Timeline for PCA with variables in different Likert scales
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Mar 15, 2023 at 19:10 | comment | added | Alexis | PCA does not analyze data, it typically analyzes the correlation matrix of the data ($\boldsymbol{R}$) which is insensitive to the distributional forms of the data and invariant to linear transformations of the data. Occasionally PCA analyzes the variance covariance matrix of the data ($\boldsymbol{\Sigma}$), but this is typically done when $\boldsymbol{\Sigma} \approx \boldsymbol{R}$, so again… | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Jul 22, 2016 at 23:59 | comment | added | maya | When I run the pca when all variables range 0-6 and then run again with some variables have been manipulated to range from 0-6 and others 0-12 I get two different models with different interpretations (eg number of components to retain and loading loadings) are different. this makes me believe that even with standardization that happens with the correlation matrix, the range of the Likert scale still matters (which I want). | |
| Jul 22, 2016 at 23:58 | comment | added | maya | What if I want the variables to be weighted (so that certain variables are more important)? Eg I want to keep the fact that certain Likert scales go to 6 while others go to 12. If I run pca based on a correlation matrix (which standardizes the data) does this get rid of this weighting? | |
| Jul 21, 2016 at 22:16 | history | answered | Pere | CC BY-SA 3.0 |