R-Square
- R Square statistics explains the variance in the endogenous variable explained by the exogenous variable(s).
- For example, a variable Y influenced by X1, X2, and X3 has a R-Square value of 0.623. This would mean that 62.3% change in Y can be explained by X1, X2, X3.
- In order to make it easier to interpret, look for the arrows that are pointing towards the dependent (endogenous) variable.
- Falk and Miller (1992) recommended that R2 values should be equal to or greater than 0.10 in order for the variance explained of a particular endogenous construct to be deemed adequate.
- Cohen (1988) suggested R2 values for endogenous latent variables are assessed as follows: 0.26 (substantial), 0.13 (moderate), 0.02 (weak).
- Chin (1998) recommended R2 values for endogenous latent variables based on: 0.67 (substantial), 0.33 (moderate), 0.19 (weak).
- Hair et al. (2011) & Hair et al. (2013) suggested in scholarly research that focuses on marketing issues, R2 values of 0.75, 0.50, or 0.25 for endogenous latent variables can, as a rough rule of thumb, be respectively described as substantial, moderate or weak.