ich habe verschiedene Fragebögen, die sich jeweils aus verschiedenen Items zusammensetzen. Ich muss jetzt "Parcels" bilden abhängig von der Faktorladung. Ich kann ja mit der summary-funktion die Faktorladungen der einzelnen Items anschauen (liege ich richtig, dass die Faktorladungen die Zahlen bei "Latent Variables" und "Estimate" sind?), aber hat jemand von euch eine Idee, wie ich die Items gleich so ordnen kann, dass ich sehe welches Item die größte Faktorladung usw. hat?
Momentan sieht das z.B. so aus:
Code: Alles auswählen
modelmbi <- '
mbi =~ mbi1+mbi2+mbi3+mbi4+mbi5+mbi6+mbi7+mbi8+mbi9+mbi10+mbi11+mbi12+mbi13+mbi14+mbi15+mbi16+mbi17+mbi18+mbi19+mbi20+mbi21+mbi22
'
fitmbi <- sem(model=modelmbi, data= data_eh)
summary(fitmbi, standardized=T, fit.measures=T)
Code: Alles auswählen
lavaan 0.6-3 ended normally after 32 iterations
Optimization method NLMINB
Number of free parameters 44
Used Total
Number of observations 197 200
Estimator ML
Model Fit Test Statistic 879.301
Degrees of freedom 209
P-value (Chi-square) 0.000
Model test baseline model:
Minimum Function Test Statistic 1846.893
Degrees of freedom 231
P-value 0.000
User model versus baseline model:
Comparative Fit Index (CFI) 0.585
Tucker-Lewis Index (TLI) 0.542
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -7481.304
Loglikelihood unrestricted model (H1) -7041.653
Number of free parameters 44
Akaike (AIC) 15050.607
Bayesian (BIC) 15195.068
Sample-size adjusted Bayesian (BIC) 15055.678
Root Mean Square Error of Approximation:
RMSEA 0.128
90 Percent Confidence Interval 0.119 0.136
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.122
Parameter Estimates:
Information Expected
Information saturated (h1) model Structured
Standard Errors Standard
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
mbi =~
mbi1 1.000 1.248 0.757
mbi2 0.939 0.089 10.574 0.000 1.171 0.738
mbi3 0.992 0.100 9.895 0.000 1.239 0.695
mbi4 -0.105 0.080 -1.314 0.189 -0.131 -0.098
mbi5 0.414 0.076 5.449 0.000 0.516 0.398
mbi6 0.636 0.097 6.535 0.000 0.793 0.473
mbi7 0.038 0.079 0.485 0.628 0.048 0.036
mbi8 1.189 0.092 12.872 0.000 1.484 0.878
mbi9 0.027 0.105 0.256 0.798 0.034 0.019
mbi10 0.716 0.093 7.731 0.000 0.893 0.555
mbi11 0.645 0.089 7.252 0.000 0.804 0.522
mbi12 0.505 0.078 6.482 0.000 0.630 0.470
mbi13 0.971 0.088 11.025 0.000 1.212 0.765
mbi14 0.499 0.101 4.926 0.000 0.623 0.361
mbi15 0.300 0.083 3.630 0.000 0.374 0.268
mbi16 0.657 0.088 7.453 0.000 0.821 0.536
mbi17 0.434 0.083 5.258 0.000 0.542 0.384
mbi18 0.258 0.085 3.031 0.002 0.322 0.224
mbi19 0.298 0.081 3.671 0.000 0.372 0.271
mbi20 0.556 0.084 6.598 0.000 0.694 0.478
mbi21 0.405 0.080 5.056 0.000 0.506 0.370
mbi22 0.530 0.101 5.251 0.000 0.661 0.384
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mbi1 1.162 0.133 8.742 0.000 1.162 0.427
.mbi2 1.149 0.130 8.872 0.000 1.149 0.456
.mbi3 1.639 0.180 9.102 0.000 1.639 0.516
.mbi4 1.792 0.181 9.916 0.000 1.792 0.990
.mbi5 1.418 0.145 9.761 0.000 1.418 0.842
.mbi6 2.179 0.225 9.672 0.000 2.179 0.776
.mbi7 1.777 0.179 9.924 0.000 1.777 0.999
.mbi8 0.655 0.094 6.960 0.000 0.655 0.229
.mbi9 3.092 0.312 9.924 0.000 3.092 1.000
.mbi10 1.796 0.188 9.536 0.000 1.796 0.692
.mbi11 1.724 0.180 9.597 0.000 1.724 0.727
.mbi12 1.403 0.145 9.678 0.000 1.403 0.779
.mbi13 1.039 0.120 8.675 0.000 1.039 0.414
.mbi14 2.596 0.265 9.794 0.000 2.596 0.870
.mbi15 1.816 0.184 9.858 0.000 1.816 0.928
.mbi16 1.670 0.175 9.572 0.000 1.670 0.713
.mbi17 1.693 0.173 9.774 0.000 1.693 0.852
.mbi18 1.958 0.198 9.879 0.000 1.958 0.950
.mbi19 1.756 0.178 9.856 0.000 1.756 0.927
.mbi20 1.628 0.168 9.666 0.000 1.628 0.772
.mbi21 1.612 0.165 9.786 0.000 1.612 0.863
.mbi22 2.532 0.259 9.774 0.000 2.532 0.853
mbi 1.558 0.256 6.093 0.000 1.000 1.000