Can anybody here suggest me whether I can use the model (SEM in Lavaan) in my article based on the results presented below. I am aware of the comparatively higher RMSEA and lower CFI & TLI values. Thanks.
summary(mod.est, fit.measures = TRUE)
lavaan 0.6-9 ended normally after 319 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 52
Number of observations 56
Model Test User Model:
Test statistic 688.049
Degrees of freedom 273
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1739.061
Degrees of freedom 300
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.712
Tucker-Lewis Index (TLI) 0.683
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) 1813.539
Loglikelihood unrestricted model (H1) 2157.563
Akaike (AIC) -3523.077
Bayesian (BIC) -3417.759
Sample-size adjusted Bayesian (BIC) -3581.193
Root Mean Square Error of Approximation:
RMSEA 0.165
90 Percent confidence interval - lower 0.149
90 Percent confidence interval - upper 0.180
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.085
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
SRI =~
SR 0.138 0.016 8.661 0.000
NDVI 0.074 0.009 8.366 0.000
GNDVI 0.044 0.007 6.556 0.000
NWI 0.084 0.014 5.968 0.000
PRI 0.078 0.010 7.555 0.000
NCPI -0.140 0.031 -4.565 0.000
ARI -0.180 0.103 -1.748 0.080
XES -0.075 0.013 -5.591 0.000
mCRI -0.151 0.029 -5.149 0.000
SIPI -0.047 0.006 -8.450 0.000
PSRI -0.079 0.020 -4.031 0.000
PNSI 0.092 0.010 8.867 0.000
EVI 0.108 0.012 9.239 0.000
MSAVI 0.056 0.006 9.197 0.000
OSAVI 0.086 0.009 9.881 0.000
SG 0.019 0.006 3.447 0.001
CTD 0.074 0.014 5.355 0.000
YC =~
DTH 0.001 0.002 0.490 0.624
PH 0.014 0.004 3.400 0.001
NSM 0.070 0.014 4.822 0.000
NKS 0.052 0.013 3.936 0.000
WKS 0.053 0.012 4.310 0.000
HKW 0.024 0.008 2.901 0.004
BY 0.099 0.011 9.354 0.000
Regressions:
Estimate Std.Err z-value P(>|z|)
GY ~
SRI 0.045 0.023 1.278 0.021
YC 0.067 0.024 2.788 0.005
Covariances:
Estimate Std.Err z-value P(>|z|)
SRI ~~
YC 0.913 0.035 25.984 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.SR 0.005 0.001 4.890 0.000
.NDVI 0.002 0.000 4.962 0.000
.GNDVI 0.002 0.000 5.169 0.000
.NWI 0.007 0.001 5.200 0.000
.PRI 0.003 0.001 5.087 0.000
.NCPI 0.042 0.008 5.247 0.000
.ARI 0.567 0.107 5.286 0.000
.XES 0.007 0.001 5.215 0.000
.mCRI 0.036 0.007 5.231 0.000
.SIPI 0.001 0.000 4.944 0.000
.PSRI 0.018 0.003 5.258 0.000
.PNSI 0.002 0.000 4.824 0.000
.EVI 0.002 0.000 4.653 0.000
.MSAVI 0.000 0.000 4.677 0.000
.OSAVI 0.001 0.000 3.927 0.000
.SG 0.002 0.000 5.268 0.000
.CTD 0.008 0.002 5.224 0.000
.DTH 0.000 0.000 5.290 0.000
.PH 0.001 0.000 5.224 0.000
.NSM 0.009 0.002 5.136 0.000
.NKS 0.008 0.002 5.197 0.000
.WKS 0.007 0.001 5.174 0.000
.HKW 0.003 0.001 5.244 0.000
.BY 0.001 0.001 1.886 0.059
.GY 0.001 0.000 3.690 0.000
SRI 1.000
YC 1.000