• Corpus ID: 235352600

Power of Mediation Effects Using Bootstrap Resampling

@inproceedings{Steffener2021PowerOM,
  title={Power of Mediation Effects Using Bootstrap Resampling},
  author={Jason Steffener},
  year={2021}
}
Mediation analyses are a statistical tool for testing the hypothesis about how the relationship between two variables may be direct or indirect via a third variable. Assessing statistical significance has been an area of active research; however, assessment of statistical power has been hampered by the lack of closed form calculations and the need for substantial amounts of computational simulations. The current work provides a detailed explanation of implementing large scale simulation… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 20 REFERENCES

Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

A general framework for power analyses for complex mediational models is described, based on the well-known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero.

A comparison of methods to test mediation and other intervening variable effects.

A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect and found two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power.

Determining Power and Sample Size for Simple and Complex Mediation Models

A new method and convenient tools for determining sample size and power in mediation models are proposed and demonstrated and will allow researchers to quickly and easily determine power and sample size for simple and complex mediation models.

Monte Carlo based statistical power analysis for mediation models: methods and software

  • Z. Zhang
  • Business
    Behavior research methods
  • 2014
This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation and develops a free R package to conduct the power analysis discussed in this study.

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

  • T. Beasley
  • Mathematics
    Journal of experimental education
  • 2014
Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a, and implications for statistical analysis and applied researchers are discussed.

Equivalence of the Mediation, Confounding and Suppression Effect

The statistical similarities among mediation, confounding, and suppression are described and methods to determine the confidence intervals for confounding and suppression effects are proposed based on methods developed for mediated effects.

A General Model for Testing Mediation and Moderation Effects

Methods for testing mediation and moderation effects in a dataset, both together and separately are described, and the utility of combining the effects into a single model is described.

Required Sample Size to Detect the Mediated Effect

The necessary sample sizes for six of the most common and the most recommended tests of mediation for various combinations of parameters are presented to provide a guide for researchers when designing studies or applying for grants.

DIRECT AND INDIRECT EFFECTS: CLASSICAL AND BOOTSTRAP ESTIMATES OF VARIABILITY

The decomposition of effects in structural equation models has been of considerable interest to social scientists. Finite-sample or asymptotic results for the sampling distribution of estimators of