# How do I specify a lavaan sem model with more than one single-indicator factor?

I am trying to specify a lavaan CFA that has some factors with three or more indicators, one factor with two indicators, and two factors with one indicator. For the single indicator factors, I tried to follow the advice previously posted to specify the variance of the observed variable to 0 so the latent variable will account for all of the variance in the observed variable. However, when I do so, I still get a covariance matrix that is not positive definite where the latent variance does not equal the variance for the single items. Is this still the correct way to specify single indicator factors or is there another way? I've created a reproducible example below.

Simulated data with a positive definite covariate matrix.  library(MASS) mu <- c(2.5,3,3.1,3.5,2.1,1.5,2,4,4.2,4.5) Sigma <- matrix(.5, nrow=10, ncol=10) + diag(10)*.5 set.seed(1527) rawvars <- mvrnorm(n=200, mu=mu, Sigma=Sigma)

Convert latent responses to positive ordered categories (to create likert-type items).

i1 = findInterval(rawvars[,1], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i2 = findInterval(rawvars[,2], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i3 = findInterval(rawvars[,3], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i4 = findInterval(rawvars[,4], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i5 = findInterval(rawvars[,5], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i6 = findInterval(rawvars[,6], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i7 = findInterval(rawvars[,7], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i8 = findInterval(rawvars[,8], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i9 = findInterval(rawvars[,9], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) i10 = findInterval(rawvars[,10], vec=c(-Inf,2,2.75,3.5,4.25,Inf)) df <- data.frame(cbind(i1,i2,i3,i4,i5,i6,i7,i8,i9,i10)) 

Confirm all items have small to moderate positive correlations.

> round(cor(df),2) i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i1 1.00 0.53 0.55 0.50 0.50 0.42 0.51 0.48 0.51 0.38 i2 0.53 1.00 0.51 0.48 0.39 0.45 0.42 0.50 0.55 0.40 i3 0.55 0.51 1.00 0.46 0.49 0.37 0.43 0.52 0.48 0.42 i4 0.50 0.48 0.46 1.00 0.34 0.43 0.35 0.50 0.42 0.42 i5 0.50 0.39 0.49 0.34 1.00 0.37 0.43 0.36 0.35 0.39 i6 0.42 0.45 0.37 0.43 0.37 1.00 0.34 0.38 0.39 0.32 i7 0.51 0.42 0.43 0.35 0.43 0.34 1.00 0.40 0.46 0.37 i8 0.48 0.50 0.52 0.50 0.36 0.38 0.40 1.00 0.39 0.35 i9 0.51 0.55 0.48 0.42 0.35 0.39 0.46 0.39 1.00 0.43 i10 0.38 0.40 0.42 0.42 0.39 0.32 0.37 0.35 0.43 1.00 

Built model constraining two item factors to be equal and specifying the variance on the observed variables (i1 and i10) to 0.

> library(lavaan) fa.mod <- ' f1=~ i1 f2=~ i2 + i3 + i4 f3=~ i5 + i6 + i7 f4=~ ai8 + ai9 f5=~ i10 i1~~ 0*i1 i10~~ 0*i10' 

Model fit was not positive definite and the latent variance does not equal the variance for the single items.

> fa.fit<- sem(fa.mod,data=df) Warning message: In lav_object_post_check(object) : lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate. 

 
> inspect(fa.fit,"cov.lv")
f1    f2    f3    f4    f5
f1 1.312
f2 0.755 0.766
f3 0.595 0.555 0.451
f4 0.565 0.614 0.419 0.383
f5 0.394 0.465 0.360 0.356 0.828

> summary(fa.fit,fit.measures=T,standardized=T)
lavaan (0.5-23.1097) converged normally after  39 iterations

Number of observations                           200

Estimator                                         ML
Minimum Function Test Statistic               26.478
Degrees of freedom                                28
P-value (Chi-square)                           0.547

Model test baseline model:

Minimum Function Test Statistic              760.907
Degrees of freedom                                45
P-value                                        0.000

User model versus baseline model:

Comparative Fit Index (CFI)                    1.000
Tucker-Lewis Index (TLI)                       1.003

Loglikelihood and Information Criteria:

Loglikelihood user model (H0)              -2560.036
Loglikelihood unrestricted model (H1)      -2546.797

Number of free parameters                         27
Akaike (AIC)                                5174.071
Bayesian (BIC)                              5263.126
Sample-size adjusted Bayesian (BIC)         5177.587

Root Mean Square Error of Approximation:

RMSEA                                          0.000
90 Percent Confidence Interval          0.000  0.051
P-value RMSEA |z|)   Std.lv  Std.all
f1 =~
i1                1.000                               1.146    1.000
f2 =~
i2                1.000                               0.875    0.722
i3                0.971    0.100    9.709    0.000    0.850    0.716
i4                0.911    0.103    8.856    0.000    0.798    0.653
f3 =~
i5                1.000                               0.671    0.633
i6                0.736    0.106    6.947    0.000    0.494    0.579
i7                0.911    0.121    7.551    0.000    0.612    0.641
f4 =~
i8         (a)    1.000                               0.619    0.601
i9         (a)    1.000                               0.619    0.646
f5 =~
i10               1.000                               0.910    1.000

Covariances:
Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
f1 ~~
f2                0.755    0.107    7.053    0.000    0.753    0.753
f3                0.595    0.091    6.517    0.000    0.774    0.774
f4                0.565    0.078    7.250    0.000    0.797    0.797
f5                0.394    0.079    4.996    0.000    0.378    0.378
f2 ~~
f3                0.555    0.089    6.256    0.000    0.945    0.945
f4                0.614    0.081    7.541    0.000    1.132    1.132
f5                0.465    0.078    5.976    0.000    0.584    0.584
f3 ~~
f4                0.419    0.065    6.430    0.000    1.008    1.008
f5                0.360    0.065    5.515    0.000    0.589    0.589
f4 ~~
f5                0.356    0.059    6.047    0.000    0.631    0.631

Variances:
Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
.i1                0.000                               0.000    0.000
.i10               0.000                               0.000    0.000
.i2                0.703    0.083    8.422    0.000    0.703    0.478
.i3                0.688    0.081    8.498    0.000    0.688    0.488
.i4                0.854    0.095    9.020    0.000    0.854    0.573
.i5                0.673    0.079    8.547    0.000    0.673    0.599
.i6                0.485    0.054    8.985    0.000    0.485    0.665
.i7                0.536    0.063    8.467    0.000    0.536    0.589
.i8                0.678    0.080    8.527    0.000    0.678    0.639
.i9                0.535    0.068    7.899    0.000    0.535    0.583
f1                1.312    0.131   10.000    0.000    1.000    1.000
f2                0.766    0.137    5.608    0.000    1.000    1.000
f3                0.451    0.099    4.561    0.000    1.000    1.000
f4                0.383    0.075    5.133    0.000    1.000    1.000
f5                0.828    0.083   10.000    0.000    1.000    1.000


## 1 Answer

ANSWERED

If anyone else has the same issue, here are some notes I've learned from the lavaan google group and experimenting.

1. Someone pointed out that the warning went away if I increased the N in my example to 20000. So, the problem is not the syntax.

2. As this post indicates, specifying the variance of the observed single indicator to 0 is not necessary and is now the default behavior of lavaan in the case of single indicator factors.

3. The above issues made me realize that my actual model was likely misspecified. After checking the modification indices, I saw (which I should have known from prior analyses) that two items on separate factors were correlated. After allowing those two items to covary, the model worked fine.

Take away, if you think your syntax is correct, it's probably your model.