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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how to make regression model using factor loading? [on hold]
I have a data set on survey for measuring customer satisfaction basis 11 parameters (these are independent variables) and overall customer satisfaction as dependent variable. So a data set of a total ...
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50 views
How to get non-standardized factor scores [on hold]
I applied principal component analysis to reduce dimensionality of my data. I need to use factor scores for descriptive analysis. However, because factor scores I get from ...
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7 views
Factor analysis with categorical variables(FAMD)
I'm using the FAMD(factor analysis for categorical and numerical variables) function from the FactoMineR package in R. The cumulative variance of my first 4 dimensions is very small. The first 4 ...
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Maximum likelihood estimator Factor Analysis
I'm looking for a good explanation for how to perform factor analysis using MLE. I'm aware of the factanal function in R, but I'm looking to calculate it manually ...
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MCA in FactoMineR: all variables are the same on Dim 1?
My data is ordinal (1, 2, 3, 4, 5, NA from a likert scale) and doesn't have any obvious pattern looking at the raw data.
My code looks like this:
library(FactoMineR)
df <- data.frame(Var1 ...
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19 views
Significance of factor variable that is an indicator for missing data
I read here and elsewhere that one technique for dealing with NAs in a database is to create a dummy variable that is 1 if an observation (row) has no missing data ...
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explained variance of single item
Assume I've got a factor loading matrix like this:
Now what I am trying to figure out is the percentage of variance in item would be explained by F1 (the first factor).
I'm not sure what that ...
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Can I obtain original data by multiplying varimax-rotated principal components with varimax-rotated unit length loadings?
I have perform conventional Empirical Orthogonal Function (EOF) analysis to my data set and obtain loadings (eigenvector scaled by square root of eigenvalue) and corresponding principal component.
...
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16 views
sklearn FactorAnalysis with wide data
I am using sklearn's FactorAnalysis to identify latent factors in my data. I prefer FA to PCA because I cannot assume equal errors or variance across variables. The dataset is wide (275 variables) and ...
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Correlation in Factor Analysis
In the context of factor analysis as in the extract below
Could somebody explain why the correlation between the $j^{th}$ feature and the factor $G$ is $w_j$. Also, why is the correlation between the ...
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Should a factor analysis (for construct validity) be performed in a section of a test that is meant to measure knowledge in a specific subject?
As a part of a study, a survey to measure the impact of an environmental education project is being developed, and to do so, factor analysis and principal component analysis are being performed for ...
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How to assign a prior distribution to a loading matrix that has restrictions?
I came across the paper Fast Variational Bayesian Linear State-Space Model. They work with the following model:
$$\begin{align}
{\bf{x}}_n &= {\bf A} {\bf{x}}_{n-1} + {\text{noise}} \\
{\bf{y}}_n &...
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SEM: issue with two correlated latent variables
I am fitting a SEM model that includes socio-economic status (SES) for a household and environmental conditions (env) surrounding this household (road condition, sanitation etc).
My (obvious) ...
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Convergent & Discriminant Validity -Same construct (measured by different meaures) in two different samples
I don't know how to go about looking at the Convergent & Discriminant Validity of the same construct measured with two different scales in two different contexts.
This is a cross-cultural ...
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9 views
Why protect “general factor”s in factor analysis?
My Multivariate Analysis textbook states that
As we have noted, a general factor (that is, one on which all the
variables load highly) tends to be "destroyed after rotation." For
this reason, ...
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how to interpret factor analysis results
I’ve just recently started using r and I’m also new in factor analysis and I’m struggling a little understanding the results I’m getting. I used factanal() function and this is the results I got:
With ...
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How to analyze difference in answers between groups across multiple Likert questions? Categorical PCA and Chi Sq?
I'm analyzing this dataset from UCI ML Repo. It's on the perception of Wikipedia amongst university professors/instructors. I'm looking to test the differences between departments in their answers to ...
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Two questions regarding factors/loadings in PCA (Factor analysis)
Sorry if these end up being kind of naive questions, but I'm only starting to get into this type of data analysis technique:
-When I decide to remove a specific variable from my Rotated Component ...
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Cronbach's alpha interpretation - dichotomous data
I have calculated Cronbach's alpha in R using the psych package and I am trying to interpret them now. I want to properly understand this, as it is part of my ...
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How to decide if a rotation is necessary in a factor anaysis?
I have a (self-report) scale with 18 items. The scale as a whole is very reliable ($\alpha = .92$); however, the original authors report two sub-scales.
Here is the interesting thing that I don't ...
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10 views
Resampling or simulating orginal data to assess validity of experimental measure
I would like to conduct combination of EFA and CFA to assess if the measure I've designed to evaluate results of an experiment fits into proposed theoretical model or not. My theoretical model ...
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7 views
Interesting Way To Implement Factor Analysis From PCA
Is it possible to modify the PCA algorithm so that it actually implements factor analysis? We can assume that the uniquenesses are known.
I'm aware that for a $d$-dimensional data $x$, PCA takes the ...
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Problem with Exploratory Factor Analysis for my 7 items measuring attitude on SPSS
My factor analysis using direct oblimin keeps giving me only partial results, stating 'Attempted to extract 2 factors. In iteration 25, the communality of a variable exceeded 1.0. Extraction was ...
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Hierarchical Factor Analysis - Analyzing the factor structure of an identified factor
Problem Summary
After performing an exploratory factor analysis one of the resulting factors "contains" a lot of variables which make its interpretation very hard.
Since all the other factors have a ...
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1answer
68 views
How can Factor Analysis be used to remove questions from a survey?
Suppose I have a psychological questionnaire asking 30 questions about a person's mental health (on a Likert-scale 1-7). These 30 questions fall into 7 separate, but correlated categories.
The ...
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1answer
20 views
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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32 views
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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17 views
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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1answer
32 views
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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28 views
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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21 views
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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1answer
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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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1answer
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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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1answer
18 views
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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1answer
71 views
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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1answer
58 views
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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22 views
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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1answer
59 views
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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1answer
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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:
...
