Timeline for Retaining second principal component as a single index
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| May 10, 2015 at 3:06 | comment | added | user179313 | Sorry for the confusion. I meant that the bi-directional causality may exist is just conceptually, nothing related to statistical inference. | |
| May 9, 2015 at 1:46 | history | edited | katya | CC BY-SA 3.0 |
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| May 9, 2015 at 1:45 | comment | added | katya | @user179313: why do you 'assume there must be a bi-directional causality' and how does it relate to the decision to use secondary axes with such small proportion of variance explained? ps I apologize for my last question, which I will edit, I was meaning to ask whether they are cross-correlated (not sure what happened there) | |
| May 8, 2015 at 20:39 | comment | added | user179313 | I predicted the scores for 3 components & got the correlation among the components and original items. Correlation between the predicted scores of component 1 and the original items are higher than that of component 2, except the correlation between 2c and predicted scores of component 1. The cross-corr. is generally linear. Now I plan to use the predicted scores of components 2 and 3 as instruments for my variable of interest (IV); I assume there must be a bi-directional causality between my IV & DV. So do you think if it is possible to use components 2 and 3 as instrument variables? | |
| May 8, 2015 at 20:38 | comment | added | user179313 | Thanks, @Katya! Please see above for my another curious question. | |
| May 7, 2015 at 2:16 | history | answered | katya | CC BY-SA 3.0 |