Hi,
I am running nonrecursive models using the sem command. I have encountered a problem with the results, and I am hoping somebody can help me with it:
The observed variables in the bidirectional relationship in my sem model are:
V1: % of population that are obese; and
V2: % of population who took walking trips.
Both V1 and V2 equations also include various other explanatory variables.
This is what confuses me:
The model gives a negative sign for the coefficient estimate of obesity in the walking model (meaning that higher obesity percentages are correlated with lower percentages of walking trips (which I kind of expected because declined health due to obesity may prevent people from making walking trips). However, the model returns a positive sign for the coefficient estimate of walking trips in the obesity model meaning that higher percentages of walking trips are correlated with higher percentages of obesity . This is not logical to me ; I expected to see that more walking trips are correlated with lower obesity rates (and not higher as the model results suggest).
My question is:
Is it possible to get opposite signs on the coefficient estimates of the observed variables in the bidirectional relationship in nonrecursive sem models? How could in a nonrecursive model, variable V1 affect V2 in a negative way, while V2 affects V1 in a positive way? If not, what could be wrong in my model? Every example I have seen (including Example 7 - Page 187 of the Stata SEM Reference Manual Release 13 (https://www.stata.com/manuals13/sem.pdf)) has results with similar signs for the coefficient estimates of the observed variables in the bidirectional relationship.
Any help is appreciated!
I am running nonrecursive models using the sem command. I have encountered a problem with the results, and I am hoping somebody can help me with it:
The observed variables in the bidirectional relationship in my sem model are:
V1: % of population that are obese; and
V2: % of population who took walking trips.
Both V1 and V2 equations also include various other explanatory variables.
This is what confuses me:
The model gives a negative sign for the coefficient estimate of obesity in the walking model (meaning that higher obesity percentages are correlated with lower percentages of walking trips (which I kind of expected because declined health due to obesity may prevent people from making walking trips). However, the model returns a positive sign for the coefficient estimate of walking trips in the obesity model meaning that higher percentages of walking trips are correlated with higher percentages of obesity . This is not logical to me ; I expected to see that more walking trips are correlated with lower obesity rates (and not higher as the model results suggest).
My question is:
Is it possible to get opposite signs on the coefficient estimates of the observed variables in the bidirectional relationship in nonrecursive sem models? How could in a nonrecursive model, variable V1 affect V2 in a negative way, while V2 affects V1 in a positive way? If not, what could be wrong in my model? Every example I have seen (including Example 7 - Page 187 of the Stata SEM Reference Manual Release 13 (https://www.stata.com/manuals13/sem.pdf)) has results with similar signs for the coefficient estimates of the observed variables in the bidirectional relationship.
Any help is appreciated!
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