**62 factor-rotation questions.**

Is it possible (or does it make sense) to check for correlation after varimax rotation, since varimax assumes that there aren't any correlation between factors (or components)?

I conducted a principal component analysis (PCA) with direct oblimin factor rotation in SPSS.
Because by that time I didn't know any better, I used the COMPONENT MATRIX for interpretation. I added ...

I'm doing uniqueness on factor loading matrix in a factor model.
$ y = \Lambda f + \epsilon$
where $ f \sim N(0,\Sigma)$ , $\epsilon \sim N(0,\Omega) $ and $\epsilon \perp f$.
It's well known that ...

My PCA with prcomp in R results in very low "loadings" (i.e. eigenvectors, see figure below).
I've tried a rotation with ...

A predictive model that I currently use relies on PCA with varimax rotation to reduce the dimensionality of the data (whether this is appropriate is a separate question).
The dataset consists of ...

I'm trying to perform a PCA Extraction + Varimax Rotation in MATLAB and obtain the same results as in SPSS.
My data is the following matrix A:
...

I have a couple of questions involving doing a regression (logistic or linear) after principal component analysis.
If I find principal components using Principal component analysis, can I use these ...

In the rotation options of SPSS Factor Analysis, there is a rotation method named "Varimax". If I choose this option, does it mean the orthogonal rotation technique of Principal Component Analysis ...

I wonder if there is a method that allows finding factor loadings so that the factor would predict the distal outcome the best? The ordinary SEM model would estimate factor loadings and regression ...

Im working on data that I should weight the data using a weight variable that I have and I want to evaluate the association of dietary pattern and obesity. So first I run Proc factor and use the "...

I want to create factors from various binary items. Using the polycor package (Fox, 2006) and R-Essentials I created a tetrachoric correlation matrix in SPSS. The items are all exploratory, so I ...

What is the difference between principal component analyses (PCA) and principal axis factoring (PAF)?
Also, I understand the difference between varimax and oblimin rotations, but is that the same as ...

I am performing a principal components analysis using the psych package in R. I have the results for a PCA with varimax rotation, but I can only figure out how to make a scree plot for the PCA without ...

I have tried to reproduce some research (using PCA) from SPSS in R. In my experience, principal() function from package psych ...

We conducted an exploratory factor analysis using the psych package with oblique rotation and found an acceptable solution with 3 factors. Now a reviewer ask me to provide the proportion of variance ...

There are linearly overlapped components typically in curve resolution or factor analysis techniques [1] [2].
Also for PCA, it is common that it is easy to change the sign of the loadings or scores ...

I need to be able to reproduce some SAS results in R, as far as this is possible (note: I'm very familiar with R and barely used SAS). The original SAS code is as follows:
...

How would you interpret negative correlations between factors in factor analysis, and should they ever be negative?
I did FA with oblique rotation (Oblimin) on 30 variables which were intended to ...

I am currently trying to find the meaning of principal components of my data. I find the raw loading very hard to interpret, because the first components drags a large part of the total variance (50 %)...

This great answer shows how to compute varimax-rotated loadings and scores from PCA results using princomp.
I am conducting a robust PCA analysis using the pcaHubert function from the rrcov package. ...

Will orthogonal relationships show up when using Oblique rotation?
Based on the articles I have read on EFA rotation my understanding is that although oblique rotation procedures might be expected ...

I have an application of factor analysis, where the theoretical model specifies that certain loadings within each factor should ideally share a same sign for the sake of interpretability. For example,...

I am having some issues running PCA with varimax rotation in R.
My data is from an 18 item questionnaire with a 5-1 likert scale with 1893 entries/rows. All missing values are imputed using the ...

In many receptor-modeling studies, after performing the PCA analysis, they often "rescale" their varimax-rotated PC scores (which are standardized with mean zero and standard deviaiton of 1) to ...

I want to apply a 2D rotation of a $\theta$ angle to my two first principal components of a PCA.
What I understood from this post is that I have to apply a rotation matrix R :
$$
R_\theta = \left(
\...

I have done a questionnaire with six questions to measure engagement. This is the only component I measure. To make sure they measure just one component, I tried to run a Factor analysis with Direct ...

What are the differences between varimax and promax rotations in PCA?

I am fairly familiar with the practical application of principal component analysis (PCA). PCA tries to find the first PC, for example, by minimization of the sum of squared perpendicular distances of ...

I ran PCA on 25 variables and selected the top 7 PCs using prcomp. prc <- prcomp(pollutions, center=T, scale=T, retx=T)I ...

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, ...

This is my first post so apologies for any incorrect formatting or whether this has been answered elsewhere but I seem to be going around in circles.
Basically, I have 12 survey plots and have ...

I have a data set in which each subject answered a subset of questions in response to visual stimuli (picture rating). At the beginning of the experiment, every subject got assigned 2 out of 16 ...

I am trying to conduct a small experiment based on Likert style data. I have a total of 20 questions, 10 are referring to a latent construct of happiness, and the other 10 to a latent construct of ...

I am conducting a Factor Analysis using PCA.
I have used Oblique and Orthagonal Rotations and when I am trying to analyse my results I get the message:
"Rotation failed to converge in 25 iterations"
...

I've just run a FA using a oblique rotation (promax) and an item yielded a factor loading of 1.041 on one factor, (and factor loadings of -.131, -.119 and .065 on the other factors using pattern ...

Using the pcaMethods package in R I have run PCA on a data set of ~500 subjects with ~300 variables each. There are some missing values so I am employing the ...

I am conducting a CFA on a questionnaire with 4 factors. I know that the exploratory factor analysis to obtain theses 4 factors was done using oblimin rotation. I am now wondering, if this affects the ...

I have just read about the usual mistake in Principal Components Analysis of confusing between principal directions and component loadings following the explanation and links here:
Relationship ...

I am using correspondence analysis (CA) to analyze a contingency table.
In the columns I have statements about some brands (characteristics) and in the rows I have the brands. My aim is to obtain in ...

My Questions
What is the intuitive reason behind doing rotations of factors in factor analysis (or components in PCA)?
My understanding is, if variables are almost equally loaded in the top ...

Factor analysis has several rotation methods, such as varimax, quartimax, equamax, promax, oblimin, etc. I am unable to find any information that relates their names to their actual mathematical or ...

Empirically the quartimax-/varimax-rotation has proven useful and it was always converging in my applications. But from my readings years ago (most prominent S.Mulaik and K.Ãœberla monographies on ...

Background
I'm reading some notes in multivariate data analysis, in particular factor analysis.
A data vector $X_{p\times 1}$, with $E(X) = \mu$
A vector $F_{m \times 1} $ of factors,
A matrix $L_{...

Are there any reasons to not rotate an exploratory factor analysis solution?
It's easy to find discussions comparing orthogonal solutions with oblique solutions, and I think I completely understand ...

After doing PCA, the first component describes the largest part of variability. This is important e.g. in study of body measurements where it is commonly known (Jolliffe, 2002) that PC1 axis captures ...

I am currently trying to use a PCA in combination with a varimax rotation on some measurements to extract underlying factors. However, some of the variables are related, in the sense that they ...

I am trying to perform PCA with varimax rotation on a set of data with 2 variables, in order to orthogonalise them (not for dimension reduction). I used prcomp() to ...

I have a raster stack of 19 layers called "raster_bio". The code to do PCA analysis is:
...

I have a matrix of (scaled) co-occurence counts which I would like to summarise using (classical, i.e. PCA-related) Multi-Dimensional Scaling (MDS), and then rotate (with ...

When I try to compare oblique rotated factor analysis (promax, ML) with bifactor rotated analysis (ML), I get different pattern loadings. I don't know which solution should be retained; I am planning ...

- pca
- factor-analysis
- r
- spss
- interpretation
- regression
- correlation
- variance
- logistic
- data-transformation
- stata
- terminology
- dimensionality-reduction
- sas
- eigenvalues
- confirmatory-factor
- partial-least-squares
- cronbachs-alpha
- rotation
- machine-learning
- bayesian
- references
- matlab
- normalization
- multicollinearity

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