1
$\begingroup$

I have obtained responses on around 48 items measuring employer attractiveness. The goal of my analysis is to cluster respondents according to these employer attractiveness dimensions. I plan to reduce the dimensionality with PCA and also confirm the structure with CFA before getting to cluster analysis. 48 items were measured in forced 4-point Likert scale, but the data I obtained for analysis was transformed by another research to binomial level - important or not important (0 or 1 values). I was wondering what effect that can have on PCA and cluster analysis that these data are binomial. Can these analysis techniques be executed on binomial data?

$\endgroup$
2
$\begingroup$

Using a "common sense" approach the trasformation from 4 level variables into dicotomic variables have clearly reduced the richness of information expressed in each variable, so I would expect more difficulty in extracting conclusions from statistical analyses on such dataset than if you would have analyzed original data. Considering the topic you have addressed, PCA/Factor analysis and Cluster analysis for binary data, I would give a look to: * Homogeneity analysis: (see http://www.r-bloggers.com/finding-patterns-amongst-binary-variables-with-the-homals-package/), a sort of factor analysis that can be used to analyze binary data. * The mona function in R cluster package: a cluster analysis tailored for binary data (see Cluster analysis of boolean vectors in R)

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.