Rで因子分析をしているが，因子得点を表示できない。

前提・実現したいこと

因子分析をしているが，因子得点を表示できない。
表示できたら，因子得点のみ変数に格納したい。
なお，分析手法に最尤法は用いないため，最尤法でのみ使用できるfactanal関数は使えません。

該当のソースコード

R
1library(psych)
2library(GPArotation)
3
4dat2 = read.csv("ans2.csv", row.names = 1)
5
6KMO(dat2) #標本妥当性の確認
7
8fa.parallel( dat2, fa = "fa", use = "complete.obs" ) #スクリープロットを表示
9abline( h = 0 ) #y = 0 の横線を追加
10
11#因子負荷の推定
12result = fa(dat2, use = "complete.obs", nfactors = 4, fm = "gls", rotate = "oblimin", scores = TRUE)
13print(result, digits=3, sort=TRUE)

疑問

すみませんが，大きく3つあります。

（1）
上記の結果が以下です。

R
1Factor Analysis using method =  gls
2Call: fa(r = dat2, nfactors = 4, rotate = "oblimin", scores = TRUE, 
3    fm = "gls", use = "complete.obs")
4Standardized loadings (pattern matrix) based upon correlation matrix
5    item   GLS1   GLS4   GLS3   GLS2    h2    u2  com
6B13   13  0.885  0.041 -0.045 -0.030 0.790 0.210 1.01
7B14   14  0.839 -0.007  0.028  0.078 0.722 0.278 1.02
8B17   17  0.638 -0.300  0.149  0.165 0.400 0.600 1.70
9B06    6  0.575  0.139  0.216  0.069 0.582 0.418 1.44
10B29   29  0.522  0.183 -0.086 -0.319 0.443 0.557 2.01
11B02    2  0.433  0.281  0.271  0.039 0.585 0.415 2.49
12B22   22  0.417  0.137  0.348 -0.298 0.596 0.404 3.05
13B09    9  0.324  0.283  0.203  0.226 0.453 0.547 3.54
14B25   25  0.023  0.725 -0.012  0.225 0.624 0.376 1.19
15B31   31 -0.215  0.587  0.085  0.093 0.327 0.673 1.36
16B26   26  0.011  0.575  0.229  0.044 0.486 0.514 1.32
17B24   24  0.323  0.535 -0.053 -0.024 0.513 0.487 1.67
18B05    5  0.129  0.533 -0.054 -0.079 0.336 0.664 1.19
19B27   27  0.374  0.518 -0.104  0.025 0.530 0.470 1.92
20B19   19 -0.239  0.423  0.355  0.000 0.311 0.689 2.56
21B07    7  0.158 -0.396  0.147  0.255 0.162 0.838 2.41
22B01    1  0.265  0.343 -0.199  0.008 0.225 0.775 2.54
23B20   20 -0.063 -0.110  0.905 -0.029 0.738 0.262 1.04
24B21   21  0.180  0.044  0.633  0.228 0.578 0.422 1.44
25B10   10 -0.017  0.369  0.543  0.105 0.565 0.435 1.85
26B23   23  0.280  0.214  0.505 -0.169 0.640 0.360 2.24
27B04    4  0.214  0.028  0.468 -0.451 0.583 0.417 2.41
28B08    8  0.265  0.382  0.441 -0.220 0.739 0.261 3.17
29B18   18  0.247  0.356  0.359 -0.155 0.562 0.438 3.14
30B03    3  0.118  0.133 -0.149  0.695 0.559 0.441 1.23
31B12   12 -0.018 -0.031  0.088  0.501 0.248 0.752 1.07
32B11   11  0.134  0.299  0.004  0.487 0.414 0.586 1.84
33B15   15  0.030 -0.075  0.282  0.467 0.269 0.731 1.71
34B28   28  0.191  0.386  0.113  0.420 0.516 0.484 2.56
35B32   32 -0.226 -0.022  0.195  0.368 0.185 0.815 2.26
36B30   30 -0.173  0.198  0.122  0.365 0.200 0.800 2.31
37B16   16  0.324  0.089 -0.032  0.360 0.267 0.733 2.13
38
39                       GLS1  GLS4  GLS3  GLS2
40SS loadings           4.647 4.318 3.593 2.587
41Proportion Var        0.145 0.135 0.112 0.081
42Cumulative Var        0.145 0.280 0.392 0.473
43Proportion Explained  0.307 0.285 0.237 0.171
44Cumulative Proportion 0.307 0.592 0.829 1.000
45
46 With factor correlations of 
47       GLS1  GLS4   GLS3   GLS2
48GLS1  1.000 0.443  0.363 -0.007
49GLS4  0.443 1.000  0.333  0.117
50GLS3  0.363 0.333  1.000 -0.066
51GLS2 -0.007 0.117 -0.066  1.000
52
53Mean item complexity =  2
54Test of the hypothesis that 4 factors are sufficient.
55
56The degrees of freedom for the null model are  496  and the objective function was  22.653 with Chi Square of  1551.701
57The degrees of freedom for the model are 374  and the objective function was  8.436 
58
59The root mean square of the residuals (RMSR) is  0.067 
60The df corrected root mean square of the residuals is  0.078 
61
62The harmonic number of observations is  81 with the empirical chi square  365.432  with prob <  0.615 
63The total number of observations was  81  with Likelihood Chi Square =  555.368  with prob <  3.1e-09 
64
65Tucker Lewis Index of factoring reliability =  0.7583
66RMSEA index =  0.0764  and the 90 % confidence intervals are  0.0639 0.0911
67BIC =  -1088.156
68Fit based upon off diagonal values = 0.952
69Measures of factor score adequacy             
70                                                   GLS1  GLS4  GLS3  GLS2
71Correlation of (regression) scores with factors   0.967 0.939 0.947 0.914
72Multiple R square of scores with factors          0.935 0.882 0.896 0.835
73Minimum correlation of possible factor scores     0.870 0.764 0.792 0.669