Key considerations are randomizing X i. Clustered Data. Preacher and Geoffrey J.
Do they sum to zero? Thus, one computes the power of test of paths a and b and then multiply their power to obtain the power of the test of the indirect effect. Complete mediation is the case in which variable X no longer affects Y after M has been controlled, making path c' zero. Shrout, P.
Quantitative strategies for communicating indirect effects. New recommendations for testing indirect effects in mediational models: Methods for mediation and interaction. A latent variable analysis might be used to remove the effects of correlated measurement error.
Sensitivity Analyses. Example A mail server accepts mail from the Internet and copies the messages into a spool directory; a local server will complete delivery.
Judd, C. Although this mechanism violates the principle as stated, it is considered sufficiently minimal to be acceptable. To remove the biasing effect of measurement error, multiple indicators of the variable can be used to tap a latent variable. Questions and tips in the use of structural equation modeling. Baron, R.
The moderator-mediator variable distinction in social psychological research: Interfaces to other modules are particularly suspect, because modules often make implicit assumptions about input or output parameters or the current system state; should any of these assumptions be wrong, the module's actions may produce unexpected, and erroneous, results.
If a covariates interacts with X or M, it would be called a moderator variable. Also Preacher, Rucker, and Hayes have developed a macro for estimating moderated mediation click here.