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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Am I finding redundant columns in my data using Factor Analysis

I have a pandas data frame with 50 columns and 10 rows. The columns represent events and the rows are days. If an event occurs in a day, then the corresponding cell is a "1", else, is a &...
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Stationarity issue in Factor Analysis

I'm applying Exploratory Factor Analysis (EFA) on 20 variables to identify latent factors. The normalized data (Z-scores) is not stationary (Augmented Dickey-Fuller test). First differencing makes the ...
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Rank deficiency and interaction term not estimated

I am trying to inspect the data from a 2 x 2 factorial design. The experiment was run by other researchers and the design was settled upon before. Participants were tested 3 times using 3 different ...
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models MIMIC, predicted probabilities?

I am investigating MIMIC (Multiple Indicators Multiple Causes) models since with them I can do regressions, including factors (made up of several items), and the observed variables (glycemia, ...
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VAE with linear decoder and nonlinear encoder, does this just learn a linear decomposition of the data?

There are a number of variational autoencoder(VAE) methods that have nonlinear encoders and linear decoders. The concept of using the linear decoder is to improve the interpretability (which features ...
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Exploratory Factor Analysis vs. PCA

There is a very nice and clear way to illustrate the geometry of PCA. Is there a similar, geometrical way to illustrate Exploratory Factor Analysis? For example, we have 3D data and we’re using 2 ...
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Are squared loadings and squared distances the same in Principal Component Analysis?

I read online that: Eigenvalue: Represents the variance explained by the principal component. It equals both the sum of squared loadings for that component and the sum of squared projections of data ...
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Can we use Variable loadings from Multi Factor Analysis to define the variable importance

I wanted to know more in-depth about drawing the relationship between variable loadings that we get from Multi-Factor Analysis to variable importance. In other words, for what purposes can we use ...
srinadh nidadana's user avatar
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How to Include a 'No Shift' Scenario in a Temperature Shift DOE

I'm currently working on a Design of Experiments (DOE) for a process that involves temperature shifts at three different times. My challenge is incorporating a 'No Shift' scenario as one of the ...
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Technical differences between EFA and CFA

I've been looking for the answer everywhere but can't seem to find it. When you do EFA, you'll get how much variance is explained by every single factor (and by the entire factor model as well). When ...
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How to create social participation index using STATA?

In order to construct the Social Participation Variable (SPV) for older adults, the study employs a comprehensive set of questions designed to capture the diverse spectrum of social activities in ...
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The Impact of Vector Magnitudes in Recommendation Systems Matrix Factorization Models

I'm currently exploring latent factor models in recommendation systems, specifically focusing on the interaction between vector magnitudes and the angles between these vectors. While it's clear that ...
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Best way to format this data for exploratory factor analysis, using R?

I originally asked this on StackOverflow, but it's more of a stats question than a coding question. My question is about data formatting. I have this dataset (well, this is just the first two of ...
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Good reliability analysis with reverse coded items: further checks needed?

After conducting an exploratory factor analysis on a set of data, I have obtained 3 factors, whose items (can) make sense from a theoretical perspective — even though some negatively worded items ...
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Getting individual level scores from factor analysis with lots of missing data

I have a setting where I'm doing factor analysis in a context where I have lots of rows and where ~90% of data is missing (it's a survey of a couple hundred thousand people, each person was asked a ...
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Specifics of varimax criterion

The varimax criterion maximises high and low value factor loadings and minimises mid-value factor loadings, in order to achieve the maximum value for its objective function. How does doing this ensure ...
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Are factor loadings regression weights or correlations? (orthogonal rotatet EFA)

Are "factor loadings" (orthogonal rotated) in exploratory factor analysis (EFA) correlations or standardized regression weights? For instance in r, the factor loadings are very high, if the ...
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How does varimax work

I have some questions regarding how varimax works. I read that given an n by k matrix A, the k by k orthogonal matrix B maximises the varimax criterion, which measures the difference of these 2 terms: ...
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Comparing Factor Structures Between Groups/Difference in Sample Size?

I am comparing the factor structure of an autism screener across two groups (group 1-> toddlers diagnosed with Down syndrome; group 2-> toddlers diagnosed with Cerebral Palsy). The first group ...
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Generating Composite Index by PCA for a Single Country with Many Variables

I am doing a course project where I am trying to generate an index to measure the overall level of prosperity of a single country - such as the United States. What I hope is that this index could be ...
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How to compute factor loading manually in CFA

How to calculate factor loading manually in CFA? Can anyone explain it by giving the equation and if so, can you give an example too?
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Comparing two groups using factor analysis

I have a 258*14 dataset, I want to compare the latent factor structure of two sub-groups from this dataset (group1= 146, group2= 112). I started by performing an EFA (using ML and Promax rotation) on ...
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How to score a psychological scale with three factors [closed]

I have helped to validate a new psychological scale and after conducting exploratory and confirmatory factor analyses, we have found the best fit for the scale is a 3-factor structure. The factor ...
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Should I go with an unrotated factor analysis model?

I'm running a project on survey data where I have a bunch of very similar operationalizations of my DV (four different indices of my DV). Let's call it support for X behavior. All of them are ...
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longitudinal measurement invariance - why do loadings for latent factor changed signs?

I have run separate EFAs and then CFAs on the second half of the dataset to confirm the solution and the one-factor solution fits well across time points. One weird thing is happening though when I ...
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Do you need to correlate factor residuals of factors that are measured at the same time point

I am running a second-order (multiple indicator) latent growth curve. The model has three latent factors (excluding the growth intercept and slope factors) that each have 4-5 indicators. Two of them ...
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How to Handle Non-Multinormality in the Context of Exploratory Factor Analysis for Logistic Regression

I'm trying to follow the book A Step-by-Step Guide to Exploratory Factor Analysis with R and Rstudio, by Marley W. Watkins, and apply the principles in the book to a real-world data set. Ultimately, ...
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Should I perform CFA after the EFA and then move to multiple regression analysis with the outcomes?

I have gathered 41 variables that are supposed to explain dependent variable Y in a dataset. Is the following reasonable? First, I will conduct EFA, reduce the dimension, conduct CFA to confirm/reduce....
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Factor Analysis: how to distinguish cross-loadings from correlated latent factors

Factor analysis can allow the factors to be correlated in 'oblique' rotations. But this seems underdetermined. How would (explanatory) factor analysis distinguish the two cases below (using the data ...
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CFA - How do within-factor error covariances affect how a psychometric scale is used/scored?

I'm working on a confirmatory factor analysis for a measure with one factor, 8 items, each is a 7-point Likert scale. Two items within the factor are worded very similarly and their errors are likely ...
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Are imaginary eigenvalues a fatal flaw when doing a factor analysis?

I am running an exploratory factor analysis (EFA) in R, extracting three factors (determined via parallel analysis). ...
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Reducing the number of observables in Factor Analysis

I have $k$ observables (e.g. questions in a questionnaire) and $n$ observational units (e.g. respondents of the questionnaire). Let's call the observation matrix $X$. Let's assume I have performed a ...
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Experimental design study on arousal/attention

I hope this question is simple enough, suits this forum and does not consume much of your time. Essentially, want to make sure I have the appropriate design that answers my research question without ...
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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 ...
izzi3880's user avatar
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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 ...
harrybenjamin's user avatar
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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 ...
Kanza Shah's user avatar
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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 ...
Yohanes Yordan's user avatar
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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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