I wanted to run a path analysis but could not report the RMSEA and its p-value. The results showed that RMSEA = 0.00 Confidence interval = 0.00 and p-value: NA. I wonder what it does mean.
In my analysis, I have four IVs and three DVs. This is the code what I wanted to run:
model1 <-"ML_NC+ML_NS+Preventive ~ TPE+covidconcern+MisinfoEx+CorrectAction"
fit_analysis1 <- sem(model1, data = dataset2)
summary(fit_analysis1, fit.measures=TRUE, standardized=T,rsquare=T)
And also, if I want to include the control variables and mediating variables in the path analysis, how do I add these?
This is the results.
lavaan 0.6-12 ended normally after 15 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 18
Number of observations 272
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Model Test Baseline Model:
Test statistic 419.201
Degrees of freedom 15
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -849.964
Loglikelihood unrestricted model (H1) -849.964
Akaike (AIC) 1735.928
Bayesian (BIC) 1800.833
Sample-size adjusted Bayesian (BIC) 1743.759
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value RMSEA <= 0.05 NA
Standardized Root Mean Square Residual:
SRMR 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ML_NC ~
TPE 0.208 0.044 4.700 0.000 0.208 0.247
covidconcern 0.113 0.054 2.083 0.037 0.113 0.121
MisinfoEx 0.298 0.053 5.609 0.000 0.298 0.309
CorrectAction 0.205 0.066 3.107 0.002 0.205 0.186
ML_NS ~
TPE 0.183 0.040 4.633 0.000 0.183 0.245
covidconcern 0.128 0.049 2.648 0.008 0.128 0.155
MisinfoEx 0.272 0.048 5.713 0.000 0.272 0.317
CorrectAction 0.130 0.059 2.205 0.027 0.130 0.132
Preventive ~
TPE 0.073 0.052 1.410 0.159 0.073 0.068
covidconcern 0.610 0.064 9.578 0.000 0.610 0.509
MisinfoEx -0.105 0.063 -1.686 0.092 -0.105 -0.085
CorrectAction 0.300 0.077 3.870 0.000 0.300 0.211
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ML_NC ~~
.ML_NS 0.295 0.034 8.642 0.000 0.295 0.615
.Preventive 0.008 0.038 0.198 0.843 0.008 0.012
.ML_NS ~~
.Preventive 0.053 0.034 1.540 0.123 0.053 0.094
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ML_NC 0.535 0.046 11.662 0.000 0.535 0.744
.ML_NS 0.429 0.037 11.662 0.000 0.429 0.752
.Preventive 0.739 0.063 11.662 0.000 0.739 0.623
R-Square:
Estimate
ML_NC 0.256
ML_NS 0.248
Preventive 0.377
This is data structure, and this is the results
> dput(head(dataset2))
structure(list(ID = 1:6, Demo = c(1L, 1L, 3L, 3L, 5L, 1L), Gender = c(2L,
1L, 2L, 1L, 2L, 1L), Ethnicity = c(6L, 1L, 2L, 1L, 4L, 3L), Education = c(2L,
3L, 4L, 2L, 6L, 3L), Income = c(1L, 1L, 6L, 5L, 4L, 3L), Poli = c(1,
3, 2, 2, 2, 3), covidconcern = c(4.33333333333333, 4, 5, 5, 5,
2.33333333333333), TPEm = c(4.8, 5, 5, 5, 4.8, 2.8), TPEo = c(5,
5, 5, 5, 4.8, 3.6), TPE = c(0.2, 0, 0, 0, 0, 0.8), MisinfoEx = c(4,
4.83333333333333, 5, 4, 4.66666666666667, 4.16666666666667),
CorrectAction = c(3.6, 5, 5, 4.6, 4.8, 4.4), ML_NC = c(3.83333333333333,
4, 5, 4.5, 4.83333333333333, 5), ML_NS = c(4, 4.4, 5, 4.6,
4.6, 5), ML = c(3.91666666666667, 4.2, 5, 4.55, 4.71666666666667,
5), Preventive = c(4, 4.75, 4.75, 3.75, 5, 4.5)), row.names = c(NA,
-6L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x7f90cc83e2e0>)
>
Thank you.
