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Questions tagged [factor-analysis]

Factor analysis is a dimensionality reduction latent variable technique which replaces inter-correlating variables by 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 tag 'confirmatory-factor'. Also, term "factor" of factor analysis should not be confused with "factor" as categorical predictor of a regression/ANOVA.]

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Exploratory factor analysis for clustered data in R

I am new to both R and factor analysis, and I need to run EFA for a dataset that used cluster survey design. Is there any package for FA in R that can handle cluster data?
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Is there a point to using EFA for scale validation when you can always fit a second-order CFA?

I have been trying to understand the use of Exploratory Factor Analysis for the purpose of scale validation. Say you have developed a scale to measure construct X, which is supposed to be one unified ...
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How to identify and reduce question overlap and redundancies in a survey? (remove questions asked for a more concise survey, w/o losing information)

Suppose I have a survey that contains 30 items. The items ask about the relationship between the respondent and their family, in many different realms. For example, the strength of the connection ...
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Plot of two ordinal variables?

It is common to represent various (ordinal, social) attributes on a pair of axes as in the examples below. In general, what is this sort of graph/representation called? It is, perhaps, useful to ...
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how can I perform confirmatory factor analysis when covariance matrix contains missing values?

I have dataset of test items which were administered in blocks. As a result, not all students answered each test item and there are some pairs of items for which no observations are shared, ...
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Markov property of manifest variables in longitudinal ESM

I am working on a longitudinal ESM model were the indicators are (highly) autocorrelated. This means that the classic cross-lagged models of panel data analysis cannot be used directly. I have ...
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Do the criteria for factor retention apply equally to component retention?

I'm familiar with aspects of the received wisdom about the selection of the number of factors in factor analysis. For example, Wikipedia suggests that Horn's parallel analysis is a good method, and ...
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What is *common variance* in factor analysis and how is it estimated?

Some methods of factor extraction (e.g. principal component analysis, PCA) are based on all variance in the data, while other methods (like principal axis factoring, PAF) are based on (or perhaps ...
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Is it possible to have a moderator with two components - of which one interacts with the IV?

Using factor analysis, I have extracted two components and one of the components interacts with my IV. Should I treat those two components as separate variables or should I treat them as two ...
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Can multiple regression be used to test relationships between factors resulting from EFA?

Following an initial exploratory qualitative phase of research, I have a model containing 7 independent variables (IV's) and two unrelated dependent variables (DV's). All are latent reflective ...
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EFA over time where changes in the factors are of interest

I am asking to find out if an idea that popped into my head is a real thing or just silly. EFA often presumes the data are static. I know there are forms of EFA that take time into account, but I ...
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Longitudinal CFA/SEM with autocorrelated/autoregressed indicators (in R, better with lavaan)

The problem is, I am trying to fit a multilevel factor model to highly autocorrelated (in fact, autoregressive) indicators. More specifically, I have multiple measurements (60-70) per person (5-10 ...
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Multiple linear regression appropriate? Factor analysis?

i want to test the following hypothesis: German consumers perceive products from country X a less favorable than products from country Y I measured this on a 5 point Likert scale with statements ...
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ANOVA: single or combined statements

I want to test a hypothesis that states that country of origin significantly influences the consumers' price perception of electronics 'Made in Country X'. In my data, i have four questions measuring ...
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R - Trying to add factor analysis scores to my original data

I ran a polychoric exploratory factor analysis and I'm trying to assign factor scores to each case in my original data. I've found a few suggestions for how to do this, but each one results in the ...
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How to do factor analysis when there are multiple data about one objective?

20 respondents -> 100 questionnaires(10 identical questions each) about 5 brands each respondent -> 5 questionnaires(5 brands) should I preprocess the data(ex. calculate mean base on brands, the new ...
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Should one remove highly correlated variables before doing EFA?

I am doing Exploratory Factor Analysis with 7 items. One of them (call it v1) have high correlations with 3 others, and other 3 are mutually moderately correlated. ...
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Parallel analysis with rotated data

I am trying to do Factor analysis with varimax rotation for my data using R psych package. To determine number of factors I use R paran package. The problem I see is that eigenvalues produced by ...
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I was requested to run exploratory factor analysis (EFA) on 3 variables. Is there a minimum number of variables to use in EFA?

I was requested to run exploratory factor analysis (EFA) on 3 variables to see if they would load into a single factor. And if they do, then use the factor scores in the further analysis instead of ...
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Item response theory for continuous variables, and estimating standard error of measurement

I really like how traditional item response theory (IRT) packages tell you the standard error of measurement conditional on one's ability level, and from that, you can calculate the test information ...
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Why do I get an error with this data using principal axis factoring but not minimal residual factoring?

I am using n_factors() from the "psycho" package in R to figure out the number of factors for a set of data. When I use prinicipal axis factoring I get the following error: ...
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Factor Analysis: Single variable contributing to several latent variables

I was wondering whether factor analysis is right tool in my scenario. That is, I have dataset $X = (X_1, X_2, X_3, X_4)$, where $X_i$ denotes a single variable. As far as I understand factor analysis, ...
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Is it legitimate to use PCA on scale totals (rather than individual questions) to uncover latent variables (Social Science/Psychology)?

I believe a latent self-control variable may be at the root of plenty of the variation I see in my dependent variable. However, I am using a secondary dataset and do not have access to individual ...
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Factor analysis

I have a couple of questions on factor analysis using Stata. How to decide whether to use pf (principal factors, default), pcf (principal-components factor), ipf (iterated principal factor) or mle (...
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Using numpy SVD to calculate factor loadings [duplicate]

I'm doing PCA (Principal Component Analysis) in Python using the numpys Singular Value Decomposition. Effectively extracting the principal components like so: ...
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EFA with heterogeneously transformed variables

Can I perform an EFA (oblique rotation) with variables that have undergone different types of transformations? Can I still interpret the factor loadings and other output coherently? For ...
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Number of factors in Factor Analysis of Mixed Data with FactoMineR

I'm trying to perform FAMD with FactoMineR because I want to reduce the dimensionality of my data. My data has 378 dimensions and 34K rows. Around 350 of those dimensions are categorical and the rest ...
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Lavaan LavPredict when samples have 0 variance

I am using Lavaan to perform a Confirmatory Factor Analysis on a dataset of 10,000 League of Legends games. The features of the dataset are certain variables of the games like kills, deaths, gold ...
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Why is normalising not recommended for a factorial analysis?

I have been told that during a factorial analysis you shouldn't center and/or normalise your design matrix. I.e. you should not mean-center and rescale the columns of your design matrix. However, I ...
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Using non-parametric transformation (ranking) on variables for factor analysis in R

I have been wrestling with how to simplify a set of mixed variables (some orthogonal, some numeric) some (of both types) of which are significantly skewed. I have run a factor analysis anyway and ...
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Help with PCA Question

The conventional model for probabilistic principal component analysis has a standard normal latent $\vec{y}$ and a loading matrix $\Lambda$: $P(\vec{y}) \sim N(\vec{0}, I)$, $P(\vec{x}|\vec{y}) \sim ...
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Is it possible to weight items differently in a factor analysis?

Suppose I have 100 targets that have been rated by 1000 individuals. I want to perform a PCA on those 100 targets. Now, I'm curious if I were to take some property of the targets into account, how ...
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How to decide number of retained factors when the scree test and the eigenvalue result in different numbers?

I was trying decide on the number of factors to retain in a factor analysis problem. My scree plot showed an "elbow" after the fourth factor and although the fourth factor had an eigenvalue under ...
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Factor Analysis, Cluster Analysis, or something else?

I'm trying to figure out how to determine how a set of variables tend to group together without discouraging crossloadings. For example, say I had a set of symptoms: sneezing, headache, fever, nausea,...
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After conducting an EFA, how to score a factor for new participants using its constituting variables?

I study psychology, and I'm learning EFA (again autodidactically -_- bloody middle east).. So I hope you help me with this naive question Scenario I have a questionnaire made, and I was able to ...
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Define statistical potential energy [closed]

I am looking for a statistical method that closely relates to the idea of potential energy. Here is a quick google definition for potential energy "...the energy possessed by a body by virtue of ...
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stability of factor analysis

I am using the R "psych" package to run factor analysis. If I check the optim function for convergence, I notice that while I get convergence for say 22 factors, I don't get convergence for 21 factors....
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Too many factors extracted from EFA!

I am constructing a scale. I have 80 items and 300 sample data. I ran EFA - Principal Axis Method of extraction and direct oblimin rotation in SPSS. I have 23 factors (using both Eigen value and ...
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How to use Factors from Exploratory Factor analysis in further analysis?

I have performed an exploratory factor analysis on a large data set as a dimension reduction technique. I have come up with 20 factors that group together my predictor variables. However, I am not ...
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The outcome of a Dynamic Factor Model

A basic question but you might help me a lot. What is the outcome of the Dynamic Factor Model (or Static)? As an example let's assume we have 24 time series in an hourly panel. We apply PCA and we ...
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How to interpret feature importance in a decision tree after applying Factor Analysis

I'm using SKlearn to apply Factor Analysis (FA) to my data before training a Decision Tree. I then want to do an importance analysis. If I had not applied FA to my data, I could just call clf....
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Choosing number of factors in PLSR

Im confused about how many factors I should choose for my prediction model. I am using Unscrambler X to do PLSR. Unscrambler is supposed to suggest the optimal number of factors. It suggests 4 factors ...
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How to control/correct for response bias in survey or questionnaire data for Factor Analysis

I would like to apply Confirmatory Factor Analysis (CFA) to a Likert-type questionnaire data. It is supposed that this data is affected by response bias: some patients either overestimated or ...
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Can factor analysis be fit with gradient-based methods?

I know you can fit factor analysis using EM, but can you use gradient-based methods? If so, a reference would be great; otherwise, why not?
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Promax PCA interpretation in order to validate singular items - Structure or Pattern Matrix?

I am doing a promax PCA analysis. I have a big dataset (over 1000 subjects) and about 50 items. I am trying to validate the singular items. My final aim is to exclude non-relevant items (those which ...
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Normality of Data for Factor Analysis?

The organization where I'm working at required me to study Factor Analysis to apply it to some of their research. They gave me different textbooks as study material and switching through both of them, ...
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FAVAR estimation Boivin et. al.(2009)

Boivin et. al. (2009) following the paradigm of BBE(2005) manages to decompose the fluctuations of each series into common and series specific components and study shocks into these components ? Do ...
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Why would someone use regression analysis to compute weighted variables?

I stumbled upon a measure that uses multiple regression analysis to compute weighted variables, instead of factorial analysis or other more common methods. I have a feeling this is just bad statistics ...
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Detecting insufficient communalities in R

Given is this sorted factor analysis: ...
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Interpreting PCA with varimax rotation

I have problems understanding the Factor Component Analysis of the paper: "Measuring thirty facets of the Five Factor Model with a 120-item public domain inventory: Development of the IPIP-NEO-120". ...