I am testing this model in SPSS AMOS.
The value of .23 above the top right corner of timedrs is the squared multiple correlation for that variable.
I also ran the same analysis as two multi-step regressions. The results came out like this:
The generalized squared multiple correlation is described by Schumacker & Lomax (2004) on p159 as a "traditional non-SEM path model-fit [index]." The relevant text is as follows:
Applying the formula for a generalized squared multiple correlation, I get:
1 – (1 – .119) × ( 1 – .227) = 0.32.
This is higher than the .23 I obtained from the path analysis run in AMOS, and from the equation I can see that it can never be lower than the smallest value of R Square that goes into its calculation. I understand that I should not be surprised that the values are not the same. However, I am unsure about how to interpret the generalized squared multiple correlation, i.e. this $R^2m$ thing. What would a high/low generalized squared multiple correlation mean? Is it a good method of assessing model fit?
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Psychology Press.
Best Answer
Although as far as I can tell the 3rd edition of Schumacker & Lomax doesn't answer my question, the 4th edition (from 2015) does! Quoting p84 of that text (but changing the figure to match my data), the answer to the question is:
"The $R^2m$ for the path model would suggest that [32%] of the variance in [timedrs] is explained by the relations in the path model."
I'd still welcome further explanation of how this should be interpreted, but I'll take this as a good enough answer for now.