Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

Experimental Designs

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Guidance for the Methodological Challenges of Polytobacco Use in Tobacco Regulatory Science.

Public health reports (Washington, D.C. : 1974)·2025

Same author

Apolipoprotein E, Executive Function, and Falls across Cognitive Status: A Cross-Sectional Study.

Dementia and geriatric cognitive disorders·2025

Same author

Qualitative Mediation Analysis: an Important Method for Exploring Mediating Mechanisms in Prevention Science.

Prevention science : the official journal of the Society for Prevention Research·2025

Same author

Three Approaches to Testing for Statistical Suppression.

Multivariate behavioral research·2025

Same author

Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors.

Frontiers in digital health·2025

Same author

Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary.

Multivariate behavioral research·2024

Same journal

Maximum Likelihood and Bayesian Estimation in Cross-Domain Latent Growth Curve Modeling: The Impact of Reliability, Sample Size, and Missing Data.

Structural equation modeling : a multidisciplinary journal·2026

Same journal

Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.

Structural equation modeling : a multidisciplinary journal·2026

Same journal

Accommodating Continuous Time Metrics within the Discrete-time Latent Change Score Model Using Definition Variables.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in MASEM.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations.

Structural equation modeling : a multidisciplinary journal·2025

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS.

Felix Thoemmes¹, David P Mackinnon, Mark R Reiser

¹Department of Educational Psychology, Texas A&M University.

Structural Equation Modeling : a Multidisciplinary Journal

|August 13, 2013

Summary

This summary is machine-generated.

This study provides a general framework for statistical power analyses in complex mediation models. It details methods for calculating power in various mediation scenarios, aiding researchers in study design.

Keywords:

Mediation Monte Carlo Mplus Statistical Power

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Related Experiment Videos

Last Updated: May 8, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Area of Science:

Psychological methods
Statistical modeling
Quantitative psychology

Background:

Mediation effects are frequently incorporated into advanced statistical models like latent variable and growth curve models.
Existing literature lacks guidance on estimating statistical power for mediation in these complex models.

Purpose of the Study:

To introduce a general framework for conducting statistical power analyses for complex mediational models.
To provide practical guidance and examples for researchers applying these methods.

Main Methods:

The study employs a Monte Carlo simulation approach to estimate statistical power.
Power is calculated as the proportion of samples where the mediation estimate is statistically significant.

Main Results:

The framework is demonstrated with power calculations for various mediation models, including single, multiple, and moderated mediation.
Specific applications cover latent variable mediation and longitudinal designs.

Conclusions:

The developed framework offers a comprehensive solution for power analysis in complex mediation.
The study includes sample syntax and tables of required sample sizes to facilitate practical application.