The Importance Of Mediating Effect On A Second Variable Intervenes

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Mediating effect refers when a third variable intervenes between the related constructs and explains why a relationship between them exists (Hair et al., 2009). There are several types of tests that can test the mediating effects. McKinnon, Lockwood, Hoffman, West, & Sheets’ (2002) summary of the tests of significance of intervening variable effects is shown in Table 1.
In addition to the methods displayed in the summary table, recently other methods such as Bootstrapping (e.g., Bollen & Stine, 1990) and the Monte Carlo Method (e.g., MacKinnon, Lockwood, & Williams, 2004) have become popular. Bootstrapping is a non-parametric method based on resampling with replacement that is performed many times (e.g., 1000 times). From each sample, the indirect effects are computed and their distribution is generated. The Monte Carlo Method is a computer simulation test of the indirect effect that is proposed by MacKinnon et al. (2004).
Baron and Kenny (1986) provided a seminal work in their assertion of specific steps for testing cross-section mediation models. They provided four conditions or steps for testing in their causal steps approach, which linearly lays out specific relationships amongst the independent, dependent, and mediator variable in four separate regression models. These four steps are noted below:
1) Demonstrate that the independent variable (X) is significantly related to the dependent variable (Y). This is denoted as the c path.
2) Demonstrate that the independent
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