VALIDASI PU-NIN
Heru Wiryanto
August 18, 2017
MENARIK DAN DATA EKSPLORASI
Dengan jumlah item 28 dan responden untuk peinilaian pribadi 136 orang
library(psych)
DATA<-read.csv("~/Desktop/PU-NIN.csv")
DATA<-DATA[1:28]
describe(DATA)## vars n mean sd median trimmed mad min max range skew
## i1OP 1 136 80.55 21.38 85.0 85.00 14.83 0 95 95 -2.42
## i2OP 2 136 78.64 17.98 85.0 81.91 14.83 0 95 95 -2.59
## i3OP 3 136 70.74 17.05 67.5 71.50 25.95 0 95 95 -0.92
## i4OP 4 136 78.31 16.12 75.0 80.41 0.00 0 95 95 -1.76
## i5INT 5 136 82.17 14.97 85.0 84.64 14.83 0 95 95 -2.78
## i6INT 6 136 79.78 14.40 85.0 81.77 14.83 0 95 95 -2.74
## i7INT 7 136 87.21 7.09 85.0 88.00 0.00 50 95 45 -1.42
## i8INT 8 136 79.56 12.42 75.0 80.14 14.83 50 95 45 -0.31
## i9INT 9 136 81.47 12.08 75.0 82.73 14.83 50 95 45 -0.51
## i10INT 10 136 89.19 11.44 95.0 92.05 0.00 50 95 45 -1.89
## i11KOM 11 136 82.79 13.45 85.0 84.23 14.83 50 95 45 -0.87
## i12KOM 12 136 85.74 11.39 90.0 87.64 7.41 50 95 45 -1.06
## i13KOM 13 136 84.41 8.65 85.0 85.36 0.00 50 95 45 -1.68
## i14KJ 14 136 82.61 13.32 85.0 83.91 14.83 50 95 45 -0.66
## i15KJ 15 136 80.51 11.31 85.0 81.86 14.83 50 95 45 -1.07
## i16KJ 16 136 81.95 11.91 85.0 83.36 14.83 50 95 45 -0.60
## i17KP 17 136 84.23 10.24 85.0 85.00 14.83 50 95 45 -0.84
## i18KP 18 136 81.03 12.67 85.0 82.23 14.83 50 95 45 -0.75
## i19KP 19 136 81.88 12.49 85.0 82.91 14.83 0 95 95 -2.28
## i20KP 20 136 84.45 12.29 85.0 86.55 14.83 50 95 45 -1.30
## i21KP 21 136 84.63 11.75 85.0 86.41 14.83 50 95 45 -0.88
## i22KP 22 136 72.68 14.72 60.0 71.64 0.00 50 95 45 0.48
## i23KP 23 136 84.23 6.83 85.0 85.00 0.00 50 95 45 -2.63
## i24KP 24 136 86.32 11.32 85.0 88.68 14.83 50 95 45 -1.59
## i25DIS 25 136 87.87 9.46 90.0 89.73 7.41 50 95 45 -1.73
## i26DIS 26 136 70.15 13.04 60.0 69.05 14.83 50 95 45 0.54
## i27DIS 27 136 75.15 14.86 75.0 75.77 22.24 50 95 45 -0.21
## i28DIS 28 136 82.98 14.04 85.0 84.73 14.83 50 95 45 -0.93
## kurtosis se
## i1OP 6.21 1.83
## i2OP 8.38 1.54
## i3OP 2.17 1.46
## i4OP 6.29 1.38
## i5INT 11.42 1.28
## i6INT 11.88 1.23
## i7INT 5.31 0.61
## i8INT -0.99 1.06
## i9INT -0.20 1.04
## i10INT 2.24 0.98
## i11KOM -0.63 1.15
## i12KOM 0.19 0.98
## i13KOM 4.57 0.74
## i14KJ -0.93 1.14
## i15KJ 0.91 0.97
## i16KJ -0.17 1.02
## i17KP 0.88 0.88
## i18KP -0.41 1.09
## i19KP 12.14 1.07
## i20KP 0.91 1.05
## i21KP -0.22 1.01
## i22KP -1.45 1.26
## i23KP 9.21 0.59
## i24KP 1.91 0.97
## i25DIS 3.13 0.81
## i26DIS -0.88 1.12
## i27DIS -1.17 1.27
## i28DIS -0.48 1.20METODE PRINCIPAL KOMPONEN ANALISIS
p3p <-principal(DATA,6,n.obs = 136,rotate="Promax")
p3p## Principal Components Analysis
## Call: principal(r = DATA, nfactors = 6, rotate = "Promax", n.obs = 136)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC4 RC5 RC3 RC6 h2 u2 com
## i1OP -0.32 0.88 0.06 0.07 -0.09 -0.02 0.58 0.42 1.3
## i2OP -0.17 0.83 0.20 -0.28 -0.10 0.07 0.64 0.36 1.5
## i3OP -0.06 0.44 -0.03 0.15 0.16 0.53 0.62 0.38 2.4
## i4OP 0.15 0.67 -0.05 0.16 -0.16 -0.08 0.56 0.44 1.4
## i5INT 0.29 0.68 -0.08 0.06 -0.16 0.15 0.74 0.26 1.6
## i6INT 0.14 0.63 -0.10 0.10 0.17 0.10 0.65 0.35 1.4
## i7INT -0.05 0.04 -0.34 0.93 0.34 0.02 0.68 0.32 1.6
## i8INT -0.10 -0.03 0.37 0.40 0.15 0.35 0.52 0.48 3.4
## i9INT 0.32 0.45 -0.06 0.21 -0.08 -0.13 0.50 0.50 2.6
## i10INT 0.44 0.14 -0.18 -0.24 0.28 0.23 0.51 0.49 3.7
## i11KOM -0.09 0.02 0.79 -0.07 0.36 0.03 0.72 0.28 1.5
## i12KOM 0.62 0.01 0.36 -0.35 0.06 0.13 0.64 0.36 2.4
## i13KOM 0.17 0.16 -0.04 0.34 -0.20 0.32 0.42 0.58 3.6
## i14KJ 0.02 0.22 0.32 0.32 0.13 -0.13 0.45 0.55 3.5
## i15KJ 0.57 0.17 0.10 -0.14 0.08 -0.02 0.50 0.50 1.4
## i16KJ 0.69 0.06 -0.13 0.10 -0.13 0.03 0.50 0.50 1.2
## i17KP 0.18 -0.11 0.31 0.40 0.04 0.13 0.50 0.50 2.8
## i18KP 0.13 0.24 0.06 0.05 0.38 -0.17 0.37 0.63 2.5
## i19KP 0.45 -0.22 0.09 0.15 0.07 -0.03 0.27 0.73 1.9
## i20KP 0.69 0.05 0.11 -0.08 -0.09 0.06 0.55 0.45 1.1
## i21KP 0.49 -0.15 0.27 0.10 -0.19 -0.10 0.41 0.59 2.3
## i22KP 0.03 0.04 0.85 -0.29 -0.13 -0.09 0.60 0.40 1.3
## i23KP 0.62 0.00 -0.03 0.28 -0.14 -0.12 0.54 0.46 1.6
## i24KP 0.91 -0.16 -0.16 -0.04 0.00 -0.05 0.58 0.42 1.1
## i25DIS 0.30 -0.14 -0.17 0.12 -0.01 0.53 0.40 0.60 2.1
## i26DIS -0.25 -0.25 0.01 0.41 0.93 0.01 0.71 0.29 1.7
## i27DIS 0.35 -0.04 -0.08 0.13 0.22 -0.60 0.47 0.53 2.1
## i28DIS 0.09 0.05 0.30 0.06 0.61 -0.10 0.59 0.41 1.6
##
## RC1 RC2 RC4 RC5 RC3 RC6
## SS loadings 4.26 3.58 2.16 1.97 1.82 1.44
## Proportion Var 0.15 0.13 0.08 0.07 0.07 0.05
## Cumulative Var 0.15 0.28 0.36 0.43 0.49 0.54
## Proportion Explained 0.28 0.23 0.14 0.13 0.12 0.09
## Cumulative Proportion 0.28 0.51 0.66 0.79 0.91 1.00
##
## With component correlations of
## RC1 RC2 RC4 RC5 RC3 RC6
## RC1 1.00 0.52 0.45 0.41 0.27 0.23
## RC2 0.52 1.00 0.30 0.17 0.32 0.17
## RC4 0.45 0.30 1.00 0.43 0.10 0.11
## RC5 0.41 0.17 0.43 1.00 -0.15 0.06
## RC3 0.27 0.32 0.10 -0.15 1.00 0.08
## RC6 0.23 0.17 0.11 0.06 0.08 1.00
##
## Mean item complexity = 2
## Test of the hypothesis that 6 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
## with the empirical chi square 416.1 with prob < 1.5e-13
##
## Fit based upon off diagonal values = 0.94print(p3p,cut=0.3,digits=3)## Principal Components Analysis
## Call: principal(r = DATA, nfactors = 6, rotate = "Promax", n.obs = 136)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC4 RC5 RC3 RC6 h2 u2 com
## i1OP -0.319 0.876 0.578 0.422 1.31
## i2OP 0.830 0.643 0.357 1.49
## i3OP 0.438 0.534 0.625 0.375 2.36
## i4OP 0.666 0.564 0.436 1.40
## i5INT 0.681 0.741 0.259 1.64
## i6INT 0.630 0.648 0.352 1.44
## i7INT -0.341 0.927 0.342 0.683 0.317 1.57
## i8INT 0.366 0.402 0.351 0.521 0.479 3.44
## i9INT 0.322 0.450 0.496 0.504 2.63
## i10INT 0.442 0.512 0.488 3.68
## i11KOM 0.788 0.362 0.720 0.280 1.46
## i12KOM 0.617 0.357 -0.348 0.639 0.361 2.39
## i13KOM 0.344 0.322 0.424 0.576 3.65
## i14KJ 0.318 0.325 0.455 0.545 3.46
## i15KJ 0.569 0.502 0.498 1.42
## i16KJ 0.691 0.497 0.503 1.21
## i17KP 0.306 0.404 0.502 0.498 2.79
## i18KP 0.384 0.369 0.631 2.53
## i19KP 0.453 0.269 0.731 1.88
## i20KP 0.692 0.546 0.454 1.14
## i21KP 0.491 0.414 0.586 2.35
## i22KP 0.845 0.597 0.403 1.31
## i23KP 0.617 0.540 0.460 1.61
## i24KP 0.914 0.585 0.415 1.13
## i25DIS 0.531 0.398 0.602 2.12
## i26DIS 0.414 0.926 0.707 0.293 1.72
## i27DIS 0.348 -0.602 0.465 0.535 2.09
## i28DIS 0.606 0.593 0.407 1.62
##
## RC1 RC2 RC4 RC5 RC3 RC6
## SS loadings 4.257 3.577 2.163 1.970 1.821 1.442
## Proportion Var 0.152 0.128 0.077 0.070 0.065 0.052
## Cumulative Var 0.152 0.280 0.357 0.427 0.492 0.544
## Proportion Explained 0.280 0.235 0.142 0.129 0.120 0.095
## Cumulative Proportion 0.280 0.514 0.656 0.786 0.905 1.000
##
## With component correlations of
## RC1 RC2 RC4 RC5 RC3 RC6
## RC1 1.000 0.518 0.449 0.411 0.274 0.226
## RC2 0.518 1.000 0.302 0.173 0.322 0.174
## RC4 0.449 0.302 1.000 0.434 0.097 0.109
## RC5 0.411 0.173 0.434 1.000 -0.152 0.058
## RC3 0.274 0.322 0.097 -0.152 1.000 0.080
## RC6 0.226 0.174 0.109 0.058 0.080 1.000
##
## Mean item complexity = 2
## Test of the hypothesis that 6 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.064
## with the empirical chi square 416.096 with prob < 1.51e-13
##
## Fit based upon off diagonal values = 0.94METODA FACTOR ANALYSIS
f3t <- fa(DATA,6,n.obs=136)## Loading required namespace: GPArotationprint(f3t,cut=0.3,digits=3)## Factor Analysis using method = minres
## Call: fa(r = DATA, nfactors = 6, n.obs = 136)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 MR6 MR4 MR5 h2 u2 com
## i1OP 0.707 0.4992 0.501 1.06
## i2OP 0.693 0.6021 0.398 1.24
## i3OP 0.439 0.4030 0.597 1.89
## i4OP 0.338 0.4366 0.563 3.15
## i5INT 0.355 0.502 0.7550 0.245 2.57
## i6INT 0.631 0.6734 0.327 1.38
## i7INT 0.576 0.4506 0.549 1.51
## i8INT 0.474 0.362 0.4334 0.567 2.56
## i9INT 0.342 0.3953 0.605 2.77
## i10INT 0.427 0.2951 0.705 1.95
## i11KOM 0.807 0.7287 0.271 1.09
## i12KOM 0.436 0.359 0.5704 0.430 3.20
## i13KOM 0.2630 0.737 3.03
## i14KJ 0.3543 0.646 4.81
## i15KJ 0.422 0.4511 0.549 2.24
## i16KJ 0.467 0.305 0.4495 0.551 2.11
## i17KP 0.318 0.344 0.3881 0.612 2.90
## i18KP 0.2479 0.752 3.00
## i19KP 0.327 0.1873 0.813 1.86
## i20KP 0.608 0.5027 0.497 1.12
## i21KP 0.507 0.3509 0.649 1.99
## i22KP 0.539 0.3146 0.685 1.34
## i23KP 0.575 0.4607 0.539 1.40
## i24KP 0.699 0.4867 0.513 1.19
## i25DIS 0.0960 0.904 2.52
## i26DIS 0.654 0.5042 0.496 1.43
## i27DIS 0.0891 0.911 2.10
## i28DIS 0.583 0.6217 0.378 2.02
##
## MR1 MR2 MR3 MR6 MR4 MR5
## SS loadings 3.422 2.015 1.980 2.111 1.192 1.290
## Proportion Var 0.122 0.072 0.071 0.075 0.043 0.046
## Cumulative Var 0.122 0.194 0.265 0.340 0.383 0.429
## Proportion Explained 0.285 0.168 0.165 0.176 0.099 0.107
## Cumulative Proportion 0.285 0.453 0.618 0.793 0.893 1.000
##
## With factor correlations of
## MR1 MR2 MR3 MR6 MR4 MR5
## MR1 1.000 0.286 0.387 0.379 0.098 0.271
## MR2 0.286 1.000 0.268 0.389 0.055 0.052
## MR3 0.387 0.268 1.000 0.266 0.278 0.128
## MR6 0.379 0.389 0.266 1.000 0.072 0.190
## MR4 0.098 0.055 0.278 0.072 1.000 0.066
## MR5 0.271 0.052 0.128 0.190 0.066 1.000
##
## Mean item complexity = 2.1
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 378 and the objective function was 11.232 with Chi Square of 1402.114
## The degrees of freedom for the model are 225 and the objective function was 2.577
##
## The root mean square of the residuals (RMSR) is 0.043
## The df corrected root mean square of the residuals is 0.056
##
## The harmonic number of observations is 136 with the empirical chi square 189.491 with prob < 0.959
## The total number of observations was 136 with Likelihood Chi Square = 311.427 with prob < 0.000119
##
## Tucker Lewis Index of factoring reliability = 0.8517
## RMSEA index = 0.0636 and the 90 % confidence intervals are 0.0381 0.0671
## BIC = -793.92
## Fit based upon off diagonal values = 0.973
## Measures of factor score adequacy
## MR1 MR2 MR3 MR6
## Correlation of scores with factors 0.917 0.888 0.904 0.892
## Multiple R square of scores with factors 0.841 0.789 0.817 0.796
## Minimum correlation of possible factor scores 0.682 0.579 0.635 0.592
## MR4 MR5
## Correlation of scores with factors 0.848 0.814
## Multiple R square of scores with factors 0.718 0.662
## Minimum correlation of possible factor scores 0.437 0.324fa.diagram(f3t)
METODA FACTOR ANALYSIS DENGAN PRINCIPAL AXIS (PA)
f3 <- fa(DATA,6,n.obs = 136,fm="pa")
f3o <- target.rot(f3)
print(f3o,cut=0.3,digits=3)##
## Call: NULL
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA6 PA4 PA5 h2 u2
## i1OP 0.721 0.5199 0.480
## i2OP 0.695 0.5414 0.459
## i3OP 0.458 0.2751 0.725
## i4OP 0.330 0.2516 0.748
## i5INT 0.338 0.501 0.4567 0.543
## i6INT 0.642 0.4612 0.539
## i7INT 0.587 0.4077 0.592
## i8INT 0.563 0.388 0.4265 0.573
## i9INT 0.300 0.2340 0.766
## i10INT 0.422 0.2604 0.740
## i11KOM 0.799 0.6739 0.326
## i12KOM 0.482 0.4436 0.556
## i13KOM 0.1780 0.822
## i14KJ 0.2304 0.770
## i15KJ 0.441 0.2899 0.710
## i16KJ 0.481 0.3445 0.655
## i17KP 0.381 0.377 0.2848 0.715
## i18KP 0.1554 0.845
## i19KP 0.331 0.1502 0.850
## i20KP 0.608 0.3915 0.608
## i21KP 0.486 -0.317 0.3792 0.621
## i22KP 0.545 0.3389 0.661
## i23KP 0.556 0.3989 0.601
## i24KP 0.739 0.5351 0.465
## i25DIS 0.0741 0.926
## i26DIS 0.630 0.5199 0.480
## i27DIS 0.0982 0.902
## i28DIS 0.616 0.4970 0.503
##
## PA1 PA2 PA3 PA6 PA4 PA5
## SS loadings 2.812 1.596 1.566 1.599 1.147 1.097
## Proportion Var 0.100 0.057 0.056 0.057 0.041 0.039
## Cumulative Var 0.100 0.157 0.213 0.271 0.311 0.351
## Proportion Explained 0.286 0.163 0.160 0.163 0.117 0.112
## Cumulative Proportion 0.286 0.449 0.609 0.771 0.888 1.000
## PA1 PA2 PA3 PA6 PA4 PA5
## PA1 1.000 0.193 0.170 0.113 0.026 0.066
## PA2 0.193 1.000 -0.008 -0.013 -0.003 0.032
## PA3 0.170 -0.008 1.000 0.155 0.043 -0.257
## PA6 0.113 -0.013 0.155 1.000 0.016 0.036
## PA4 0.026 -0.003 0.043 0.016 1.000 0.032
## PA5 0.066 0.032 -0.257 0.036 0.032 1.000fa.diagram(f3o)
METODA FACTOR ANALYSIS DENGAN WLS (WLS)
f3w <- fa(DATA,6,n.obs = 136,fm="wls")
print(f3w,cut=0.3,digits=3)## Factor Analysis using method = wls
## Call: fa(r = DATA, nfactors = 6, n.obs = 136, fm = "wls")
## Standardized loadings (pattern matrix) based upon correlation matrix
## WLS1 WLS6 WLS3 WLS2 WLS4 WLS5 h2 u2 com
## i1OP 0.593 0.4834 0.517 1.45
## i2OP 0.665 0.6327 0.367 1.34
## i3OP 0.525 0.3717 0.628 1.18
## i4OP 0.450 0.4562 0.544 2.10
## i5INT 0.719 0.7950 0.205 1.35
## i6INT 0.764 0.6964 0.304 1.17
## i7INT 0.308 -0.309 0.3575 0.643 4.53
## i8INT 0.314 0.460 0.3906 0.609 2.63
## i9INT 0.365 0.3918 0.608 2.36
## i10INT 0.388 0.2904 0.710 2.34
## i11KOM 0.781 0.7198 0.280 1.13
## i12KOM 0.545 0.6902 0.310 2.17
## i13KOM 0.349 0.2496 0.750 2.17
## i14KJ 0.351 0.3747 0.625 3.32
## i15KJ 0.4385 0.562 3.60
## i16KJ 0.391 0.396 0.4598 0.540 2.66
## i17KP 0.367 0.338 0.3964 0.604 3.06
## i18KP 0.300 0.2522 0.748 2.63
## i19KP 0.332 0.1904 0.810 1.75
## i20KP 0.576 0.5221 0.478 1.37
## i21KP 0.615 0.3604 0.640 1.33
## i22KP 0.543 0.3262 0.674 1.39
## i23KP 0.632 0.4497 0.550 1.03
## i24KP 0.563 0.4522 0.548 1.68
## i25DIS 0.1000 0.900 1.85
## i26DIS 0.644 0.4710 0.529 1.36
## i27DIS 0.0877 0.912 2.55
## i28DIS 0.591 0.6083 0.392 1.94
##
## WLS1 WLS6 WLS3 WLS2 WLS4 WLS5
## SS loadings 3.319 3.184 1.857 1.446 1.285 0.924
## Proportion Var 0.119 0.114 0.066 0.052 0.046 0.033
## Cumulative Var 0.119 0.232 0.299 0.350 0.396 0.429
## Proportion Explained 0.276 0.265 0.155 0.120 0.107 0.077
## Cumulative Proportion 0.276 0.541 0.696 0.816 0.923 1.000
##
## With factor correlations of
## WLS1 WLS6 WLS3 WLS2 WLS4 WLS5
## WLS1 1.000 0.464 0.362 0.097 0.141 0.176
## WLS6 0.464 1.000 0.299 0.314 0.147 0.168
## WLS3 0.362 0.299 1.000 0.174 0.320 0.190
## WLS2 0.097 0.314 0.174 1.000 0.041 0.163
## WLS4 0.141 0.147 0.320 0.041 1.000 0.077
## WLS5 0.176 0.168 0.190 0.163 0.077 1.000
##
## Mean item complexity = 2.1
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 378 and the objective function was 11.232 with Chi Square of 1402.114
## The degrees of freedom for the model are 225 and the objective function was 2.563
##
## The root mean square of the residuals (RMSR) is 0.043
## The df corrected root mean square of the residuals is 0.056
##
## The harmonic number of observations is 136 with the empirical chi square 191.623 with prob < 0.948
## The total number of observations was 136 with Likelihood Chi Square = 309.693 with prob < 0.000153
##
## Tucker Lewis Index of factoring reliability = 0.8547
## RMSEA index = 0.0631 and the 90 % confidence intervals are 0.0374 0.0667
## BIC = -795.655
## Fit based upon off diagonal values = 0.972
## Measures of factor score adequacy
## WLS1 WLS6 WLS3 WLS2
## Correlation of scores with factors 0.914 0.942 0.897 0.865
## Multiple R square of scores with factors 0.835 0.888 0.804 0.748
## Minimum correlation of possible factor scores 0.671 0.775 0.608 0.496
## WLS4 WLS5
## Correlation of scores with factors 0.846 0.811
## Multiple R square of scores with factors 0.716 0.658
## Minimum correlation of possible factor scores 0.433 0.317fa.diagram(f3w)
METODE CLUSTER ANALYSIS
ic <- iclust(DATA)
summary(ic)## ICLUST (Item Cluster Analysis)Call: iclust(r.mat = DATA)
## ICLUST
##
## Purified Alpha:
## [1] 0.89
##
## Guttman Lambda6*
## [1] 0.93
##
## Original Beta:
## [1] 0.31
##
## Cluster size:
## [1] 28
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.89KONGRUENSI FACTOR ANALYSIS DAN CLUSTER
round(factor.congruence(list(p3p,f3t,f3o,f3w)),2)## RC1 RC2 RC4 RC5 RC3 RC6 MR1 MR2 MR3 MR6 MR4
## RC1 1.00 0.02 0.01 0.01 -0.09 -0.02 0.95 -0.02 0.15 0.35 0.00
## RC2 0.02 1.00 0.03 0.03 -0.11 0.13 0.11 0.92 0.10 0.61 -0.04
## RC4 0.01 0.03 1.00 -0.19 0.14 -0.03 0.12 0.16 0.92 -0.16 0.07
## RC5 0.01 0.03 -0.19 1.00 0.33 0.10 0.17 -0.01 0.02 0.10 0.29
## RC3 -0.09 -0.11 0.14 0.33 1.00 -0.03 -0.04 -0.10 0.27 0.18 0.94
## RC6 -0.02 0.13 -0.03 0.10 -0.03 1.00 -0.05 0.06 0.13 0.47 -0.21
## MR1 0.95 0.11 0.12 0.17 -0.04 -0.05 1.00 0.14 0.23 0.27 0.09
## MR2 -0.02 0.92 0.16 -0.01 -0.10 0.06 0.14 1.00 0.12 0.33 0.04
## MR3 0.15 0.10 0.92 0.02 0.27 0.13 0.23 0.12 1.00 0.13 0.13
## MR6 0.35 0.61 -0.16 0.10 0.18 0.47 0.27 0.33 0.13 1.00 0.07
## MR4 0.00 -0.04 0.07 0.29 0.94 -0.21 0.09 0.04 0.13 0.07 1.00
## MR5 0.13 0.12 -0.06 0.95 0.12 0.24 0.29 0.08 0.15 0.17 0.07
## PA1 0.98 0.02 0.10 0.10 -0.06 -0.08 0.99 0.04 0.20 0.24 0.07
## PA2 -0.13 0.91 0.13 -0.04 -0.18 0.06 0.02 0.99 0.06 0.26 -0.05
## PA3 -0.04 0.06 0.89 0.16 0.21 0.15 0.08 0.10 0.96 0.00 0.05
## PA6 0.23 0.66 -0.19 0.07 0.19 0.47 0.15 0.39 0.08 0.99 0.09
## PA4 0.01 0.03 0.14 0.26 0.91 -0.23 0.12 0.14 0.17 0.06 0.99
## PA5 0.15 0.05 -0.03 0.94 0.11 0.22 0.31 0.02 0.17 0.11 0.06
## WLS1 0.83 0.11 0.13 0.40 -0.07 -0.04 0.95 0.18 0.23 0.14 0.07
## WLS6 0.36 0.72 -0.12 0.30 0.10 0.45 0.35 0.48 0.17 0.95 0.03
## WLS3 0.08 0.11 0.91 0.09 0.26 0.12 0.19 0.13 0.99 0.09 0.12
## WLS2 -0.11 0.82 0.21 -0.26 -0.13 -0.06 0.03 0.95 0.07 0.15 0.04
## WLS4 0.07 0.00 0.07 0.35 0.93 -0.17 0.17 0.08 0.16 0.13 0.99
## WLS5 0.58 0.01 0.20 -0.67 0.03 -0.01 0.43 -0.03 0.21 0.34 0.06
## MR5 PA1 PA2 PA3 PA6 PA4 PA5 WLS1 WLS6 WLS3 WLS2
## RC1 0.13 0.98 -0.13 -0.04 0.23 0.01 0.15 0.83 0.36 0.08 -0.11
## RC2 0.12 0.02 0.91 0.06 0.66 0.03 0.05 0.11 0.72 0.11 0.82
## RC4 -0.06 0.10 0.13 0.89 -0.19 0.14 -0.03 0.13 -0.12 0.91 0.21
## RC5 0.95 0.10 -0.04 0.16 0.07 0.26 0.94 0.40 0.30 0.09 -0.26
## RC3 0.12 -0.06 -0.18 0.21 0.19 0.91 0.11 -0.07 0.10 0.26 -0.13
## RC6 0.24 -0.08 0.06 0.15 0.47 -0.23 0.22 -0.04 0.45 0.12 -0.06
## MR1 0.29 0.99 0.02 0.08 0.15 0.12 0.31 0.95 0.35 0.19 0.03
## MR2 0.08 0.04 0.99 0.10 0.39 0.14 0.02 0.18 0.48 0.13 0.95
## MR3 0.15 0.20 0.06 0.96 0.08 0.17 0.17 0.23 0.17 0.99 0.07
## MR6 0.17 0.24 0.26 0.00 0.99 0.06 0.11 0.14 0.95 0.09 0.15
## MR4 0.07 0.07 -0.05 0.05 0.09 0.99 0.06 0.07 0.03 0.12 0.04
## MR5 1.00 0.21 0.06 0.28 0.12 0.06 1.00 0.52 0.41 0.21 -0.19
## PA1 0.21 1.00 -0.08 0.03 0.11 0.09 0.23 0.91 0.29 0.15 -0.05
## PA2 0.06 -0.08 1.00 0.07 0.33 0.05 0.00 0.08 0.41 0.08 0.96
## PA3 0.28 0.03 0.07 1.00 -0.04 0.09 0.32 0.16 0.09 0.98 0.04
## PA6 0.12 0.11 0.33 -0.04 1.00 0.08 0.05 0.02 0.93 0.04 0.23
## PA4 0.06 0.09 0.05 0.09 0.08 1.00 0.04 0.10 0.04 0.16 0.14
## PA5 1.00 0.23 0.00 0.32 0.05 0.04 1.00 0.55 0.35 0.24 -0.24
## WLS1 0.52 0.91 0.08 0.16 0.02 0.10 0.55 1.00 0.30 0.21 0.03
## WLS6 0.41 0.29 0.41 0.09 0.93 0.04 0.35 0.30 1.00 0.15 0.25
## WLS3 0.21 0.15 0.08 0.98 0.04 0.16 0.24 0.21 0.15 1.00 0.07
## WLS2 -0.19 -0.05 0.96 0.04 0.23 0.14 -0.24 0.03 0.25 0.07 1.00
## WLS4 0.14 0.14 -0.02 0.08 0.14 0.99 0.13 0.15 0.11 0.15 0.04
## WLS5 -0.63 0.50 -0.12 -0.05 0.29 0.07 -0.62 0.14 0.13 0.11 0.07
## WLS4 WLS5
## RC1 0.07 0.58
## RC2 0.00 0.01
## RC4 0.07 0.20
## RC5 0.35 -0.67
## RC3 0.93 0.03
## RC6 -0.17 -0.01
## MR1 0.17 0.43
## MR2 0.08 -0.03
## MR3 0.16 0.21
## MR6 0.13 0.34
## MR4 0.99 0.06
## MR5 0.14 -0.63
## PA1 0.14 0.50
## PA2 -0.02 -0.12
## PA3 0.08 -0.05
## PA6 0.14 0.29
## PA4 0.99 0.07
## PA5 0.13 -0.62
## WLS1 0.15 0.14
## WLS6 0.11 0.13
## WLS3 0.15 0.11
## WLS2 0.04 0.07
## WLS4 1.00 0.05
## WLS5 0.05 1.00f3 <- fa(DATA,6,fm="pa")
factor.congruence(f3,ic)## [,1]
## PA1 0.77
## PA2 0.52
## PA3 0.54
## PA6 0.61
## PA4 0.26
## PA5 0.44Analisa Omega
omegaSem(DATA,n.obs=138,6)## Loading required namespace: lavaan## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
##
## Call: omegaSem(m = DATA, nfactors = 6, n.obs = 138)
## Omega
## Call: omega(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option)
## Alpha: 0.89
## G.6: 0.93
## Omega Hierarchical: 0.62
## Omega H asymptotic: 0.68
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## i1OP 0.33 0.62 0.50 0.50 0.22
## i2OP 0.38 0.61 0.60 0.40 0.24
## i3OP 0.45 0.35 0.40 0.60 0.50
## i4OP 0.49 0.30 0.22 0.44 0.56 0.55
## i5INT 0.63 0.31 0.41 0.76 0.24 0.52
## i6INT 0.58 0.20 0.51 0.67 0.33 0.51
## i7INT 0.27 0.22 0.55 0.45 0.55 0.16
## i8INT 0.38 0.39 0.35 0.43 0.57 0.33
## i9INT 0.49 0.26 0.23 0.40 0.60 0.60
## i10INT 0.36 0.34 0.30 0.70 0.44
## i11KOM 0.46 0.67 0.73 0.27 0.29
## i12KOM 0.55 0.33 0.21 0.30 -0.21 0.57 0.43 0.54
## i13KOM 0.34 0.27 0.26 0.74 0.44
## i14KJ 0.43 0.22 0.35 0.65 0.53
## i15KJ 0.53 0.32 0.22 0.45 0.55 0.63
## i16KJ 0.47 0.35 0.25 0.45 0.55 0.50
## i17KP 0.40 0.26 0.33 0.39 0.61 0.42
## i18KP 0.36 0.27 0.25 0.75 0.52
## i19KP 0.29 0.25 0.19 0.81 0.44
## i20KP 0.53 0.46 0.50 0.50 0.56
## i21KP 0.33 0.38 -0.22 0.35 0.65 0.31
## i22KP 0.29 0.45 0.31 0.69 0.27
## i23KP 0.44 0.43 0.23 0.46 0.54 0.43
## i24KP 0.44 0.52 0.49 0.51 0.40
## i25DIS 0.20 0.10 0.90 0.41
## i26DIS 0.64 0.50 0.50 0.03
## i27DIS 0.09 0.91 0.24
## i28DIS 0.43 0.21 0.57 0.62 0.38 0.30
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 4.85 1.56 1.27 1.03 1.11 1.06 0.99
##
## general/max 3.1 max/min = 1.59
## mean percent general = 0.4 with sd = 0.15 and cv of 0.36
## Explained Common Variance of the general factor = 0.41
##
## The degrees of freedom are 225 and the fit is 2.58
## The number of observations was 136 with Chi Square = 311.43 with prob < 0.00012
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.064 and the 10 % confidence intervals are 0.038 0.067
## BIC = -793.92
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 350 and the fit is 5.52
## The number of observations was 136 with Chi Square = 685.14 with prob < 5.9e-24
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.091 and the 10 % confidence intervals are 0.075 0.094
## BIC = -1034.29
##
## Measures of factor score adequacy
## g F1* F2* F3* F4*
## Correlation of scores with factors 0.80 0.73 0.79 0.74 0.80
## Multiple R square of scores with factors 0.65 0.54 0.63 0.54 0.63
## Minimum correlation of factor score estimates 0.29 0.08 0.26 0.09 0.27
## F5* F6*
## Correlation of scores with factors 0.83 0.77
## Multiple R square of scores with factors 0.69 0.60
## Minimum correlation of factor score estimates 0.37 0.20
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4*
## Omega total for total scores and subscales 0.92 0.80 0.68 0.71 0.70
## Omega general for total scores and subscales 0.62 0.48 0.26 0.44 0.25
## Omega group for total scores and subscales 0.12 0.31 0.42 0.28 0.45
## F5* F6*
## Omega total for total scores and subscales 0.57 0.56
## Omega general for total scores and subscales 0.16 0.29
## Omega group for total scores and subscales 0.41 0.27
##
## The following analyses were done using the lavaan package
##
## Omega Hierarchical from a confirmatory model using sem = 0.79
## Omega Total from a confirmatory model using sem = 0.92
## With loadings of
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## i1OP 0.42 1.11 1.42 -0.42 0.12
## i2OP 0.47 0.34 0.34 0.66 0.65
## i3OP 0.56 0.20 0.35 0.65 0.90
## i4OP 0.65 0.43 0.57 0.98
## i5INT 0.78 0.22 0.67 0.33 0.91
## i6INT 0.70 0.54 0.77 0.23 0.64
## i7INT 0.29 0.57 0.40 0.60 0.21
## i8INT 0.42 0.28 0.25 0.75 0.71
## i9INT 0.62 0.39 0.61 0.99
## i10INT 0.40 0.25 0.22 0.78 0.73
## i11KOM 0.45 0.62 0.59 0.41 0.34
## i12KOM 0.57 0.31 0.42 0.58 0.77
## i13KOM 0.41 0.26 0.24 0.76 0.70
## i14KJ 0.51 0.28 0.72 0.93
## i15KJ 0.60 0.25 0.42 0.58 0.86
## i16KJ 0.53 0.34 0.40 0.60 0.70
## i17KP 0.44 0.29 0.28 0.72 0.69
## i18KP 0.41 0.30 0.26 0.74 0.65
## i19KP 0.27 0.36 0.20 0.80 0.36
## i20KP 0.58 0.37 0.48 0.52 0.70
## i21KP 0.34 0.36 0.24 0.76 0.48
## i22KP 0.30 0.45 0.29 0.71 0.31
## i23KP 0.46 0.46 0.43 0.57 0.49
## i24KP 0.46 0.43 0.40 0.60 0.53
## i25DIS 0.23 0.07 0.93 0.76
## i26DIS 0.52 0.27 0.73 0.01
## i27DIS 0.06 0.94 0.43
## i28DIS 0.45 0.70 0.69 0.31 0.29
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 6.32 1.09 1.36 0.44 0.67 0.88 0.49
##
## The degrees of freedom of the confimatory model are 322 and the fit is 540.8577 with p = 2.470246e-13
## general/max 4.64 max/min = 3.11
## mean percent general = 0.6 with sd = 0.26 and cv of 0.44
## Explained Common Variance of the general factor = 0.56
##
## Measures of factor score adequacy
## g F1* F2* F3* F4*
## Correlation of scores with factors 0.93 0.83 1.34 0.86 0.97
## Multiple R square of scores with factors 0.87 0.70 1.80 0.74 0.94
## Minimum correlation of factor score estimates 0.73 0.39 2.60 0.49 0.88
## F5* F6*
## Correlation of scores with factors 0.94 0.79
## Multiple R square of scores with factors 0.89 0.63
## Minimum correlation of factor score estimates 0.78 0.26
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4*
## Omega total for total scores and subscales 0.92 0.83 0.85 0.79 0.63
## Omega general for total scores and subscales 0.79 0.57 0.43 0.63 0.27
## Omega group for total scores and subscales 0.10 0.25 0.42 0.15 0.36
## F5* F6*
## Omega total for total scores and subscales 0.59 0.60
## Omega general for total scores and subscales 0.17 0.38
## Omega group for total scores and subscales 0.42 0.22
##
## To get the standard sem fit statistics, ask for summary on the fitted objectAnalisa Reliabilitas Split Half
splitHalf(DATA)## Split half reliabilities
## Call: splitHalf(r = DATA)
##
## Maximum split half reliability (lambda 4) = 0.94
## Guttman lambda 6 = 0.93
## Average split half reliability = 0.89
## Guttman lambda 3 (alpha) = 0.89
## Minimum split half reliability (beta) = 0.8Very Simple Structure Analysis (VSS)
my.vss <- vss(DATA,title="Very Simple Structure of inventory",n=6,rotate="varimax",diagonal=FALSE)
print(my.vss,digits =2) ##
## Very Simple Structure of Very Simple Structure of inventory
## Call: vss(x = DATA, n = 6, rotate = "varimax", diagonal = FALSE, title = "Very Simple Structure of inventory")
## VSS complexity 1 achieves a maximimum of 0.72 with 1 factors
## VSS complexity 2 achieves a maximimum of 0.77 with 2 factors
##
## The Velicer MAP achieves a minimum of 0.01 with 2 factors
## BIC achieves a minimum of -1066.12 with 1 factors
## Sample Size adjusted BIC achieves a minimum of -82.14 with 6 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex
## 1 0.72 0.00 0.016 350 653 1.3e-20 22 0.72 0.087 -1066 41 1.0
## 2 0.50 0.77 0.015 323 530 2.7e-12 18 0.77 0.077 -1056 -35 1.5
## 3 0.45 0.74 0.015 297 441 1.0e-07 15 0.81 0.068 -1018 -78 1.7
## 4 0.45 0.72 0.017 272 394 1.8e-06 14 0.83 0.067 -942 -82 1.9
## 5 0.39 0.67 0.018 248 354 1.1e-05 12 0.84 0.066 -864 -80 2.1
## 6 0.34 0.61 0.021 225 311 1.2e-04 11 0.86 0.064 -794 -82 2.4
## eChisq SRMR eCRMS eBIC
## 1 757 0.086 0.089 -962
## 2 507 0.070 0.076 -1080
## 3 347 0.058 0.066 -1112
## 4 281 0.052 0.062 -1055
## 5 231 0.047 0.059 -987
## 6 189 0.043 0.056 -916VSS.plot(my.vss)
VSS.scree(cor(DATA), main ="scree plot")
#now, some simulated data with two factors
VSS(sim.circ(nvar=24),fm="pa" ,title="VSS of 24 circumplex variables")
##
## Very Simple Structure of VSS of 24 circumplex variables
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
## VSS complexity 1 achieves a maximimum of 0.6 with 2 factors
## VSS complexity 2 achieves a maximimum of 0.84 with 2 factors
##
## The Velicer MAP achieves a minimum of 0.01 with 2 factors
## BIC achieves a minimum of -1207.23 with 2 factors
## Sample Size adjusted BIC achieves a minimum of -480.37 with 2 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC
## 1 0.44 0.00 0.0451 252 2031 4.3e-275 34.4 0.44 0.12 465 1265
## 2 0.60 0.84 0.0058 229 216 7.2e-01 9.8 0.84 0.00 -1207 -480
## 3 0.59 0.83 0.0081 207 186 8.6e-01 9.4 0.85 0.00 -1101 -444
## 4 0.58 0.82 0.0107 186 156 9.5e-01 8.9 0.86 0.00 -1000 -410
## 5 0.58 0.82 0.0133 166 132 9.8e-01 8.5 0.86 0.00 -900 -373
## 6 0.57 0.79 0.0163 147 111 9.9e-01 8.3 0.87 0.00 -803 -336
## 7 0.57 0.80 0.0199 129 95 9.9e-01 8.0 0.87 0.00 -707 -297
## 8 0.55 0.78 0.0239 112 73 1.0e+00 7.5 0.88 0.00 -623 -268
## complex eChisq SRMR eCRMS eBIC
## 1 1.0 9066 0.181 0.190 7500
## 2 1.4 170 0.025 0.027 -1253
## 3 1.5 142 0.023 0.026 -1145
## 4 1.5 117 0.021 0.025 -1039
## 5 1.6 96 0.019 0.024 -936
## 6 1.8 78 0.017 0.023 -836
## 7 1.9 65 0.015 0.022 -737
## 8 2.0 49 0.013 0.021 -647VSS(sim.item(nvar=28),fm="pa" ,title="VSS of 28 simple structure variables")
##
## Very Simple Structure of VSS of 28 simple structure variables
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title, use = use, cor = cor)
## VSS complexity 1 achieves a maximimum of 0.85 with 6 factors
## VSS complexity 2 achieves a maximimum of 0.87 with 8 factors
##
## The Velicer MAP achieves a minimum of 0 with 2 factors
## BIC achieves a minimum of -1693.92 with 2 factors
## Sample Size adjusted BIC achieves a minimum of -668.7 with 2 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC
## 1 0.44 0.00 0.0515 350 2428 5.8e-307 44.4 0.44 0.11 252 1363
## 2 0.85 0.86 0.0048 323 313 6.4e-01 11.5 0.86 0.00 -1694 -669
## 3 0.85 0.86 0.0063 297 273 8.3e-01 11.0 0.86 0.00 -1572 -630
## 4 0.85 0.86 0.0080 272 235 9.5e-01 10.5 0.87 0.00 -1455 -592
## 5 0.85 0.86 0.0099 248 205 9.8e-01 10.1 0.87 0.00 -1337 -550
## 6 0.85 0.87 0.0119 225 179 9.9e-01 9.8 0.88 0.00 -1219 -505
## 7 0.85 0.87 0.0144 203 152 1.0e+00 9.3 0.88 0.00 -1109 -465
## 8 0.81 0.87 0.0168 182 131 1.0e+00 8.9 0.89 0.00 -1000 -423
## complex eChisq SRMR eCRMS eBIC
## 1 1.0 12601 0.183 0.190 10426
## 2 1.0 247 0.026 0.028 -1760
## 3 1.1 207 0.023 0.026 -1639
## 4 1.1 172 0.021 0.025 -1519
## 5 1.2 143 0.019 0.024 -1398
## 6 1.2 122 0.018 0.023 -1276
## 7 1.3 103 0.016 0.022 -1159
## 8 1.3 85 0.015 0.022 -1046