Thank you very much for your nice presentation with strong theoretical explanation. You mentioned to suggest suitable names for the factors identified. If you please, give an example, how names of factors can be chosen, it would be helpful for me. Another issue, can i use the results of factor loading to see the association between/among categorical data (e.g. age, gender, marital status, educational qualification etc.)? If you have any lecture/link on that issue, reply please.
Thanks Dear professor for this super video. I have a question: In Exploratory Factor analysis many researchs assume that the factors are not correlated with the measurement errors and the measurement errors are not correlated between them. This assumption isn't valid in reality?
Thank you for your smooth explanation, Dr Nayak.
thank you so much, professor. the way you taught nothing left in doubt. 😊
Thank you very much Prof. Nayak and the whole team of this course. Very useful videos.
Spectacular lesson, thank you.
Thank You very much for your great explanation!
Thank you very much for your nice presentation with strong theoretical explanation. You mentioned to suggest suitable names for the factors identified. If you please, give an example, how names of factors can be chosen, it would be helpful for me. Another issue, can i use the results of factor loading to see the association between/among categorical data (e.g. age, gender, marital status, educational qualification etc.)? If you have any lecture/link on that issue, reply please.
Thank you sir for your presentation which is helping me lot .
Hello sir, when we will use EFA... I would like to know about while testing the hypothesis or reduce the Variables in to groups...
Thanks in advance
Thank you sir. very well explained!
very well explained
How to find out whether flooring and ceiling effects exists in the scale data?
Thanks Dear professor for this super video.
I have a question:
In Exploratory Factor analysis many researchs assume that the factors are not correlated with the measurement errors and the measurement errors are not correlated between them.
This assumption isn't valid in reality?