Linked Questions
12 questions linked to/from Estimating specific variance for items in factor analysis - how to achieve the theoretical maximum?
53
votes
4
answers
29k
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Why does inversion of a covariance matrix yield partial correlations between random variables?
I heard that partial correlations between random variables can be found by inverting the covariance matrix and taking appropriate cells from such resulting precision matrix (this fact is mentioned in ...
- 1,278
34
votes
1
answer
45k
views
Best factor extraction methods in factor analysis
SPSS offers several methods of factor extraction:
Principal components (which isn't factor analysis at all)
Unweighted least squares
Generalized least squares
Maximum Likelihood
Principal Axis
Alpha ...
- 14.1k
15
votes
1
answer
25k
views
Steps done in factor analysis compared to steps done in PCA
I know how to perform PCA (principal component analysis), but I would like to know steps that should be used for factor analysis.
To perform PCA, let us consider some matrix $A$, for instance:
...
- 775
5
votes
1
answer
17k
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What's the relationship between initial eigenvalues and sums of squared loadings in factor analysis?
On the one hand I read in a comment here that:
You can't speak of "eigenvalues" after rotation, even orthogonal
rotation. Perhaps you mean sum of squared loadings for a principal
component, ...
7
votes
2
answers
7k
views
Can you calculate $R^2$ from correlation coefficents in multiple linear regression?
In simple linear regression, $R^2$ is equivalent to the squared correlation of a dependent and an independent variable. Is this also true for multiple linear regression?
For example, I measured trait ...
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4
votes
1
answer
4k
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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 ...
- 43
3
votes
1
answer
7k
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What is the difference between the anti-image covariance and the anti-image correlation?
What is the difference between the anti-image covariance and the anti-image correlation? How are the matrices of these coefficients computed, and what is the meaning of their elements?
2
votes
0
answers
4k
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What is the intuition behind the KMO formula?
In answer to a different question about data assumptions of factor analysis rolando2 writes:
There is another condition that is sometimes treated as an "assumption": that the zero-order (vanilla) ...
4
votes
1
answer
704
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Factor extraction methods: Harris and Image extraction
Is Harris's (1962) extraction method considered a form of principal component analysis (PCA) or factor analysis (FA)? What about image extraction?
I am using SAS and the documentation says:
HARRIS | ...
- 435
2
votes
0
answers
764
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Negative Eigenvalues in EFA
Assuming a set of data meet all assumptions for EFA and we are doing a factor analysis (with the SMC used to define the shared variance), how do negative eigenvalues appear and what does that say ...
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4
votes
1
answer
401
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Factor Analysis does not give a better covariance estimate than the Empirical Covariance matrix?
I do not see that Factor Analysis gives a better covariance estimate than the empirical covariance estimate, from the toy data simulation with explanation and code below. Am I doing something wrong?
...
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0
votes
0
answers
253
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Can PCA work when the number of observations is smaller than the number of dimensions? [duplicate]
I understand how principal component analysis works. However, in a financial time series sense, I do not understand why the number of observations should be larger than the number of dimensions. I am ...
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