Martin,

hi,

I have following data and code;

cov <- c
(1.670028
,-1.197685
,-2.931445,-1.197685,1.765646,3.883839,-2.931445,3.883839,12.050816)

cov.matrix <- matrix(cov, 3, 3, dimnames=list(c("y1","x1","x2"),
c("y1","x1","x2")))

path.model <- specify.model()
   x1 -> y1, x1-y1
   x2 <-> x1,     x2-x1
   x2 <-> x2,     x2-x2
   x1 <-> x1,     x1-x1
   y1 <-> y1,     y1-y1
  x2 -> y1,  x2-y1

  summary(sem(path.model, cov.matrix, N = 422))


and I get following results;



  Model Chisquare =  12.524   Df =  1 Pr(>Chisq) = 0.00040179
  Chisquare (null model) =  812.69   Df =  3
  Goodness-of-fit index =  0.98083
  Adjusted goodness-of-fit index =  0.885
  RMSEA index =  0.16545   90% CI: (0.09231, 0.25264)
  Bentler-Bonnett NFI =  0.98459
  Tucker-Lewis NNFI =  0.9573
  Bentler CFI =  0.98577
  SRMR =  0.027022
  BIC =  6.4789

  Parameter Estimates
       Estimate Std Error z value Pr(>|z|)
x1-y1 -0.67833 0.033967  -19.970 0        y1 <--- x1
x2-x1  3.88384 0.293743   13.222 0        x1 <--> x2
x2-x2 12.05082 0.831569   14.492 0        x2 <--> x2
x1-x1  1.76565 0.121839   14.492 0        x1 <--> x1
y1-y1  0.85761 0.059124   14.505 0        y1 <--> y1

  Iterations =  0


Now I wonder why the chi-square value is so bad and what Pr(>Chisq) tells me.

Can anyone help me on this?


When I allow the path x2 -> y1 I get of course a good fit, but the path coefficient of x2 -> y1 is pretty low (-0.084653), so I thought I
can restrict that one to zero.



If you examine the residuals
 mod1 <- sem(p.model,cov.matrix,N=422)
residuals(mod1)

You will see that you are completing ignoring the y1-x2 covariance.

When you examine your covariance matrix as a correlation matrix,
r.mat <- cov2cor(cov.matrix)
you will note that the x2-y1 relationship is very large (the correlation is -.65)

Your original model was fully saturated and what you are reporting is actually what I label as p.model which is your full model without the last row.

If you compare the fully saturated model with your mod1, you will find that the reason for the large chi square is due to not specifying the x2-y1 path.

You might want to read some more on sem techniques. A good introduction is a text by John Loehlin.

Bill

--
William Revelle         http://personality-project.org/revelle.html
Professor                       http://personality-project.org/personality.html
Department of Psychology             http://www.wcas.northwestern.edu/psych/
Northwestern University http://www.northwestern.edu/
Attend  ISSID/ARP:2009               http://issid.org/issid.2009/

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