Linked Questions

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1answer
653 views

Can I physically determine the number of factors in Factor Analysis?

I have completed a Factor Analysis on a around 180 responses for a questionnaire with 56 Variables. I got 15 factors using Oblique Rotation PCA. Some of the factors have only two variables of ...
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2answers
237 views

Which is more appropriate in this case — PCA or Factor Analysis?

Suppose I have an experiment that has 8 factors. These 8 factors are probably related and can hopefully be reduced. For each combination of these 8 factors that I test, I get a single output. My goal ...
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1answer
632 views

Is it necessary to do confirmatory factor analysis for structure equation modelling?

I have done Principal Component Analysis for a scale comprising multiple latent variables. Is it necessary to do Confirmatory Factor Analysis before doing Structure Equation Modelling?
2
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1answer
380 views

What are the differences between these two kinds of PCA?

The book "Elements of Statistical Learning" describes Principal Components Analysis through SVD as follows: $$X = UDV^T$$ Then $ UD $ are the Principal Components and $ V $ are the directions. ...
3
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1answer
487 views

Understanding (exploratory) factor analysis: some points for clarification

[A question about what we optimize in FA, is FA a clustering of variables, and when/how we choose the number of factors] I have read some tutorials and looked at some of the questions here, as well, ...
0
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1answer
313 views

How to use factors generated from PCA? [closed]

I have survey data measuring the BIG five personality test. In total, there are 60 variables measuring the five components in a five-point scale. The goal is to look at whether certain experiences in ...
3
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0answers
213 views

How does PCA maximise Total Variance without maximising Co-variance?

https://stats.stackexchange.com/a/3374/92071 - In PCA, the components are actual orthogonal linear combinations that maximize the total variance. In FA, the factors are linear combinations that ...
1
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0answers
156 views

Why does PCA's failure “to explicitly model error variance” make it difficult to interpret components?

I've heard statements like this many times over the years, and it's perhaps expressed most clearly by Preacher & MacCullum (2003), which is a popular paper on stats.stackexchange.com (e.g. ...
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0answers
152 views

Principal component analysis (PCA) vs. method of principal components for factor analysis (FA) [duplicate]

I have just read in one of the answers here as follows: One of the biggest reasons for the confusion between the two [principal component analysis (PCA) and factor analysis (FA)] has to do with the ...
1
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0answers
122 views

Conceptual question: how is a factor created in exploratory factor analysis?

As a conceptual question: in exploratory factor analysis, how is a factor created? I would like to know your simple answer to this simple question. Imagine, my academic field does not dependent on ...
0
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1answer
57 views

Why is the result a set of factors instead of just only one?

I am trying to get into SEM and factor analysis. I understand a factor is a latent construct, say e.g. $intelligence$, user-defined by the (weighted) average of a set of indicators $x_1, x_2\dots x_n$....
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0answers
55 views

What to do when EFA requires more factors but the number of variables is low?

There's a lot of information on the forum about the differences between principal components analysis (PCA) and exploratory factor analysis (EFA) (for example here and here). I am new to both methods, ...

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