Factor analysis is a data reduction 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. For ...

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Factor analysis: what are the total possible scores for calculated factors based on Likert scale responses?

I have two factors, Peer Social Interaction (PSI) and Sense of Belonging (SB). Four Likert scale questions correspond to each factor. I used the factor loadings for each question to create a ...
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21 views

Factor Analysis vs. Random Forest Feature importance

Could someone explain the intuition behind the difference of feature importance using Factor Analysis vs. Random Forest Feature importance. Does there lie an advantage in RF due to the fact that it ...
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14 views

Reverse scored items lead to different factor loadings

I am doing a factor analysis with principal axis analysis and oblimin rotation. Many of my items are negatively formulated, and seeing that i need to do a reliability analysis, i decided to reversed ...
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51 views

Principal component analysis with large number of predictors and small number of samples ($p\gg n$)

I have a data set with large number of predictors and small number of samples ($p \gg n$). I would like to apply PCA or factor analysis on it, to reduce dimensionality. I would like to know, is it ...
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Principle of factor analysis

Please is anybody can explain me the principle of factor analysis (exploratory and confirmatory) that I will use in the validation of a questionnaire translated. Do I have to make a survey using the ...
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47 views

Normality assumption in PCA

From Shapiro-Wilk's test I see that the responses to Likert (4point) items are not normally distributed, although Q-Q plots approximately indicate to normality. I have done PCA analysis on those items ...
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41 views

Factor analysis with repeated measures

Multilevel factor analysis seems to be the technical term for factor analysis with repeated measures, judging from this abstract. To be precise, following Wikipedia's factor analysis notation, the ...
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Mixture distribution fitting for latent variable analysis

Are there any analytic approaches to using mixture distribution fitting for latent variable analysis? I'm specifically interested in existing approaches to determining whether mixture components ...
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13 views

Variation of binary responses between repeated measures

I am bit confused in terms of analyzing my data. I have done an experiment which involves testing of detection systems with controlled footage.The footage has been grouped based on scene properties ...
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26 views

Factor analysis results

I carried out factor analysis on 31 5 likert-scale questions which represent my 6 constructs. The results showed 6 factors, consistent with my hypothesis but the analysis grouped my questions ...
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49 views

How to handle variables with low correlation but high loadings in factor analysis

I am doing factor analysis to check the factorial validity of a 14-items scale with four subscales. Two items have low (less than 0.3) correlations with other items in the subscale to which they ...
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48 views

Determining Relative Weights

I am looking for some recommendations and more specifics about how to do the following: Objective: To determine the weights of a number of stock valuation metrics. I am looking at doing this across ...
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12 views

Individual item reliability in factor analysis

Im currently testing the reliability and validity of the SERVQUAL model (measuring just the items for each dimension. Hence these items should load on just factor). My supervisor wants me to include ...
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60 views

Use of factor analysis + regression

Independent Variable: I have a survey of 50 states indicating the amount of control the state board of education has in 31 areas answered on a three point scale (1 = total control; 2 = partial ...
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20 views

Multiple Factor analysis and squared cosines

I am a bit confused on how to proceed using the MFA analysis from FactoMineR in my data set. I am currently working with activity results of 15 bacteria (b1, b2, b3, b4, b5,.., b15), divided into 3 ...
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56 views

Factor Analysis in SPSS & CFA in AMOS

I'm currently in the middle of analysing data for a masters dissertation and I'm having a lot of trouble with understanding factor analysis. I've collected data using a questionnaire in which I ...
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1answer
61 views

Is it a good idea to use log scale on scree plots for PCA/ICA/FA?

I always found the concept of determining the "ideal" number of components/factors for an ICA/PCA/FA via a scree plot useful and quick, but also a bit shaky. In an effort to try to make the scree ...
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6 views

Factor Analysis - principal axis analysis and MAP-Test

I have got an important question concerning principal axis factor analysis: Is it possible to extract the factors according to the MAP-criteria, or is this criteria only valid when using principal ...
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1answer
46 views

Linear Model or Logistic Model: Can Someone Recommend a Book?

I have a huge data set that looks roughly like this: ...
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24 views

What does it mean to include a p-value in an exploratory factor analysis?

I have been informed that I should have a p value listed in my EFA results section? I am confused by this and wonder what am I missing?
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Cross Vaidation in Factor Analysis

I am trying to conduct an EFA with a sample size of 150 respondents. I would also like to use cross-validation but my professor says that the sample is not big enough for that. Is that true?
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In CFA, which method (MLE, GLS, etc.) is preferable for Likert scale type items?

I need to perform a CFA on an 18 items questionnaire, and I wonder which method would be best suited for the analysis (considering multivariate normality requirements are met): MLE, GLS, ULS, or WLS? ...
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21 views

Rotation of Mean Centred Variables in Principle Components Analysis

I'm looking to manually (Excel) perform PCA without any statistical packages such as R, but having trouble understanding how to rotate the original variables to find the maximum variance for the new ...
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56 views

Singular covariance matrix in exploratory Factor Analysis

I'm kind of a noob to EFA and am trying to use the FANode object in Python. This is from the MDP library. I am using it on survey data to see which variables are tied together. Whenever I run it on my ...
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12 views

How to compute a latent variable (as a composite index)

Can I calculate a latent variable (construct) after the formula: Sum of (summated scale for factor i * variance explained by factor i), i taking values from 1 to n (n being the number of factors)? Or ...
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20 views

Summated scales for factors (latent variables)

For summated scales: can I use all the variables that load on a factor or is it necessary to use only the variables with high loading (over 0.70, for example)?
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27 views

Loadings in factor analysis

If I have a sample of over 350 respondents, is it acceptable to consider loadings over 0.50 as being practically significant or should I consider loadings over 0.30 as being significant (which would ...
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Criteria for deleting variables in factor analysis [duplicate]

Does it have importance in conventional PCA/factor analysis which of the criteria: communality and loading problems is taken into consideration for deleting variables in the conventional PCA/factor ...
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Summated scales (after factor analysis)

Is it okay to calculate summated scales if I am undertaking an exploratory study and some of the factors that result are somewhat different from what I have found in the literature? (I have read that ...
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22 views

Combining CATPCA with conventional PCA or factor analysis

I have some concerns regarding factor analysis and especially about combining the factor analysis for an ordinal scale (categorical data) - CATPCA with conventional PCA or with factor analysis. ...
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67 views

Factor analysis - CATPCA combined with conventional PCA

I have some concerns regarding factor analysis and especially about combining the factor analysis for an ordinal scale (categorical data) - CATPCA with conventional PCA. Basically, I need to enter my ...
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36 views

Creating index from three variables in panel data

I have three variables which I want to combine in one. The obvious choice is factor analysis or principal component analysis but it's not so simple with panel data because it needs means and standard ...
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1answer
75 views

Principal component regression analysis using SPSS

I have done multiple regression analysis (MLR) of my data and find out $R^2$ and $r$, and then to remove multicollinearity problem I used PCA. This analysis generated PC equal to my variables, I ...
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10 views

Scores derived from variance specific for a factor

I received a comment about my article from one of the reviewer but I do not understand what I should do. Here is a short explanation of the study: This study investigated the influence of lectures ...
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1answer
48 views

Eliminated items in Factor Analysis

I'd like to get your opinions on how to interpret items that had to be eliminated in factor analysis (FA). I've been researching consumer shopping motivations and ran a survey with 40 items, which ...
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72 views

factor analysis for given data with help of matlab

suppose that we have following data i have done covariance matrix and eigenvalue decomposition ...
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75 views

What is the relationship between scale reliability measures (Cronbach's alpha etc.) and component/factor loadings?

Let's say I have a dataset with scores on a bunch of questionnaire items, which are theoretically comprised of a smaller number of scales, like in psychology research. I know a common approach here ...
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293 views

Steps done in factor analysis compared to steps done in PCA

I know basically how to express PCA (Principal component analysis) mathematicaly, but I would like to know steps that should be used for factor analysis. ...
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43 views

Determining characteristics of sampling sets for EFA/CFA/SEM

Dividing sample data into several sets seems to be a common approach in statistics. This is especially evident in predictive modeling, where samples are traditionally divided into two sets, usually ...
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1answer
43 views

Factor analysis Vs PCA [closed]

Which is a better method of data reduction - factor analysis or principal component analysis?
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23 views

Factor analysis using principal component method [duplicate]

Is factor analysis using principal component method the same as principal component analysis?
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11 views

Can non-normal data be used for factor analysis? [duplicate]

I would like to do factor analysis to derive a nutrient intake pattern. Many of these variables are not normally distributed. Is it gonna be a problem? And there are just 7 variables available. Can ...
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15 views

Implications of doing a confirmatory factor analysis with a correlation matrix as input instead of a variance-covariance matrix?

Is this possible, and if so what are the implications of doing things one way rather than another. Is one approach generally preferable? So far I have only been taught to use a variance-covariance ...
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1answer
113 views

Why is it wrong to discover factors using EFA then use CFA on the same data to confirm that factor model?

I understand that it's instead correct to cross-validate using new data. Why is it so? It is just that a model will tend to fit the data set that was used to created it better than another randomly ...
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213 views

How to interpret and code semantic differential scale into SPSS?

Is the semantic differential rating scale below considered a scale of intensity? This is a sample of 7 items contained in a group PQ. Legend: 1 and 7: (extremely), 2 and 6: (moderately), 3 and ...
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Do we need to check Internal Consistency of Validated Questionnaire?

Do I need to necessarily conduct a reliability test to determine the internal consistency of the items in a questionnaire that I have adopted, even though that instrument had already been validated ...
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20 views

How to model the factors extracted from factor analysis with multinomial response

I want to model the 5 factors obtained from factor analysis with multinomial response. I calculated the factor scores by taking the mean of the raw scores of correlated variables in a factor. When I ...
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49 views

Number of observations needed to perform pca

I read in this paper (page 3) comparing pca to factor analysis that both methods need a number of observations about 5 times the number of variables. Why? and how would you reduce the number of ...
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Interpreting results of a factor analysis

I performed factor analysis on R using factanal. Following advice I found on this tutorial, I chose the number of factors as being the number of principal components that capture 90% of the ...