Parallel analysis is a criterion used to help decide the number of principal components or principal factors/common factors to retain. It is based on retaining eigenvalues of observed data greater than those corresponding mean eigenvalues from many data sets of uncorrelated data with the same $n$ observations and $p$ variables as the observed data.
Questions tagged [parallel-analysis]
16 questions
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A paper mentions a "Monte Carlo simulation to determine the number of principal components"; how does it work?
I'm doing a Matlab analysis on MRI data where I have performed PCA on a matrix sized 10304x236 where 10304 is the number of voxels (think of them as pixels) and 236 is the number of timepoints. The PCA gives me 236 Eigenvalues and their related…
chainhomelow
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How to correctly interpret a parallel analysis in exploratory factor analysis?
Some scientific papers report results of parallel analysis of principal axis factor analysis in a way inconsistent with my understanding of the methodology. What am I missing? Am I wrong or are they.
Example:
Data: The performance of 200 individual…
jhg
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Permutation test for factor analysis
We have a survey instrument and are interested in assessing dimensionality of it. Looking at plots of multidimensional scaling, it appears as though there are, perhaps, 3 distinct dimensions to the survey since there are 3 seemingly well defined…
AdamO
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Strange results in parallel analysis -- weird output by rstudio but not R-Fiddle
Major UPDATE based on discussion with Aleksandr Blekh's answer (thanks so much!):
This MRE would run with no problem in R-Fiddle
library(psych)
data(bock)
fa.parallel.poly(lsat6)
Output from R-Fiddle (Graph omitted as not relevant with error), no…
ceoec
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What's the difference between a component and a factor in parallel analysis?
The psych package in R has a fa.parallel function to help determine the number of factors or components. From the documentation:
One way to determine the number of factors or components in a data
matrix or a correlation matrix is to examine the…
Jim
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Does it make sense to use criteria from PCA to select the numbers of factors in a factor analysis?
Looking at both the practice of colleagues and also the practices instantiated in popular programs (e.g. SPSS, and commonly used syntax for SPSS), it seems common to use criteria based on a PCA to select the number of factors in a factor analysis.…
user1205901 - Reinstate Monica
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How do random data eigenvalues change, as random variables are added?
I am using parallel analysis (Horn 1965) to determine how many principal components I can extract from my data.
I can add more variables to my dataset, but I cannot add more cases (I know, that's weird, see below for some more context).
Presently,…
maxheld
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Can I do parallel analysis with any type of exploratory factor analysis/principal component analysis?
I wish to perform parallel analysis to determine how many factors I should extract from my maximum likelihood exploratory factor analysis. I have been referred to a program that calculates the eigenvalues for random data using Monte Carlo for…
Behacad
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Problem with parallel analysis with psych
I have a data set with several hundred variables and some thousand records. I'm reviewing the different ways for running a Principal Component Analysis and choosing the principal components.
First I used the function prcomp() to get the advantage of…
Diego
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Differential model. Random Forest
I was having discussion with some colleagues and I would like to know some external opinion.
Description:
We have to decide, for a given person, whether that person would choose item EXPENSIVE or item CHEAP.
For item EXPENSIVE we have an unbalanced…
Rommel
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EFA Parallel Analysis
First time poster, I'm looking for some assistance with parallel analysis in R. I am doing exploratory factor analysis (EFA) on a 22 item questionnaire (n=6598) and looking for an effective way to decide on an appropriate number of factors to…
William Espinoza
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What statistical techniques can I use to model improvement over time?
Say you have a group of 30 students and you measure each individual's performance on a test at 4 intervals throughout the year. (For the purpose of this investigation, assume the tests taken are identical.)
What tools can I use to investigate the…
Peta Hillier
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Unexpected eigenvalues in parallel analysis for factor analysis in SPSS
Would greatly appreciate if someone could clarify which eigenvalues I am supposed to compare when using parallel analysis to determine factor retention.
I am running Principal Axis Factoring in SPSS 24. For Parallel Analysis (PA) to determine the…
sjb
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Choosing how many factors to retain based on parallel analysis and on a scree plot without an elbow
When I realize the Factor Analysis (I have 16 items), the PCA says I have 5 factors. But in the scree plot there is no elbow at all, just a decreasing line, that makes me think maybe I shouldn't be using PCA. At the same time I realize a Parallel…
Sara
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What should we control for when using DDD in country level? Should DDD avoid parallel trend testing?
A very common assumption of DiD is parallel pre-trend satisfaction. And when we subsample, we need to make sure the parallel trend assumption of the subsample also need to be statistically satisfied. One way to circumvent it is to use DDD…
Louise
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