Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects

By J. Biesanz, Carl F. Falk, Victoria Savalei. Multivariate Behavioral Research, 2010

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Monte Carlo Confidence Intervals for Complex Functions of Indirect Effects (2016, methodology): “However, to test an indirect effect, the null hypothesis H0 : ab = 0 is the composite of many hypotheses about the combination of parameter values given that either a or b is zero (Biesanz et al., 2010; MacKinnon et al., 2002).”
Integrating Knowledge Activities for Team Innovation: Effects of Transformational Leadership (2018, background): “Such an individual-based view of leadership process, however, departs from the original formulation of transformational leadership theory that centers on change and innovation for a whole group and larger collectives (Bass; Bass & Riggio, 2006).”
Advantages of Monte Carlo Confidence Intervals for Indirect Effects (2012, methodology): “Biesanz, Falk, and Savalei (2010) found that BCa intervals did not perform as well as PC intervals.”
Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, methodology): “Biesanz, Falk, and Savalei (2010) examined the robustness of several methods, but under an observed-variable-only model, only using VM to generate nonnormal data, and did not include LCI or other emerging methods such as the Monte Carlo method (MC; e.g., MacKinnon, Lockwood, & Williams, 2004;…”