipulation checks.
How is predictive validity usually measured?
Regression.
How is convergent and discriminant validity usually measured?
Correlations.
How do factor loadings act as a measure of reliability in a simple latent variable model?
When squared, they capture how much of the variance of the factors are predicted from the latent variable they measure.
What can artificially inflate Cronbach’s alpha?
Using a large number of items and items that are only trivially different.
