[R] interpreting significance of path coefficients from sem() output

[jebyrnes]

Michael,

As a general rule of thumb (I believe this is in Jim Grace's book, if not
others) one should use 10-20 observations per variable.  If you have 5
variables, and 18 observations, you should probably be a bit suspect of your
results.  That said, if some of your paths are indeed non-significant, well,
they might be!  Have you tried an alternate model with those paths set to 0? 
You can them compare the two models in a variety of ways (LR tests, compare
BIC values, etc).

-Jarrett


Michael Rennie-2 wrote:
> 
> 
> Hi there,
> 
> Quick question about the output from the sem() function in the library 
> of the same name.
> 
> If I am getting probabilities >0.05 for some of my estimates of path 
> coefficients, I'm assuming the interpretation here is that the 
> coefficient is not significantly different from zero, correct? In that 
> case, might it make sense that I should disregard path coefficients 
> between variables where the probability is greater than 0.05? In which 
> case, would it make further sense to remove those particular links from 
> the specify.model() command and re-run the analysis, excluding those 
> rows which lacked significance in the previous attempt?
> 
> Given that in just about every other example I've been able to dig up 
> where this method is employed (including other datasets I am working 
> with), the probabilities of the path coefficients are almost always well 
> below 0.05, it makes me suspect that what I am observing may simply 
> result from the fact that I'm trying to fit a path analysis among 5 
> variables (4 predictors, 1 criterion) based on only 18 observations, 
> admittedly a small sample size and perhaps an overly ambitious approach 
> to analyzing so few data.
> 
> Last, I'm convinced that I'm using the code correctly as I was able to 
> successfully reproduce an example in Quinn and Keough (2002) before I 
> turned the code onto my own data.
> 
> 

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