Questions tagged [factor-analysis]
Factor analysis is a dimensionality reduction latent variable technique which replaces inter-correlating variables with a smaller number of continuous latent variables called factors. The factors are believed to be responsible for the inter-correlations. [For confirmatory factor analysis, please use the tag 'confirmatory-factor'. Also, the term "factor" of factor analysis should not be confused with "factor" as categorical predictor of a regression/ANOVA.]
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How to interpret marginal effect for an ordinal probit model when the independent variables consist of factor scores?
I conducted an exploratory factor analysis on a 5-point Likert scale utilizing polychoric correlations, identifying and retaining 4 factors. Subsequently, I computed factor scores through the ...
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Is FAMD (Factor Analysis of Mixed Data) truly a factor analysis technique? or it is a dimension reduction technique?
PCA is distinct from factor analysis; it's a dimension reduction technique. PCA does not account for individual variable noise. On the other hand, FAMD (Factor Analysis of Mixed Data) combines PCA and ...
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Comparing factor scores between groups
I have participants who have taken an intervention and are being measured at two different time-points. It is expected that the intervention will improve their knowledge, attitude, confidence etc. and ...
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Is there a difference between Principal Component and weighted mean using PC loadings? How to get Principal Component on scale of original variables?
I was interested in doing a Principal Component analysis but returning a Principal Component on the scale of the original variables. Principal component analysis in R defaults to scaling and centering,...
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EFA for item reduction in multi-level data prior to HLM
I am unsure how to go about an exploratory factor analysis for item reduction with multi-level/repeated measure data. My study is a daily diary, 1x a day for 2 weeks. Participants answered many state-...
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How can I test whether the oblimin exploratory factor analysis function in r has produced the most simple structure?
would anyone know what command could be used to test whether the oblimin exploratory factor analysis function in r has produced the most simple structure? (as my understanding is oblimin does not ...
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Questions related to running an exploratory factor analysis on skewed data
I want to test overall skewness / normality in a large data set of ordinal data from survey questions.
(I couldn’t use Shapiro wilk as I received an error saying the dataset is too large as it has ...
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Factor Analysis using IBM SPSS for Likert Scale
I'm running a factor analysis of a Likert questionnaire that have a Cronbach's Alpha of 0,9.
I'm trying to figure out if my process for factor analysis of the strength of the correlation between two ...
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Should I carry out factor analysis based on this correlation matrix?
I have a correlation matrix below and I am wondering if I should carry out a factor analysis. Are there issues of linear or multi collinearity.
MER RLTD PCL PCP AL ASW CSW ASL OPPO WRY OPTM ...
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How to increase total variance in exploratory factor analsis using Principal Axis Factoring?
I conducted exploratory factor analysis (EFA) on 69 variables and sample of 346 (1:5 variable to sample ratio). I used Principal Axis Factoring (as it is the most used extraction method for common ...
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Need help defining the theoretical covariance matrix between a parameter and two distinct estimators
I have a problem that resembles SEM or factor analysis, but the indicators are estimators of the parameter, not empirical observations of random variables. The model is a one-factor model with two ...
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Updating Dynamic Factor Model within a time-period
I have the following question.
Assume that we have the standard Dynamic Factor Model:
$$
X_{i,q} = \beta_i F_q + \epsilon_{i,q}, \qquad \epsilon_{i,q} \sim \mathcal{N}(0, \sigma_i^2),
$$
and
$$
F_q = \...
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Factor Analysis in Deep Learning
I'm new to deep learning and currently trying to use it for my project. The goal is to predict ship charter prices based on various factors that we have identified theoretically. In this project, we ...
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Clustering or factor analysis for dimensionality reduction in multivariate linear regression
I have dataset describing aggregated purchases from multiple brands. It contains variables:
Brand (ordinal)
Promotion (ordinal)
Sales (numeric)
I need to use linear regression to describe the effect ...
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How does a latent variable's communality value (R-squared) afect the interpretation of a latent basis model?
I am running a latent basis model with 5 time points.
The model has 5 measures of 5 items that make a factor.
I ran the model with the folloing code(Mplus) ;
...
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Percentage of missing values for multiple imputation
I am running a planned missingness design to pilot some items for a questionnaire I am designing. Specifically, I want to test 80 items and every participant (N = 300+) receives a random 10-item ...
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Can I formulate the factor model in another way to overcome the drawback of the classical factor model?
In classical factor model, assume the random vector $X = (X_1, \cdots, X_p)$ has the covariance matrix $\Sigma$, we want to write $X$ in the form $X = LF + e$, the assumptions are $Var(F) = I_r, Cov(F,...
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Principal component analysis with a limited sample size
Thanks in advance!
I am attempting to quantify hospital resources using database derived variables that are all believed based on theory to be important for surgical patients.
There are about 70 ...
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Factors or components in Stata and R (psych, lavaan): Confusing method labels and diverging results
I have some trouble translating between Stata and R (psych, lavaan) about factors and components with an interest in the loadings/eigenvectors of the items. I seek to validate results (understand the ...
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When comparing PCA and FA Models, is it possible to have a model with lower explained variance, but a higher Log Likelihood?
Having a model with a higher log-likelihood and lower explained variance doesn't make sense to me. The Bing AI thing says it's impossible... I'm new to these analyses and the log-likelihood statistic....
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Which statistical test should I use to compare two groups where their opinions are collected by using Likert Scale?
I want to find out a solution to the following question: "Is there is any impact of social media on the creation of entrepreneurial opportunity for rural women?"
I have 2 groups: Online ...
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How to interpret factor analysis output here
I am using girth package which uses polychoric correlation and then applies factor analysis. I am using the example code given on above webpage under the subheading "Polychoric Correlation ...
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Showing uncorrelatedness of related random variables
There are two random variables defined as follows:
$X \in \mathbb{R}^n$ ($n$-dimensional random vector) and $X \sim N(0, I)$ where $I$ is an identity matrix
$Y \in \mathbb{R}^m$ ($m$-dimensional ...
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VIF, SMC and the inverse of the correlation matrix
The Variance Inflation Factor (VIF) is defined to be:
$$VIF_p = \frac{1}{1-R_p^2}$$
where $R_p^2$ is the $R^2$ calculated when $X_p$ is the dependent variable, and all the other variables are ...
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factanal() argument: lower
What is the purpose of the lower element in the control argument of the factanal function?
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How to perform two-stage PCA?
I was reading this paper when I stumbled upon two-staged PCA; apparently it divides indicators into sub-indices because empirical evidence supports that PCA is biased towards the weights of indicators ...
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Can one variable load onto different components in (varimax-rotated) PCA?
I performed PCA on 33 items with 133 observations. Considering the criteria to take components with eigenvalues >1, 4 factors can be extracted. I then did varimax rotation of those. However, I ...
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Can I apply Kaiser Rule without knowing the eigenvalues?
Kaiser's rule suggests the number of principal components to be included in an analysis by looking at eigenvalues. If I'm given standard deviations only, instead of eigenvalues, can I still somehow ...
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Analyse clustered items from different scales?
I am a non stat person so pls be kind in your reply! What sort of statistic can I use to compare how the items of 3 different Likert scales covariate? My respondents sample is 150 ppl. Each respondent ...
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If different $Y_i$'s are generated linearly by some latent factor $X$, can $\mathbb{E}[X|Y_1=y_1,Y_2=y_2,...,Y_k=y_k]$ not be linear?
I'm sorry if the title is confusing. I am working with a model with $(X,Y_1,Y_2,...,Y_k)$ such that $X$ is some random variable (a latent factor) and the $Y$'s are generated according to:
$$ Y_i = \...
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How to construct a scale from factor analysis
I have conducted exploratory factor analysis with psych and confirmatory factor analysis with lavaan (following the code on ch 15 of this book: https://doi.org/10.1515/9783110786088) on a dataset. ...
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communality equals to one in factor analysis
I have an issue that results from the factor analysis show variables with communalities equal to one and the eigenvalue from the weighted reduced correlation matrix is large. I read others' comments ...
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Cluster analysis after factor analysis: What distance measure to use?
I use factor analysis on a set of 15 survey questions (likert scales).
Using the predict command (in stata) I make 5 factors.
Subsequently, I want to use cluster analysis to see if there are "...
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Comparing scores on different dimensions in factor models
So I'm currently conducting some research where I'm using item response theory (IRT) to estimate the difficulty of school subjects in Norway. It turns out that a two-dimensional, simple-structure ...
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Is the EM algorithm guaranteed to converge if the log likelihood is concave
As the EM algorithm is guaranteed to increase the log likelihood at each iteration. If the log likelihood is concave is it guaranteed to converge to the maximum of the likelihood, that is will we get ...
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Optimal predictive factors
Assume I am interested in predicting a time series variable $y_t$ using a vector of possible predictors $X_t$ of dimension $N_x$. I am interested in finding the optimal $N_z < N_x$ predictive ...
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Replacing outliers with the median value of the preceding 5 observations
In the paper Implications of dynamic factor models for VAR analysis the authors propose a a technique for removing outliers in variables used for dyanamic factors analysis:
"The outlier ...
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In R {psych} factor analysis, how to avoid warning about wrong estimated factor weights in fa.stats?
I have a tricky data set for factor analysis with 71 ordered likert-type items. The items have five levels. I'm using psych::fa.
I use ``avaan::lavCor...
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Factor Analysis: Simulating observations from predetermined factor loadings with beta-distributed variables
I have a question about simulating data in the context of (exploratory) factor analysis.
I need to simulate n observations of p measurable variables derived from k latent variables given factor ...
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How to apply Weights to Likert Scale Data for EFA in R?
I am part of a Survey Research Team and we are analysing Likert Scale Data.I would like to do a factor analysis for 9 items in the Survey,in order to find underlying structur. We also have Survey ...
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How to handle intentionally missing survey responses for factor analysis
I've been asked to do factor analysis with some data from a skills assessment questionnaire. The structure of the questionnaire is such that:
36 questions total, divided equally between 3 latent ...
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{R Psych Package} Factor Rotation Appears to Lower Proportion of Variance Explained?
This issue arose using R's psych package, specifically the fa function, but the problem is a ...
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(PCA) Is it possible for PCs loading scores to completely change sign after adding data? [duplicate]
I recently ran a PCA on a dataset of self-report data from 226 subjects to zoom in on which specific individual differences might account for participants’ predicted choices in a separate task we have ...
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Measurement invariance testing with ordinal data using lavaan: what is really happening under the hood?
I've been trying to wrap my head around measurement invariance testing with ordinal data in R version 4.2.2 using lavaan 0.6-13. It all seemed rather straight-forward in theory, but I believe I am ...
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Time invariance when the latent structure is unknown
Imagine that participants completed a series of measures indexing different abilities (memory capacity, learning, etc.) at two timepoints.
The only thing I would like to test at this stage is whether ...
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difference between 'psych::fa.parallel()' and 'paran::paran()' in Horn's parallel analysis [closed]
I am trying to conduct an explanatory factor analysis for data with dichotomous variables(csv Google link, n = 1000 originally but only 500 here) with a WLSMV estimator using ...
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Interpretation of Exploratory Factor Analysis
I am conducting research in cognition, where we are reducing a large dataset. An Exploratory Factor Analaysis with a 3-factor solution (based on a scree plot) reveals factors that appear to correspond ...
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Exploratory factor analysis (EFA) for transcriptomic data
I am doing an EFA for transcriptomic data (n=202, p=190). I did log-transformed the data because of skewness.
My question is, do I have to do false discovery rate (FDR) analysis at all prior to my EFA....
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Definition of Generalized of Low-Rank model
Recently, I looked into this paper "Generalized Low-Rank models" and found it very interesting as it gives general perspective on Principal Component Analysis (PCA) related methods.
In the ...
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How to run a PCA or Factor Analysis when you know one column of the factor loadings
I have this application where I have a direction that I want to keep fixed when I'm running a PCA or factor analysis. Is this possible? I just want to keep a column of the loadings matrix fixed.
How ...
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