Monte Carlo Confidence Intervals for Complex Functions of Indirect Effects

By Davood Tofighi, David P. MacKinnon. 2016

134 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6a1cde242ab9501505117084553824be42f5659d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. Carl F. Falk
2. A. Schoemann

In Management Science and Operations Research

1. Carl F. Falk
2. A. Schoemann
3. Aaron J. Boulton

In Statistics and Probability

1. Carl F. Falk
2. A. Schoemann
3. Aaron J. Boulton
4. Stephen D. Short

Citations in the corpus

• Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, methodology): “In addition, although MC is an increasingly popular method (Falk & Biesanz, 2016; Selig & Preacher, 2008; Tofighi & MacKinnon, 2011, 2016), it is unknown how it performs versus LCI or R-LCI and how it performs if robust corrections are used in its computations.”
• Transformational leadership and employee voice behavior : a Pygmalion mechanism. (2017, unknown): “We used the R Mediation program to calculate Monte Carlo and asymptotic normal theory confidence intervals (Tofighi & MacKinnon, 2015) to estimate the conditional indirect effects.”
• Determining Power and Sample Size for Simple and Complex Mediation Models (2017, background): “Tofighi and MacKinnon (2016) examined power and Type I error in a more complex mediation model (i.e., X!”