Creating Index of Multiple Variables using Principal Component Analysis (PCA) in 6 MINUTES

This guide shows you how to make an index of multiple variables using Principal Components Analysis PCA in Eviews. It's like a step-by-step manual, making it easy to understand and follow.
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[@lucifergaming2229]

Hello Dr. Noman, I hope you're doing well. Recently, I watched your video of PCA. I constructed 4 indices, in which one index value is 0.3382. So, now my question is, am i supposed to carry on with this exceptional result or can you provide me the link to any other research(es) whose results are like this which will support my this outcome. Or if you can provide me any information for supporting this result. It would be a great help to me. I hope to see your kind response ASAP. thank you.

[@avs3488]

Thankyou so much.

[@nomanarshed]

Welcome!

[@demir5406]

Thank you very much, could you prepare a video for creating index based on entropy method?

[@nomanarshed]

th-cam.com/video/kr4_u4lX9II/w-d-xo.html

[@demir5406]

@@nomanarshed thank you so much

[@nadjlabouchouk8824]

If we want to construct weighted index we choose symetric weight or loading weight please help me

[@nomanarshed]

It depends on theory and requirement. For symmetric weights ideally the data has same units.

[@benbrahimhatim7552]

please how can we obtain the contribution or weights of each variable in the index

[@nomanarshed]

See factor loadings generated after factor analysis

[@avs3488]

if we have many variables under different cateogories can we do pca for each category and then run the panel regression?

[@nomanarshed]

Its upto you if you want to load them in one index or make multiple indices, For this you need to study the concept of discriminant and convergent validity. If all the items are highly correlated with each other loading them in one index is better as it would remove multicollinearity

[@avs3488]

@@nomanarshed Sir forexample like making 1 index with economic variables and then other index with social variables and then applying regression with them on one variable is that possible

[@nomanarshed]

Yes then these 2 index variables can be used in regression as IVs only if they are not highly correlated with each other.

[@avs3488]

@@nomanarshed thankyou sir

[@saman0071234]

Respected Sir
How we can create index for variables on binary scale

[@nomanarshed]

since all of your data is in same scale (binary) you can average them to form an index unless you want unequal weights in that case you can use factor analysis.

[@muhammadshahzadsardar5136]

Thanks for this very informative video

[@nomanarshed]

Glad it was helpful!

[@yasmeensameh1367]

If the index results are less than 0 How I could deal with it?

[@nomanarshed]

Index is a normalized data where zero means mean value and negative values means that the paticular observation is less than mean.

[@yasmeensameh1367]

@@nomanarshed as far I know the index should be between 0-1 or from 1-100 right?
But the PCA results are not on this ranges what should I do?

[@nomanarshed]

It is a normalized variable not a index which ranges from - infinity to infinity

[@yasmeensameh1367]

@@nomanarshed so I can use it even if it is less than 0

[@nomanarshed]

Yes it is providing us a linear combination of the data measing latent variable in continuous form. If its values cannot be than zero then the error term of regression should also be not zero as it is also an index as a resultant of linear combination of dv and ivs

[@revatibehera2240]

You might be a great statistician, but not at all even a basic level teacher. !

[@nomanarshed]

Yes this is a research related channel. I am learning and your guidance will be helpful.