I'm running a non-recursive model with Lavaan. However, 2 things happened that I didn't quite understand. First, gooodness-of-fit indices and some standard errors were "NA". Second, the two coefficients between two variables of different directions were not consistent (non-recursive part: ResidentialMobility--Author): one was positive, and another one was negative (at least they should be in the same direction; otherwise, how to explain?). Can someone help me out? Please let me know if you want me to clarify it more. Thanks!
model01<-'ResidentialMobility~a*Coun
SavingMotherPercentage~e*Affect
SavingMotherPercentage~f*Author
SavingMotherPercentage~g*Recipro
Affect~b*ResidentialMobility
Author~c*ResidentialMobility
Recipro~d*ResidentialMobility
ResidentialMobility~h*Affect
ResidentialMobility~i*Author
ResidentialMobility~j*Recipro
Affect~~Author+Recipro+ResidentialMobility
Author~~Recipro+ResidentialMobility
Recipro~~ResidentialMobility
Coun~SavingMotherPercentage
ab:=a*b
ac:=a*c
ad:=a*d
be:=b*e
cf:=c*f
dg:=d*g
'
fit <- cfa(model01, estimator = "MLR", data = data01, missing = "FIML")
summary(fit, standardized = TRUE, fit.measures = TRUE)
Output:
lavaan (0.5-21) converged normally after 93 iterations
Used Total
Number of observations 502 506
Number of missing patterns 4
Estimator ML Robust
Minimum Function Test Statistic NA NA
Degrees of freedom -2 -2
Minimum Function Value 0.0005232772506
Scaling correction factor
for the Yuan-Bentler correction
User model versus baseline model:
Comparative Fit Index (CFI) NA NA
Tucker-Lewis Index (TLI) NA NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5057.346 -5057.346
Loglikelihood unrestricted model (H1) -5057.084 -5057.084
Number of free parameters 29 29
Akaike (AIC) 10172.693 10172.693
Bayesian (BIC) 10295.032 10295.032
Sample-size adjusted Bayesian (BIC) 10202.984 10202.984
Root Mean Square Error of Approximation:
RMSEA NA NA
90 Percent Confidence Interval NA NA NA NA
P-value RMSEA <= 0.05 NA NA
Standardized Root Mean Square Residual:
SRMR 0.006 0.006
Parameter Estimates:
Information Observed
Standard Errors Robust.huber.white
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ResidentialMobility ~
Coun (a) -1.543 0.255 -6.052 0.000 -1.543 -0.540
SavingMotherPercentage ~
Affect (e) 3.093 1.684 1.837 0.066 3.093 0.122
Author (f) 2.618 0.923 2.835 0.005 2.618 0.145
Recipro (g) 0.061 1.344 0.046 0.964 0.061 0.003
Affect ~
RsdntlMblt (b) -0.311 0.075 -4.125 0.000 -0.311 -0.570
Author ~
RsdntlMblt (c) -0.901 0.119 -7.567 0.000 -0.901 -1.180
Recipro ~
RsdntlMblt (d) -0.313 0.082 -3.841 0.000 -0.313 -0.512
ResidentialMobility ~
Affect (h) -0.209 0.193 -1.082 0.279 -0.209 -0.114
Author (i) 0.475 0.192 2.474 0.013 0.475 0.363
Recipro (j) 0.178 0.346 0.514 0.607 0.178 0.109
Coun ~
SvngMthrPr 0.003 0.001 2.225 0.026 0.003 0.108
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Affect ~~
.Author 0.667 0.171 3.893 0.000 0.667 0.534
.Recipro 0.669 0.119 5.623 0.000 0.669 0.773
.ResidentialMobility ~~
.Affect 0.624 0.144 4.347 0.000 0.624 0.474
.Author ~~
.Recipro 0.565 0.173 3.267 0.001 0.565 0.416
.ResidentialMobility ~~
.Author 1.029 0.288 3.572 0.000 1.029 0.499
.Recipro 0.564 0.304 1.851 0.064 0.564 0.395
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 1.813 NA 1.813 1.270
.SvngMthrPrcntg 29.591 7.347 4.027 0.000 29.591 1.499
.Affect 5.701 0.169 33.797 0.000 5.701 7.320
.Author 5.569 0.275 20.259 0.000 5.569 5.109
.Recipro 5.149 0.186 27.642 0.000 5.149 5.889
.Coun 0.367 0.069 5.336 0.000 0.367 0.735
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.ResidentlMblty 2.169 0.259 8.378 0.000 2.169 1.064
.SvngMthrPrcntg 363.792 23.428 15.528 0.000 363.792 0.934
.Affect 0.797 0.129 6.153 0.000 0.797 1.314
.Author 1.957 0.343 5.713 0.000 1.957 1.647
.Recipro 0.941 0.126 7.439 0.000 0.941 1.231
.Coun 0.242 0.004 54.431 0.000 0.242 0.969
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
ab 0.480 0.120 3.991 0.000 0.480 0.308
ac 1.390 0.261 5.328 0.000 1.390 0.637
ad 0.483 0.133 3.640 0.000 0.483 0.276
be -0.962 0.548 -1.757 0.079 -0.962 -0.070
cf -2.359 0.851 -2.771 0.006 -2.359 -0.171
dg -0.019 0.421 -0.046 0.964 -0.019 -0.001
Why you get NA I think is because you have specified a model with -2 in degrees of freedom. You should specify the model differently so that you get a positive number of degrees of freedom.