low path coefficients with acceptable R²?

[MarketingStudent]

Hi everybody,

I am at the end of my analysis, but stucked with the interpretation.

Could you give me a hint on how to interpret very low, but significant path coefficients (0.11) in relation to dependent variables with R squares that still reach the minimum level of 0.19?
--> Why are the path coefficients so low? Shouldn't a 0.19 R² reflect that the variance in that construct is explained at least to a little extent by the relation between the independent and dependent variable?

(I included two independet variables as control variables for the dependent variables, since the R squares were actually first below 0.19. This caused the R squares to increase and to reach the 0.19 level, however the path coefficients dropped to below 0.2 as mentioned above (before they were 0.21-0.22). Should I still leave the control variables in the model?)

Thank you very mich in advance for any help.

Best regards
Michelle

[Diogenes]

Hi Michelle,

Remembering that:

R2 = b1*r1y + b2 * r2y + .... + bn*rny

Where:
b1 = path coefficient from x1 to y
r1y = correlations between x1 and y

The path coefficient goes down when we have multicollinearity (in your model, correlation between independent variable and the control variables)


Best regards,

Bido

[MarketingStudent]

Thank you very much Professor Bido!