Karanpreet Kaur
6 years & 2 days ago

Thank u mam for this information .It helps a lot in understanding factor analysis.

Mam my query is
In interpretation, I studied that we have to calculate factor score and if not want to calculate then select surrogate variable ..mam plz explain this

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Prateek Sharma
5 years & 11 months ago

Hi,

Surrogate variables are just the ones which define the significance of each factor based on their high weights. We select these variable/s so as to reduce the dimension while still keeping the original significance of it.

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Karanpreet kaur
6 years & 2 days ago

Mam what is meant by high loading in factor analysis??

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Prateek Sharma
5 years & 11 months ago

Hi Karanpreet,

Hope you’re doing well.

“Loading” basically refers to the weights of each variable in respect to the factors we’re creating. All the variables may or may not show the degree of their belonging to all the factors. However, these variables show high loading weights to the underlying factors they belong to. And in order to get a clear picture, Rotated Component Matrix arranges the variable in a way that most belonging variable will come at the top of each factor with their High Loading weights.

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Pawan joshi
6 years & 1 month ago

Good morning ma’am
There is a query from my side that in total variance explained table how to know that which variable is there whose value is greater than 1 as spss giving components 1,2,3…
How to know that what are those factors/ components whose eigen value greater than 1 .
For example in the total variance table explained by you in above…
How we came to know that these 3 factors which are extracted what are these 3 components whose Eigen value greater than one..
I’ll be highly obliged please let me know..
Pawan Joshi

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Avishek Majumder
6 years & 1 month ago

Hi Pawan,
Greetings of the day!
As in the total variance table, you can see that the first three values have greater than 1. The component sequence follows the same sequence of communalities table. So 1 will be the availability of a product.

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Ayu
6 years & 6 months ago

Hi Shruti,
do we need to do again factor analysis using the latest items after we removed the items that should be removed?

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Avishek Majumder
6 years & 1 month ago

Hi Ayu,
Good day!
Yes, it is important that you re-perform factor analysis even after removing variables as the loadings and other parameters changes after every deletion.

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Iesha
6 years & 7 months ago

hi..
plz help me with one thing.. what I have learnt till now is that we apply single EFA on our questionnaire.. But I feel it is correct when we have a number of items in a single construct.. and when there are 3-4 different (logically) constructs, EFA should be applied separately.. please clarify on this..

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Chandrika Kapagunta
6 years & 6 months ago

Hi Lesha,
I agree with you. The purpose of EFA or CFA is to confirm which of the items represent a construct best, based on your data. Therefore, EFA has to be performed on each of the logically different construct separately. This way you will know which items you will need to keep, and which ones have to be dropped.

I hope this has helped.

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Iesha
6 years & 6 months ago

Thanks a lot Chandrika 🙂 one more thing is there… CFA is applied on a model as a whole and not on constructs.. Plz correct me on this if I am wrong.. and considering the same case as mentioned before, should we proced like applying EFA on individual constructs and CFA on whole model… or CFA also individually..
Thanks in advance..
Regards
Iesha

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Iesha
6 years & 6 months ago

Hello Shruti..
can u plz help me with my query mentioned..
Thanks and Regards

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William Pascal
6 years & 7 months ago

Hello Shruti Dati
In the rotated component matrix, what is considered to select the variables to be used? Im confused whether we i should choose those with higher loading from each category or i choose any with high loading regardless of its category

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6 years & 7 months ago

Dear William,

Generally, the variables which are under same category i.e. main factor are presented in one column and would have similar values. But in case they are segregated then you should pick the one with higher loading for that particular category as per the literature.

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Iesha
6 years & 7 months ago

thanks a lot Shruti.. You have been a great help 🙂

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Iesha
6 years & 7 months ago

thanks a lot Shruti for guiding 🙂
But actually few experts mention that EFA is applied for a study where no previous work is available and we make statements with the help of experts and our own understanding of the things.. and CFA is applied in case we have literature available with us and we use it as it is or with certain modifications…

Please clarify on this..

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6 years & 7 months ago

Hi,
For your study only I am stating that you will have to apply EFA first and then apply CFA. Although you have some statements, but you have will have establish their validity first. Both using EFA and other tools like chi square, goodness of fit etc.

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Iesha
6 years & 7 months ago

hi
this is regarding choice of EFA vs CFA..

what I know as per my knowledge is that when we are working on something which has no literature available and make statements with the help of experts, we employ EFA.. and if we literature is available and there are standardised scales or we modify (add/delete) items .. we employ CFA…

now I am working on something where a model has been proposed by the authors previously. so I am having the constructs available, but they have used hardly 5-6 items per construct… I, with the help of literature, have gathered or say modified a no. of statements for each construct (approx 50 statements per construct)..

now, for analysis part should I start with EFA or apply directly CFA… and wat for pilot study data.??? please help with this 🙂

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6 years & 7 months ago

Hi,
You will have to start with EFA and then apply CFA.
EFA will be applied on the pilot data only. Based on the findings of Pilot results, the final analysis will be done using CFA.

I hope this helps.

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Iesha
6 years & 8 months ago

Hi
there r certain queries related to values in rotated component matrix….

1. Is it fine to get say 12 factors in Rotated Component Matrix with none of the items/statements under one or more factors. Should we in that case consider rest of the factors or some sort treatment is needed.

2. in case an item falls under 2 or more factors, should we consider it under the one with highest value.??

3. in case there are one or two statements that come under a factor in rotated component matrix, but logically we find that those two can be included in some other factor, should we merge them or what else can be done in such a case.??

thanks in advance 🙂

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6 years & 8 months ago

Dear Iesha,
Greetings!
You have raised valid points for your analysis, however, you should not focus only on the Rotated Component Matrix. Rather you need to review the Eigen Values and the Scree Plot as well.
Please find below the answers to your queries below:

1. These 12 factors are individual factors with sub-factors under them? The factors which are not coming under any factor can be removed from the final likert scale.

2. Yes, it is advisable to chose the one with highest value.

3. Yes, in case the factor is similar then they can be merged under bigger factor.

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Iesha
6 years & 8 months ago

Hi Shruti..

Thanks a lott 🙂

in case we r getting some factors containing 2-3 random items (i.e., which are logically quite different from rest of the items in a factor), what kind of treatment should be done. I mean how can we assess where the problem lies, if even after increasing the sample size situation remains the same.

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6 years & 7 months ago

Dear Iesha,
Mostly when people respond similarly to set of statements in one factor they are grouped as one factor in the matrix.
So, you check the mean/descriptives of these individual statements to know if all the individuals are pointing at the same thing. If not then these statements should be excluded.

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