Linked Questions

4
votes
1answer
3k views

High KMO but low communality in factor analysis

I'm performing a factor analysis and I have for a variable a Kaiser-Meyer-Olkin (KMO) measurement of .710 and a communality of ...
2
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2answers
2k views

Where can I find information about using SPSS for EFA and CFA? Is PCA (two samples) and reliability sufficient for scale development?

Context: I am in the process of developing a scale for my thesis. My advisor has guided me to using SPSS PCA to complete my analyses. Initially we reduced my scale to 3 factors (her insistence), ...
2
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2answers
3k views

Differences on exploratory factor analysis, confirmatory factor analysis and principal component analysis

Before it is pointed, I am aware that a very similar question was already asked. Still, I am in doubt regarding the concept. More specifically, it is mentioned by the most voted answer that: In ...
3
votes
1answer
3k views

Metrics of correlation for 3+ variables

I have a basic question, which hopefully you all can resolve for me. What is the best way to determine correlations between 3 or more variables? I have a dataset in which 5 continuous variables each ...
5
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2answers
1k views

Where is the indeterminacy of factor values on this plot explaining factor analysis?

It is a well-known fact that in principal component analysis (PCA) we can obtain true values of components but in factor analysis (FA) we cannot obtain true values of common factors. We can compute ...
4
votes
1answer
2k views

Fundamental difference between PCA and FA?

According to this, the fundamental difference between PCA and FA can be illustrated via the following image: So, the direction of arrows changes. According to this answer and a few others: ...
4
votes
1answer
1k views

What does the Psi term in factor analysis signify?

Diagonal elements of Psi (...) represent independent noise variances for each of the variables C.M. Bishop, Pattern Recognition and Machine Learning ...but I'm not clear on what does the ...
1
vote
1answer
960 views

Recoded items as separate factors in factor analysis

Hello and thanks in advance for your help! I have conducted and EFA for a scale I made that includes 13 items. Of these items, 2 are recoded. Looking at the rotated component matrix I see that ...
0
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0answers
2k views

PCA vs PAF for exploratory factor analysis

I've been reading about performing exploratory factor analysis via principal axis factor extraction (PAF) and principal component analysis (PCA). I'm a bit confused about why the difference between ...
2
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1answer
931 views

How to reverse factor analysis (FA) and reconstruct original variables?

I saw this interesting topic: How to reverse PCA and reconstruct original variables from several principal components? and a nice answer with a very useful example of Iris data in Matlab. I would like ...
1
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0answers
2k views

When plotting PCA analysis with loadings on top, loading arrows come out way too short [duplicate]

I am trying to make a reasonable looking PCA analysis, where not only data are projected in two axis, but also the loadings of the data are projected on top of the data. Similar to the following ...
4
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0answers
918 views

Are principal components reflective, formative, both, or neither?

In reading various summaries on the similarities and differences between principal components and common factor models, I have noticed that there seems to be conflicting information about whether PCA ...
4
votes
1answer
420 views

Is it valid to use only some of the components as regressors from a PCA?

I was trying a 4 components PCA where the 1st and 2nd components have emerged exactly as I expected, but the other two components do not commensurate with the theory. I mean, I expected a little ...
0
votes
1answer
638 views

Eliminated items in Factor Analysis

I'd like to get your opinions on how to interpret items that had to be eliminated in factor analysis (FA). I've been researching consumer shopping motivations and ran a survey with 40 items, which ...
1
vote
1answer
943 views

In principal component analysis, is PC a linear combination of input variables or vice versa?

In every literature I've read each principal component is expressed as a linear combination of input variable. And the coefficient matrix is called factor loading. But why in John Hull's book (where ...

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