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Notes
1. Although the partitioning of a total effect into direct and indirect components in the manner described here does not require the assumption that the errors in estimation are uncorrelated, such intercorrelation can bias parameter estimates and standard errors. Correlated errors can result from, among other things, the exclusion of variables from the model that are correlated with two or more included variables.
2. Otherwise, I agree wholeheartedly with the position Holbert and Stephenson (2003) take in their statement that communication researchers should place much more emphasis on the estimation and testing of indirect effects than they have in the past.
3. Bootstrapping requires the raw data rather than just a covariance matrix. Readers interested in the raw data used in this example can contact me at [email protected] and I will gladly send it.
4. Contrary to conventional wisdom, it is possible (although rare in practice) for a standardized coefficient to be larger than 1 in absolute value (Deegan, Citation1978). This means that even the standardized indirect effect has no real upper or lower bound.