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Hi All,
Is there a technique other than PCA (Principal Component Analysis) to decide whether it is somehow reasonable to group together , aka " collapse" several non-continuous ( Categorical, Likert, Ordinal, etc. ) into a single one. The idea is, of course, to lose only a negligible amount of explanatory/predictive power by doing this. PCA ( Possibly Latent Component Analysis --LCA -- as well ) collects groups through the use of the Covariance matrix.
Questions:
1): Are there other basis/justifications for collapsing several
2) To what extent does PCA generalize into non-continuous variables?
Thanks.
Is there a technique other than PCA (Principal Component Analysis) to decide whether it is somehow reasonable to group together , aka " collapse" several non-continuous ( Categorical, Likert, Ordinal, etc. ) into a single one. The idea is, of course, to lose only a negligible amount of explanatory/predictive power by doing this. PCA ( Possibly Latent Component Analysis --LCA -- as well ) collects groups through the use of the Covariance matrix.
Questions:
1): Are there other basis/justifications for collapsing several
2) To what extent does PCA generalize into non-continuous variables?
Thanks.