Advantages of Monte Carlo Confidence Intervals for Indirect Effects
By Kristopher J Preacher, J. Selig. 2012
1,773 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/91685f43782980d31b022b148d26cab677e4861d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Andrew F. Hayes
- Nicholas J. Rockwood
- Daniel M. McNeish
- Kristopher J Preacher
- A. Montoya
- Niels Van Quaquebeke
- Lei Vincent Huang
- Carl F. Falk
Citations in the corpus
- Developing and Testing a Dynamic Model of Workplace Incivility Change (2017, methodology): “Therefore, to compute the confidence intervals associated with the conditional indirect effect estimate, we used Monte Carlo bootstrapping as recommend by Preacher and Selig (2012).”
- Helicopter Parenting and Emerging Adult Self-Efficacy: Implications for Mental and Physical Health (2016, methodology): “However, MCMAM has been shown to be a viable and comparable alternative method (Preacher and Selig 2012).”
- The Relative Trustworthiness of Inferential Tests of the Indirect Effect in Statistical Mediation Analysis (2013, methodology): “Using this same distribution, a biascorrected CI for ab was calculated using the bias adjustment described by Hayes (2013), MacKinnon (2008), and Preacher and Selig (2012).”
- Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. (2017, methodology): “These include the bootstrap confidence interval (Shrout & Bolger, 2002; Preacher & Hayes, 2004, 2008), the Monte Carlo confidence interval (Preacher & Selig, 2012), and the Bayesian credible interval (Yuan & MacKinnon, 2009).4 Research shows these all perform well while also outperforming the Sobel…”
- Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, methodology): “More formally, normal theory SEs are obtained from the asymptotic covariance matrix of model parameters: 1We assume that S has a Wishart distribution (Browne & Arminger, 1995), as we used the R (R Core Team, 2015) package lavaan (Rosseel, 2012) with the option to mimic EQS (Bentler, 2006).”