Explore as variáveis
names(bd_dip)
## [1] "ID" "TMP1" "OBS1" "TMPJ1" "CONPES1" "COMUN1"
## [7] "CONHTR1" "ORGPL1" "SEGTR1" "CTREMO1" "TRBEQ1" "ATCL1"
## [13] "FIDL1" "AVGE1" "RECT1" "TMP2" "OBS2" "TMPJ2"
## [19] "CONPES2" "COMUN2" "CONHTR2" "ORGPL2" "SEGTR2" "CTREMO2"
## [25] "TRBEQ2" "ATCL2" "FIDL2" "AVGE2" "RECT2" "AI_BR"
## [31] "NF_BR" "AQ_BR" "A_BR" "B_BR" "C_BR" "E_BR"
## [37] "F_BR" "G_BR" "H_BR" "I_BR" "L_BR" "M_BR"
## [43] "N_BR" "O_BR" "Q1_BR" "Q2_BR" "Q3_BR" "Q4_BR"
## [49] "AI" "NF" "AQ" "A" "A_M" "A_F"
## [55] "B" "C" "E" "E_M" "E_F" "F"
## [61] "G" "H" "I" "I_M" "I_F" "L"
## [67] "M" "N" "O" "Q1" "Q2" "Q3"
## [73] "Q4" "FATOE_I" "FATOR_II" "FATO_III" "FATOR_IV" "FATOR_V"
## [79] "RA" "RV" "RM" "RE" "RN" "EG"
## [85] "EPN_RA" "EPN_RV" "EPN_RM" "EPN_RE" "EPN_RN" "EPN_EG"
## [91] "ETNIA" "IDADE" "SEXO" "FACES" "PAISAG" "FACILIT"
## [97] "SENSA" "TRANSI" "MISTUR" "GERENC" "RELAC" "PERCEP"
## [103] "USOFACI" "CONEMO" "REGEMO" "EXPERI" "ETRATEG" "IE1"
## [109] "IE2" "IE" "AV_DES1" "AV_DES2" "AV_DES"
vars <- names(bd_dip)[c(79:83, 94:101)]
describe(bd_dip[ , vars])
## vars n mean sd median trimmed mad min max range skew
## RA 1 153 14.88 4.42 16.00 15.37 4.45 0.00 23.00 23.00 -1.04
## RV 2 153 15.20 3.81 16.00 15.37 4.45 2.00 24.00 22.00 -0.52
## RM 3 153 10.93 4.64 11.00 10.70 4.45 0.00 23.00 23.00 0.34
## RE 4 153 9.35 4.53 9.00 9.29 4.45 0.00 19.00 19.00 0.15
## RN 5 153 9.87 4.30 10.00 10.07 4.45 0.00 19.00 19.00 -0.30
## FACES 6 121 40.25 7.73 41.43 41.05 7.60 15.09 51.73 36.64 -0.88
## PAISAG 7 120 42.89 8.75 45.19 44.10 6.79 10.19 54.36 44.16 -1.35
## FACILIT 8 114 42.29 7.63 44.72 43.20 6.51 17.70 52.63 34.92 -1.07
## SENSA 9 113 37.86 9.15 39.93 38.51 8.07 11.23 53.04 41.81 -0.68
## TRANSI 10 123 43.09 5.61 43.75 43.38 5.28 22.54 54.10 31.56 -0.64
## MISTUR 11 122 37.08 6.95 36.72 37.34 7.73 19.48 50.40 30.92 -0.28
## GERENC 12 121 41.40 8.66 42.67 42.38 7.93 12.27 56.85 44.58 -0.95
## RELAC 13 120 38.89 10.31 39.82 39.54 10.61 11.00 55.23 44.23 -0.53
## kurtosis se
## RA 1.14 0.36
## RV 0.48 0.31
## RM -0.38 0.37
## RE -0.71 0.37
## RN -0.56 0.35
## FACES 0.35 0.70
## PAISAG 1.89 0.80
## FACILIT 0.66 0.71
## SENSA -0.03 0.86
## TRANSI 0.82 0.51
## MISTUR -0.44 0.63
## GERENC 0.67 0.79
## RELAC -0.22 0.94
corr.test(bd_dip[ , vars])
## Call:corr.test(x = bd_dip[, vars])
## Correlation matrix
## RA RV RM RE RN FACES PAISAG FACILIT SENSA TRANSI
## RA 1.00 0.47 0.54 0.46 0.54 0.28 0.25 0.07 0.33 0.13
## RV 0.47 1.00 0.31 0.43 0.53 0.16 0.20 0.03 0.29 0.26
## RM 0.54 0.31 1.00 0.48 0.45 0.07 0.05 -0.17 0.14 0.12
## RE 0.46 0.43 0.48 1.00 0.52 0.33 0.16 -0.12 0.22 0.17
## RN 0.54 0.53 0.45 0.52 1.00 0.16 0.25 -0.01 0.19 0.24
## FACES 0.28 0.16 0.07 0.33 0.16 1.00 0.20 0.19 0.29 0.20
## PAISAG 0.25 0.20 0.05 0.16 0.25 0.20 1.00 0.37 0.14 0.17
## FACILIT 0.07 0.03 -0.17 -0.12 -0.01 0.19 0.37 1.00 0.25 0.25
## SENSA 0.33 0.29 0.14 0.22 0.19 0.29 0.14 0.25 1.00 0.32
## TRANSI 0.13 0.26 0.12 0.17 0.24 0.20 0.17 0.25 0.32 1.00
## MISTUR 0.32 0.34 0.17 0.41 0.24 0.42 0.15 0.20 0.43 0.27
## GERENC 0.14 0.26 0.07 0.22 0.13 0.11 0.29 0.13 0.37 0.37
## RELAC 0.26 0.27 0.14 0.21 0.22 0.08 0.18 0.12 0.39 0.22
## MISTUR GERENC RELAC
## RA 0.32 0.14 0.26
## RV 0.34 0.26 0.27
## RM 0.17 0.07 0.14
## RE 0.41 0.22 0.21
## RN 0.24 0.13 0.22
## FACES 0.42 0.11 0.08
## PAISAG 0.15 0.29 0.18
## FACILIT 0.20 0.13 0.12
## SENSA 0.43 0.37 0.39
## TRANSI 0.27 0.37 0.22
## MISTUR 1.00 0.42 0.31
## GERENC 0.42 1.00 0.43
## RELAC 0.31 0.43 1.00
## Sample Size
## RA RV RM RE RN FACES PAISAG FACILIT SENSA TRANSI MISTUR
## RA 153 153 153 153 153 121 120 114 113 123 122
## RV 153 153 153 153 153 121 120 114 113 123 122
## RM 153 153 153 153 153 121 120 114 113 123 122
## RE 153 153 153 153 153 121 120 114 113 123 122
## RN 153 153 153 153 153 121 120 114 113 123 122
## FACES 121 121 121 121 121 121 119 113 112 121 120
## PAISAG 120 120 120 120 120 119 120 112 113 120 120
## FACILIT 114 114 114 114 114 113 112 114 110 114 113
## SENSA 113 113 113 113 113 112 113 110 113 113 113
## TRANSI 123 123 123 123 123 121 120 114 113 123 122
## MISTUR 122 122 122 122 122 120 120 113 113 122 122
## GERENC 121 121 121 121 121 120 119 113 112 121 120
## RELAC 120 120 120 120 120 118 118 111 111 120 120
## GERENC RELAC
## RA 121 120
## RV 121 120
## RM 121 120
## RE 121 120
## RN 121 120
## FACES 120 118
## PAISAG 119 118
## FACILIT 113 111
## SENSA 112 111
## TRANSI 121 120
## MISTUR 120 120
## GERENC 121 119
## RELAC 119 120
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## RA RV RM RE RN FACES PAISAG FACILIT SENSA TRANSI MISTUR
## RA 0.00 0.00 0.00 0.00 0.00 0.11 0.23 1.00 0.02 1.00 0.02
## RV 0.00 0.00 0.01 0.00 0.00 1.00 0.99 1.00 0.11 0.16 0.01
## RM 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00
## RE 0.00 0.00 0.00 0.00 0.00 0.01 1.00 1.00 0.64 1.00 0.00
## RN 0.00 0.00 0.00 0.00 0.00 1.00 0.24 1.00 1.00 0.27 0.27
## FACES 0.00 0.07 0.45 0.00 0.09 0.00 0.94 1.00 0.11 0.94 0.00
## PAISAG 0.01 0.03 0.62 0.08 0.01 0.03 0.00 0.00 1.00 1.00 1.00
## FACILIT 0.43 0.75 0.06 0.22 0.89 0.05 0.00 0.00 0.37 0.27 1.00
## SENSA 0.00 0.00 0.14 0.02 0.05 0.00 0.15 0.01 0.00 0.03 0.00
## TRANSI 0.15 0.00 0.19 0.05 0.01 0.03 0.06 0.01 0.00 0.00 0.15
## MISTUR 0.00 0.00 0.06 0.00 0.01 0.00 0.10 0.04 0.00 0.00 0.00
## GERENC 0.12 0.00 0.45 0.02 0.16 0.22 0.00 0.17 0.00 0.00 0.00
## RELAC 0.00 0.00 0.14 0.02 0.02 0.41 0.06 0.22 0.00 0.02 0.00
## GERENC RELAC
## RA 1.00 0.17
## RV 0.18 0.12
## RM 1.00 1.00
## RE 0.64 0.75
## RN 1.00 0.59
## FACES 1.00 1.00
## PAISAG 0.09 1.00
## FACILIT 1.00 1.00
## SENSA 0.00 0.00
## TRANSI 0.00 0.64
## MISTUR 0.00 0.03
## GERENC 0.00 0.00
## RELAC 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option
bd_dip %>% select(vars) %>% describe %>% kable(digits = 2)
| RA |
1 |
153 |
14.88 |
4.42 |
16.00 |
15.37 |
4.45 |
0.00 |
23.00 |
23.00 |
-1.04 |
1.14 |
0.36 |
| RV |
2 |
153 |
15.20 |
3.81 |
16.00 |
15.37 |
4.45 |
2.00 |
24.00 |
22.00 |
-0.52 |
0.48 |
0.31 |
| RM |
3 |
153 |
10.93 |
4.64 |
11.00 |
10.70 |
4.45 |
0.00 |
23.00 |
23.00 |
0.34 |
-0.38 |
0.37 |
| RE |
4 |
153 |
9.35 |
4.53 |
9.00 |
9.29 |
4.45 |
0.00 |
19.00 |
19.00 |
0.15 |
-0.71 |
0.37 |
| RN |
5 |
153 |
9.87 |
4.30 |
10.00 |
10.07 |
4.45 |
0.00 |
19.00 |
19.00 |
-0.30 |
-0.56 |
0.35 |
| FACES |
6 |
121 |
40.25 |
7.73 |
41.43 |
41.05 |
7.60 |
15.09 |
51.73 |
36.64 |
-0.88 |
0.35 |
0.70 |
| PAISAG |
7 |
120 |
42.89 |
8.75 |
45.19 |
44.10 |
6.79 |
10.19 |
54.36 |
44.16 |
-1.35 |
1.89 |
0.80 |
| FACILIT |
8 |
114 |
42.29 |
7.63 |
44.72 |
43.20 |
6.51 |
17.70 |
52.63 |
34.92 |
-1.07 |
0.66 |
0.71 |
| SENSA |
9 |
113 |
37.86 |
9.15 |
39.93 |
38.51 |
8.07 |
11.23 |
53.04 |
41.81 |
-0.68 |
-0.03 |
0.86 |
| TRANSI |
10 |
123 |
43.09 |
5.61 |
43.75 |
43.38 |
5.28 |
22.54 |
54.10 |
31.56 |
-0.64 |
0.82 |
0.51 |
| MISTUR |
11 |
122 |
37.08 |
6.95 |
36.72 |
37.34 |
7.73 |
19.48 |
50.40 |
30.92 |
-0.28 |
-0.44 |
0.63 |
| GERENC |
12 |
121 |
41.40 |
8.66 |
42.67 |
42.38 |
7.93 |
12.27 |
56.85 |
44.58 |
-0.95 |
0.67 |
0.79 |
| RELAC |
13 |
120 |
38.89 |
10.31 |
39.82 |
39.54 |
10.61 |
11.00 |
55.23 |
44.23 |
-0.53 |
-0.22 |
0.94 |
bd_dip %>% select(vars) %>% corr.test() %>% .$r %>% kable(digits = 2)
| RA |
1.00 |
0.47 |
0.54 |
0.46 |
0.54 |
0.28 |
0.25 |
0.07 |
0.33 |
0.13 |
0.32 |
0.14 |
0.26 |
| RV |
0.47 |
1.00 |
0.31 |
0.43 |
0.53 |
0.16 |
0.20 |
0.03 |
0.29 |
0.26 |
0.34 |
0.26 |
0.27 |
| RM |
0.54 |
0.31 |
1.00 |
0.48 |
0.45 |
0.07 |
0.05 |
-0.17 |
0.14 |
0.12 |
0.17 |
0.07 |
0.14 |
| RE |
0.46 |
0.43 |
0.48 |
1.00 |
0.52 |
0.33 |
0.16 |
-0.12 |
0.22 |
0.17 |
0.41 |
0.22 |
0.21 |
| RN |
0.54 |
0.53 |
0.45 |
0.52 |
1.00 |
0.16 |
0.25 |
-0.01 |
0.19 |
0.24 |
0.24 |
0.13 |
0.22 |
| FACES |
0.28 |
0.16 |
0.07 |
0.33 |
0.16 |
1.00 |
0.20 |
0.19 |
0.29 |
0.20 |
0.42 |
0.11 |
0.08 |
| PAISAG |
0.25 |
0.20 |
0.05 |
0.16 |
0.25 |
0.20 |
1.00 |
0.37 |
0.14 |
0.17 |
0.15 |
0.29 |
0.18 |
| FACILIT |
0.07 |
0.03 |
-0.17 |
-0.12 |
-0.01 |
0.19 |
0.37 |
1.00 |
0.25 |
0.25 |
0.20 |
0.13 |
0.12 |
| SENSA |
0.33 |
0.29 |
0.14 |
0.22 |
0.19 |
0.29 |
0.14 |
0.25 |
1.00 |
0.32 |
0.43 |
0.37 |
0.39 |
| TRANSI |
0.13 |
0.26 |
0.12 |
0.17 |
0.24 |
0.20 |
0.17 |
0.25 |
0.32 |
1.00 |
0.27 |
0.37 |
0.22 |
| MISTUR |
0.32 |
0.34 |
0.17 |
0.41 |
0.24 |
0.42 |
0.15 |
0.20 |
0.43 |
0.27 |
1.00 |
0.42 |
0.31 |
| GERENC |
0.14 |
0.26 |
0.07 |
0.22 |
0.13 |
0.11 |
0.29 |
0.13 |
0.37 |
0.37 |
0.42 |
1.00 |
0.43 |
| RELAC |
0.26 |
0.27 |
0.14 |
0.21 |
0.22 |
0.08 |
0.18 |
0.12 |
0.39 |
0.22 |
0.31 |
0.43 |
1.00 |
bd_dip %>% select(vars) %>% fa.parallel(fa = "pc")
## Parallel analysis suggests that the number of factors = NA and the number of components = 2
bd_dip %>% select(vars) %>% fa(nfactors = 2, rotate = "varimax")
## Factor Analysis using method = minres
## Call: fa(r = ., nfactors = 2, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## RA 0.69 0.25 0.54 0.46 1.3
## RV 0.55 0.32 0.41 0.59 1.6
## RM 0.69 -0.04 0.48 0.52 1.0
## RE 0.69 0.21 0.52 0.48 1.2
## RN 0.70 0.17 0.52 0.48 1.1
## FACES 0.20 0.37 0.18 0.82 1.5
## PAISAG 0.13 0.39 0.17 0.83 1.2
## FACILIT -0.18 0.47 0.26 0.74 1.3
## SENSA 0.19 0.60 0.39 0.61 1.2
## TRANSI 0.13 0.48 0.25 0.75 1.2
## MISTUR 0.29 0.59 0.43 0.57 1.5
## GERENC 0.10 0.60 0.37 0.63 1.1
## RELAC 0.20 0.47 0.26 0.74 1.4
##
## MR1 MR2
## SS loadings 2.51 2.26
## Proportion Var 0.19 0.17
## Cumulative Var 0.19 0.37
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 3.85 with Chi Square of 589.09
## The degrees of freedom for the model are 53 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic number of observations is 122 with the empirical chi square 75.9 with prob < 0.021
## The total number of observations was 159 with Likelihood Chi Square = 114.41 with prob < 2.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.821
## RMSEA index = 0.089 and the 90 % confidence intervals are 0.064 0.107
## BIC = -154.24
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.9 0.86
## Multiple R square of scores with factors 0.8 0.74
## Minimum correlation of possible factor scores 0.6 0.47
bd_dip %>% select(vars) %>% fa(nfactors = 2, rotate = "oblimin")
## Factor Analysis using method = minres
## Call: fa(r = ., nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## RA 0.68 0.13 0.54 0.46 1.1
## RV 0.52 0.23 0.41 0.59 1.4
## RM 0.74 -0.18 0.48 0.52 1.1
## RE 0.69 0.08 0.52 0.48 1.0
## RN 0.71 0.04 0.52 0.48 1.0
## FACES 0.14 0.35 0.18 0.82 1.3
## PAISAG 0.05 0.39 0.17 0.83 1.0
## FACILIT -0.29 0.54 0.26 0.74 1.5
## SENSA 0.08 0.59 0.39 0.61 1.0
## TRANSI 0.04 0.48 0.25 0.75 1.0
## MISTUR 0.19 0.56 0.43 0.57 1.2
## GERENC -0.01 0.61 0.37 0.63 1.0
## RELAC 0.12 0.45 0.26 0.74 1.1
##
## MR1 MR2
## SS loadings 2.53 2.25
## Proportion Var 0.19 0.17
## Cumulative Var 0.19 0.37
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 0.37
## MR2 0.37 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 3.85 with Chi Square of 589.09
## The degrees of freedom for the model are 53 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic number of observations is 122 with the empirical chi square 75.9 with prob < 0.021
## The total number of observations was 159 with Likelihood Chi Square = 114.41 with prob < 2.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.821
## RMSEA index = 0.089 and the 90 % confidence intervals are 0.064 0.107
## BIC = -154.24
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.91 0.88
## Multiple R square of scores with factors 0.83 0.77
## Minimum correlation of possible factor scores 0.66 0.54
m1 <- bd_dip %>% select(vars) %>% fa(nfactors = 2, rotate = "varimax")
m2 <- bd_dip %>% select(vars) %>% fa(nfactors = 2, rotate = "oblimin")
print.psych(m1, cut = .25, sort = TRUE)
## Factor Analysis using method = minres
## Call: fa(r = ., nfactors = 2, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item MR1 MR2 h2 u2 com
## RN 5 0.70 0.52 0.48 1.1
## RM 3 0.69 0.48 0.52 1.0
## RA 1 0.69 0.25 0.54 0.46 1.3
## RE 4 0.69 0.52 0.48 1.2
## RV 2 0.55 0.32 0.41 0.59 1.6
## GERENC 12 0.60 0.37 0.63 1.1
## SENSA 9 0.60 0.39 0.61 1.2
## MISTUR 11 0.29 0.59 0.43 0.57 1.5
## TRANSI 10 0.48 0.25 0.75 1.2
## FACILIT 8 0.47 0.26 0.74 1.3
## RELAC 13 0.47 0.26 0.74 1.4
## PAISAG 7 0.39 0.17 0.83 1.2
## FACES 6 0.37 0.18 0.82 1.5
##
## MR1 MR2
## SS loadings 2.51 2.26
## Proportion Var 0.19 0.17
## Cumulative Var 0.19 0.37
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 3.85 with Chi Square of 589.09
## The degrees of freedom for the model are 53 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic number of observations is 122 with the empirical chi square 75.9 with prob < 0.021
## The total number of observations was 159 with Likelihood Chi Square = 114.41 with prob < 2.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.821
## RMSEA index = 0.089 and the 90 % confidence intervals are 0.064 0.107
## BIC = -154.24
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.9 0.86
## Multiple R square of scores with factors 0.8 0.74
## Minimum correlation of possible factor scores 0.6 0.47
print.psych(m2, cut = .25, sort = TRUE)
## Factor Analysis using method = minres
## Call: fa(r = ., nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item MR1 MR2 h2 u2 com
## RM 3 0.74 0.48 0.52 1.1
## RN 5 0.71 0.52 0.48 1.0
## RE 4 0.69 0.52 0.48 1.0
## RA 1 0.68 0.54 0.46 1.1
## RV 2 0.52 0.41 0.59 1.4
## GERENC 12 0.61 0.37 0.63 1.0
## SENSA 9 0.59 0.39 0.61 1.0
## MISTUR 11 0.56 0.43 0.57 1.2
## FACILIT 8 -0.29 0.54 0.26 0.74 1.5
## TRANSI 10 0.48 0.25 0.75 1.0
## RELAC 13 0.45 0.26 0.74 1.1
## PAISAG 7 0.39 0.17 0.83 1.0
## FACES 6 0.35 0.18 0.82 1.3
##
## MR1 MR2
## SS loadings 2.53 2.25
## Proportion Var 0.19 0.17
## Cumulative Var 0.19 0.37
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 0.37
## MR2 0.37 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 78 and the objective function was 3.85 with Chi Square of 589.09
## The degrees of freedom for the model are 53 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic number of observations is 122 with the empirical chi square 75.9 with prob < 0.021
## The total number of observations was 159 with Likelihood Chi Square = 114.41 with prob < 2.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.821
## RMSEA index = 0.089 and the 90 % confidence intervals are 0.064 0.107
## BIC = -154.24
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.91 0.88
## Multiple R square of scores with factors 0.83 0.77
## Minimum correlation of possible factor scores 0.66 0.54
diagram(m1)
diagram(m2)
