I have built a bifactor model in Rstudio by taking items with negative variances off of the items they loaded on until I got a model that converged. However, I need to get the omega statistics for this model and when I use the omegaFromSem function on the fitted model, I get omega values of over 20 when they should be less than 1. Why are my omega values so high?
Below is the code and results to create my model and get the omega:
#Create a model for bifactor analysis
model_bif <- '
#Define the general factor
g =~ DTW_1 + DTW_2 + DTW_3 + DTW_6 + DTW_7 + DTW_8 + DTW_9 + DTW_10 + DTW_11 + DTW_12 + DTW_13 + DTW_14 + DTW_15 + DTW_16 + DTW_17 + DTW_18 + DTW_19 + DTW_20 + DTW_21 + DTW_22
#Define the specific factors
n =~ DTW_1 + DTW_2 + DTW_3 + DTW_4 + DTW_5 + DTW_6
m =~ DTW_7 + DTW_8 + DTW_9 + DTW_10
s=~ DTW_17 + DTW_19 + DTW_21
'
#Fit the model
fit_bif <- cfa(model_bif, data = dataset, orthogonal = TRUE)
summary(fit_bif, fit.measures = TRUE, standardized = TRUE)
lavaan 0.6.15 ended normally after 97 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 55
Used Total
Number of observations 296 302
Model Test User Model:
Test statistic 467.855
Degrees of freedom 198
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3266.707
Degrees of freedom 231
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.911
Tucker-Lewis Index (TLI) 0.896
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -6953.172
Loglikelihood unrestricted model (H1) -6719.244
Akaike (AIC) 14016.344
Bayesian (BIC) 14219.313
Sample-size adjusted Bayesian (SABIC) 14044.891
Root Mean Square Error of Approximation:
RMSEA 0.068
90 Percent confidence interval - lower 0.060
90 Percent confidence interval - upper 0.076
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.006
Standardized Root Mean Square Residual:
SRMR 0.056
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
g =~
DTW_1 1.000 0.155 0.141
DTW_2 2.552 0.993 2.570 0.010 0.397 0.349
DTW_3 2.265 0.918 2.468 0.014 0.352 0.283
DTW_6 2.589 0.988 2.622 0.009 0.402 0.366
DTW_7 2.709 1.085 2.497 0.013 0.421 0.377
DTW_8 3.002 1.202 2.498 0.012 0.466 0.379
DTW_9 2.557 1.032 2.477 0.013 0.397 0.360
DTW_10 2.485 1.032 2.408 0.016 0.386 0.311
DTW_11 3.322 1.246 2.667 0.008 0.516 0.795
DTW_12 3.459 1.303 2.654 0.008 0.537 0.717
DTW_13 3.183 1.208 2.635 0.008 0.495 0.632
DTW_14 3.588 1.349 2.660 0.008 0.557 0.752
DTW_15 3.081 1.170 2.633 0.008 0.479 0.623
DTW_16 2.946 1.103 2.671 0.008 0.458 0.826
DTW_17 3.208 1.204 2.665 0.008 0.498 0.783
DTW_18 3.506 1.330 2.636 0.008 0.545 0.635
DTW_19 3.132 1.182 2.650 0.008 0.487 0.699
DTW_20 3.170 1.203 2.635 0.008 0.493 0.630
DTW_21 3.135 1.178 2.662 0.008 0.487 0.763
DTW_22 3.112 1.172 2.655 0.008 0.484 0.724
n =~
DTW_1 1.000 0.648 0.589
DTW_2 0.617 0.121 5.113 0.000 0.400 0.352
DTW_3 0.643 0.133 4.816 0.000 0.417 0.335
DTW_4 1.038 0.137 7.560 0.000 0.673 0.644
DTW_5 1.063 0.135 7.846 0.000 0.689 0.722
DTW_6 0.748 0.121 6.181 0.000 0.485 0.442
m =~
DTW_7 1.000 0.770 0.691
DTW_8 0.726 0.103 7.030 0.000 0.559 0.454
DTW_9 0.926 0.101 9.136 0.000 0.714 0.646
DTW_10 1.034 0.115 9.020 0.000 0.797 0.642
s =~
DTW_17 1.000 0.224 0.351
DTW_19 1.310 0.228 5.739 0.000 0.293 0.421
DTW_21 1.318 0.240 5.486 0.000 0.295 0.462
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
g ~~
n 0.000 0.000 0.000
m 0.000 0.000 0.000
s 0.000 0.000 0.000
n ~~
m 0.000 0.000 0.000
s 0.000 0.000 0.000
m ~~
s 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.DTW_1 0.767 0.078 9.792 0.000 0.767 0.633
.DTW_2 0.977 0.086 11.388 0.000 0.977 0.755
.DTW_3 1.249 0.108 11.534 0.000 1.249 0.808
.DTW_6 0.809 0.075 10.834 0.000 0.809 0.671
.DTW_7 0.473 0.065 7.248 0.000 0.473 0.380
.DTW_8 0.987 0.090 10.958 0.000 0.987 0.650
.DTW_9 0.552 0.065 8.526 0.000 0.552 0.453
.DTW_10 0.757 0.085 8.876 0.000 0.757 0.491
.DTW_11 0.155 0.015 10.528 0.000 0.155 0.368
.DTW_12 0.273 0.024 11.159 0.000 0.273 0.486
.DTW_13 0.367 0.032 11.534 0.000 0.367 0.600
.DTW_14 0.239 0.022 10.930 0.000 0.239 0.435
.DTW_15 0.362 0.031 11.565 0.000 0.362 0.612
.DTW_16 0.098 0.010 10.114 0.000 0.098 0.318
.DTW_17 0.107 0.012 8.823 0.000 0.107 0.264
.DTW_18 0.440 0.038 11.525 0.000 0.440 0.597
.DTW_19 0.161 0.019 8.340 0.000 0.161 0.333
.DTW_20 0.369 0.032 11.541 0.000 0.369 0.603
.DTW_21 0.084 0.016 5.282 0.000 0.084 0.205
.DTW_22 0.213 0.019 11.119 0.000 0.213 0.476
.DTW_4 0.638 0.071 9.011 0.000 0.638 0.585
.DTW_5 0.437 0.059 7.401 0.000 0.437 0.479
g 0.024 0.018 1.336 0.182 1.000 1.000
n 0.420 0.088 4.777 0.000 1.000 1.000
m 0.594 0.096 6.213 0.000 1.000 1.000
s 0.050 0.014 3.585 0.000 1.000 1.000
#Get omega functions
omegaFromSem(fit_bif, m = NULL, flip = TRUE, plot = TRUE)
The following analyses were done using the lavaan package
Omega Hierarchical from a confirmatory model using sem = 21.24
Omega Total from a confirmatory model using sem = 2.04
With loadings of
g F1* F2* F3* h2 u2 p2
DTW_1 1.0 1.00 2.00 -1.00 0.50
DTW_2 2.5 0.62 6.89 -5.89 0.94
DTW_3 2.3 0.64 5.54 -4.54 0.92
DTW_6 2.6 0.75 7.26 -6.26 0.92
DTW_7 2.7 1.00 8.34 -7.34 0.88
DTW_8 3.0 0.73 9.54 -8.54 0.94
DTW_9 2.6 0.93 7.40 -6.40 0.89
DTW_10 2.5 1.03 7.24 -6.24 0.85
DTW_11 3.3 11.04 -10.04 1.00
DTW_12 3.5 11.96 -10.96 1.00
DTW_13 3.2 10.13 -9.13 1.00
DTW_14 3.6 12.87 -11.87 1.00
DTW_15 3.1 9.50 -8.50 1.00
DTW_16 3.0 8.68 -7.68 1.00
DTW_17 3.2 1.0 11.29 -10.29 0.91
DTW_18 3.5 12.29 -11.29 1.00
DTW_19 3.1 1.3 11.52 -10.52 0.85
DTW_20 3.2 10.05 -9.05 1.00
DTW_21 3.1 1.3 11.57 -10.57 0.85
DTW_22 3.1 9.68 -8.68 1.00
DTW_4 1.04 1.08 -0.08 0.00
DTW_5 1.06 1.13 -0.13 0.00
With sum of squared loadings of:
g F1* F2* F3*
174.5 4.6 3.5 4.5
The degrees of freedom of the confirmatory model are 198 and the fit is 467.8555 with p = 0
general/max 38.27 max/min = 1.32
mean percent general = 0.84 with sd = 0.29 and cv of 0.35
Explained Common Variance of the general factor = 0.93
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 5.08 1.50 1.45 2.36
Multiple R square of scores with factors 25.84 2.24 2.11 5.55
Minimum correlation of factor score estimates 50.68 3.48 3.22 10.10
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 2.04 19.18 13.00 13.98
Omega general for total scores and subscales 21.24 18.83 11.63 12.19
Omega group for total scores and subscales 0.33 0.34 1.37 1.79
To get the standard sem fit statistics, ask for summary on the fitted object>