Exploratory Factor Analysis using SPSS ................
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- Опубликовано: 11 сен 2024
- Exploratory Factor Analysis Using SPSS:
Factor Analysis is a process in which the values of observed data are expressed as functions of a number of possible causes in order to find which are the most important.
In this video, I have explained the process of doing Factor analysis in a simple and easy way.
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Thank you. Very useful
To be honest, this is one of the best-explained videos which I've watched and noted some other videos I understand nothing!! But I found what I was looking for on your great explanations! May I leave a question, please? As you have 17 variables, why the components are 5 in your statistics?
17 variables form into 5 components/factors in the analysis
@@MyEasyStatistics
Component Initial Eigenvalues
Total
1 10.116
2 4.025
3 2.075
4 1.606
5 1.371
6 1.242
7 1.073
8 1.007
Here as you can see sir, my components from 2 and 3 greater than 1, in this situation what may I do? Do I have to delete the component 2 and 3 or what please?
The items in your research are forming into 8 components
@@MyEasyStatistics Thank you so much sir ! Do I not have to delete component 1 and 2 because of higher than 1?
Don't delete
I am currently doing this for my PhD thesis.
My Kaiser-Mayer is above 0.9 which is good
In my research, there are 18 variables but following your instruction, I have 14 components and most rate above 0.5 belong to component 1. I am confused now. Actually I do not know what to do next. Could you help me?
Email me the details to gnsatishkumar@gmail.com
My Easy Statistics hi Satish, I have sent you the email
Sorry, I have both nominal and ordinal variable in my questionnaire, i took items from previous stuies and translate from english to french. Which one should i choose between EFA and CFA to check reliability and validity of my questionnaire? or should i use both of them? Thanks for your reply.
Sir, my Msc students is working of Family Farming Efficiency. It is measured with the help of likert scale on 5 point continuum. Scale consisting total 66 statements, out of which some are positive and some are negative. Total sample 140. KMO and Barlet test indicates sample adequacy and specificity. When I did PCA, considering eigen value more than 1, then total 21 factors are identified. Rotated component explaining 73% variation. Total 21 factors....isn't it more number of component. Is there any solution? Considering PG students time and resource I dont want to assign such bulky work. Please give me your valuable Inputs. Actually I am working as a Subject Matter Specialist in Agricultural University. She is doing PG in Agricultural Extension Discipline. Thanking you
Sir how many maximum factors can be analysed using kmo?
what if in any one component none of the questions determine value greater than 0.5 do we need to delete that component?
Yes
@@MyEasyStatistics Thank you! also the variables that have attained higher eigen values will be further used in CFA or in hypotheses testing? pls help
Yes
@@MyEasyStatistics when should we use maximum likelihood method and when should we use principal components
Isn't it wrong to carry factor analysis on Likert scale?