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| Higher-Order Factor Analysis |
 
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| df_txst posted on Sunday, February 18, 2018 - 7:06 pm
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I conducted an EFA in SPSS and found a four-factor solution: shape = 2 factors, color = 1 factor, size = 1 factor.
The 2 factors that explain shape perfectly load separately as regular and reverse-coded items. When I calculate the to scales separate, the correlation between them is low and each of their correlations with the combined scale is high. Good right? So I'm assuming if I had the two scales in the model and specified them to load on a higher-order SHAPE latent factor, then that loading would work similar. Is this the best approach to use? and If so, how exactly to is specify this in Mplus?
Btw: I'm ultimately trying to run a path analysis from shape to color to size. |
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I am not sure why you would specify a higher-order factor behind the two scales if they have a low correlation - factors are supposed to capture correlations.
Chapter 5 in the UG shows how to specify higher-order factor analysis. With only 2 first-order factors, their loadings need to both be fixed. |
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