I am working on an EFA (Exploratory Factor Analysis) using the psych package. I would like to extract the scores from the result of this EFA at the observation level, so I can use them as an independent variable in a subsequent GLM (Generalized Linear Model). The idea is to model the relationship between the factor identified in the EFA and other predictors in my dataset. I can get the factor scores, but I need them to be in a vector with the same number of observations as in my dataset, not a single value repeated across the entire dataset. How can I do this?

I can get the loadings:

fa_02$loadings

Loadings:
                 MR1    MR2   
proxy_cientifico  0.659  0.156
records           0.361  0.704
propCR                   0.473
propDD                   0.464
propVU            0.154  0.647
propEN           -0.215  0.815
label_pt          0.331 -0.104
label_fr          0.560  0.165
label_de          0.822 -0.109
label_es          0.803  0.105
label_it          0.836       
label_en          0.917       
n_languages       0.945       

                 MR1   MR2
SS loadings    4.816 2.106
Proportion Var 0.370 0.162
Cumulative Var 0.370 0.532

and I can get the factor scores:

fa_02$r.scores
          [,1]      [,2]
[1,] 1.0000000 0.3630829
[2,] 0.3630829 1.0000000

fa_02$score.cor
          [,1]      [,2]
[1,] 1.0000000 0.3403204
[2,] 0.3403204 1.0000000

But I really need the scores factor at observation levels, to use as a variable in a GLM. It’s like when we take the principal component calculated in a PCA. But in my case, the analysis I need to use is EFA to identify the underlying structure of my set of observed variables. It’s similar to when we try to identify a latent variable.