Thank you for this great job. But please what is the difference if I predict directly the id after I got pca results and the way you did. Are they the same way
Thank you very much! can you please explain how one can use that index variable in multivariate analysis? i.e would that index variable be a dependent variable? and if it is, would its be treated as a continuous variable, meaning that one cannot use binary logic regression?
Greetings. Please you generated "id" by using the respective proportions of comp1 and comp2 over the cumulative value for comp2, because it is up to only this level that we have the proportion greater than the mean value of 20%. I clearly* understand you. My question then is, "how do you calculate the "id" (which is the index) when you have only the proportion of comp1 tobe above the mean value/percentage? Would be happy if I can hear from someone on this issue as soon as possible. Thank you.
Many thanks for this video. Very helpful. I want to understand a basic thing. Why is the weighted index value (here the PC1) more than 1? It should ideally be 0-1. Can you please clear my doubt.
Sir, why are we not multiplying weights to variable values. Here just you have taken linear combination of pc1 and pc2 and calculated index but you have not calculated weight of each variable and then multiply weight with that variable to calculate index. how will i get to know hat is the weight of each variable in the index, pl. Kindly elaborate the methodology.
Thank you so much for your sharing. Very informative and useful. If my final index contains positive and negative figures, and it is more than 1, how do I constrain the index from 0 to 1 only (without negative value)?
Thank you very useful. I want to construct an index using two-step PCA. This is the first step. Is it possible to shed light on 2 things: 1. How to carry out 2nd step when I get the results (prediction) from each dimension? 2. How to convert the result (prediction) to an index between 0 and 1
@@statisticalmodelsforsocialscie bonjour, s'il vous plaît j'ai un problème avec cspro, lorsque je fini d'entrer les données dans le masque de saisie, un message d'erreur apparaît" all of the ID fields were not filled,please reenter ou que l'IDs est dupliqué..quesqu'il faut faire SVP..
bonjour, s'il vous plaît j'ai un problème avec cspro, lorsque je fini d'entrer les données dans le masque de saisie, un message d'erreur apparaît" all of the ID fields were not filled,please reenter ou que l'IDs est dupliqué..quesqu'il faut faire SVP..
G00d.. but extremely low voice,. needs audio editing.. also, sorry to mention you need to work on your communication skills, the voice audio and flow is extremely poor (indicating lack of confidence).
I couldn't see the time dimension in this analysis. Constructing a final single index should be time-varying as all other variables. You matched the order of the final index values with your initial variables' values but actually, when I do the analysis with let's say 30 variables for 10 years, I get 10 values for the final index. So how can I match these values with those 30 variables that I put into analysis in the first place? Moreover, these values generally appear to be symmetric starting from a large value with a negative sign then it decreases and then getting a positive sign and magnitude increases. Such as -4, -3, -1, 0.5, 1.2, 3, 4, ... This doesn't make any sense when we try to build an individual index as a time-varying object to indicate a variable, for example, the financial development index. What if we try to construct a financial development index by using 30 variables for 10 years. Am I wrong about the interpretation of the results in the video?
Thank you So Much Brother you solve my problem.
*nice*
Thank you!! Short and understandable!!
*you are welcome*
Thank You Sir. This video was really helpful
*You are welcome*
Is there any paper to support this approach?
Thank you for this great job. But please what is the difference if I predict directly the id after I got pca results and the way you did. Are they the same way
If I want to make three categories how do I do this, using this index?
Thank you sir. You are very kind
*you are welcome*
Thank you very much! can you please explain how one can use that index variable in multivariate analysis? i.e would that index variable be a dependent variable? and if it is, would its be treated as a continuous variable, meaning that one cannot use binary logic regression?
how can I use generated index in binary logistic regression for further analysis
*index is quantitative so to use it in binary logistic you should build a binary variable base on the index*
Hi there, thanks for the video its is very insightful.
Can I ask, is it possible to do PCA with panel data?
*yes but you should do a PCA every time unit(every year for exemple)*
Hi, very good video. You wold share the name of any paper thath use this metodologhy please. Thanks!!!
Greetings. Please you generated "id" by using the respective proportions of comp1 and comp2 over the cumulative value for comp2, because it is up to only this level that we have the proportion greater than the mean value of 20%. I clearly* understand you. My question then is, "how do you calculate the "id" (which is the index) when you have only the proportion of comp1 tobe above the mean value/percentage? Would be happy if I can hear from someone on this issue as soon as possible. Thank you.
Why you are not rotate after first PCA
Many thanks for this video. Very helpful. I want to understand a basic thing. Why is the weighted index value (here the PC1) more than 1? It should ideally be 0-1. Can you please clear my doubt.
did you find the answer, if so can you please kindle tell me about it?
Sir, why are we not multiplying weights to variable values. Here just you have taken linear combination of pc1 and pc2 and calculated index but you have not calculated weight of each variable and then multiply weight with that variable to calculate index. how will i get to know hat is the weight of each variable in the index, pl. Kindly elaborate the methodology.
Thank you so much for your sharing. Very informative and useful. If my final index contains positive and negative figures, and it is more than 1, how do I constrain the index from 0 to 1 only (without negative value)?
*You can normalize you index usion the formula new_id=(hold_id-min_of_hold_id)/(max_of_hold_id-min_of_hold_id)*
@@statisticalmodelsforsocialscie hi, i tried, now the value is lesser or equal to 1, but I still get negative value. how to solve this?
@@huishanlee3872 *It's not possible if you apply well the formula*
@@statisticalmodelsforsocialscie alright, i got it now. thank you so much.
@@huishanlee3872 *you are welcome*
Why the index values are not 0-1 like common index? we suppose to normalize again from 'id'?
Thank you very useful. I want to construct an index using two-step PCA. This is the first step. Is it possible to shed light on 2 things:
1. How to carry out 2nd step when I get the results (prediction) from each dimension?
2. How to convert the result (prediction) to an index between 0 and 1
*the first step is explain in the video, for the second you Can just normalize your index ID=(id-min)/(max-min)*
Please why didn't you rotate to obtain the orthogonal varimax and then predict ?
if i implement the command "rotate", does it make any difference? and then how i generate the id index? can you please kindly help me
can you please share the methodology of the PCA framework.
How do I construct an index with PCA? Is id the index?
*yes id id the index*
@@statisticalmodelsforsocialscie how can i create an index with three components please
Please sir we need an application to index building using MCA in SATA.
Could you please share the dataset?
*yes your email adresse*
@@statisticalmodelsforsocialscie ZiminAvdii1983@bk.ru
@@statisticalmodelsforsocialscie bonjour, s'il vous plaît j'ai un problème avec cspro, lorsque je fini d'entrer les données dans le masque de saisie, un message d'erreur apparaît" all of the ID fields were not filled,please reenter ou que l'IDs est dupliqué..quesqu'il faut faire SVP..
bonjour, s'il vous plaît j'ai un problème avec cspro, lorsque je fini d'entrer les données dans le masque de saisie, un message d'erreur apparaît" all of the ID fields were not filled,please reenter ou que l'IDs est dupliqué..quesqu'il faut faire SVP..
*done*
Again, please why didn't you just taken the proportion or the variance value of the pc2 rathan deducting their cummulative values
You will get the same results...it's just a matter of mathematics
kindly further interpret pc1 and pc2 purpose in simple words
G00d.. but extremely low voice,. needs audio editing.. also, sorry to mention you need to work on your communication skills, the voice audio and flow is extremely poor (indicating lack of confidence).
*Thanks alote for your comment,it's take into consideration*
I couldn't see the time dimension in this analysis. Constructing a final single index should be time-varying as all other variables. You matched the order of the final index values with your initial variables' values but actually, when I do the analysis with let's say 30 variables for 10 years, I get 10 values for the final index. So how can I match these values with those 30 variables that I put into analysis in the first place? Moreover, these values generally appear to be symmetric starting from a large value with a negative sign then it decreases and then getting a positive sign and magnitude increases. Such as -4, -3, -1, 0.5, 1.2, 3, 4, ... This doesn't make any sense when we try to build an individual index as a time-varying object to indicate a variable, for example, the financial development index. What if we try to construct a financial development index by using 30 variables for 10 years. Am I wrong about the interpretation of the results in the video?
J'ai besoin de vous aider ce qui concerne spss si vous pouvez de partager avec moi votre contact svvvvp
*salut hyppolitetchio@**yahoo.fr*
so many adv
*ah okay*