Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Plans01:23

Sampling Plans

994
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
994
Cluster Sampling Method01:20

Cluster Sampling Method

14.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.8K
Random Sampling Method01:09

Random Sampling Method

14.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
14.8K
Sample Size Calculation01:19

Sample Size Calculation

6.7K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.7K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.1K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Interpersonal Communication and Maternal Behavioral Practice: A Case Study of Explanatory Causal Machine Learning in Nepal.

The Journal of nutrition·2026
Same author

A practical guide to causal moderation analysis for investigating the role of context, identity, and culture in intervention research.

Journal of school psychology·2025
Same author

Expanding Education Researchers' Access to Classroom Observation Data With a Remote and Cost-Effective Video Data Collection Protocol.

Prevention science : the official journal of the Society for Prevention Research·2024
Same author

Gender, Racial, and Socioeconomic Disparities on Social and Behavioral Skills for K-8 Students With and Without Interventions: An Integrative Data Analysis of Eight Cluster Randomized Trials.

Prevention science : the official journal of the Society for Prevention Research·2022
Same author

Identifying and Estimating Causal Moderation for Treated and Targeted Subgroups.

Multivariate behavioral research·2022
Same author

Study Registration for the Field of Prevention Science: Considering Options and Paths Forward.

Prevention science : the official journal of the Society for Prevention Research·2021

Related Experiment Video

Updated: Feb 7, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

4.6K

Sample Size Planning for Cluster-Randomized Interventions Probing Multilevel Mediation.

Ben Kelcey1, Jessaca Spybrook2, Nianbo Dong3

  • 1University of Cincinnati, Cincinnati, OH, 45221, USA. ben.kelcey@gmail.com.

Prevention Science : the Official Journal of the Society for Prevention Research
|July 23, 2018
PubMed
Summary

This study introduces a method for estimating statistical power and sample sizes needed for multilevel mediation analyses. This helps researchers design studies to effectively detect intervention effects on outcomes.

Keywords:
Indirect effectsMediationMultilevel modelsPowerSample size determination

More Related Videos

A Simple Method for the Size Controlled Synthesis of Stable Oligomeric Clusters of Gold Nanoparticles under Ambient Conditions
08:21

A Simple Method for the Size Controlled Synthesis of Stable Oligomeric Clusters of Gold Nanoparticles under Ambient Conditions

Published on: February 5, 2016

22.6K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K

Related Experiment Videos

Last Updated: Feb 7, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

4.6K
A Simple Method for the Size Controlled Synthesis of Stable Oligomeric Clusters of Gold Nanoparticles under Ambient Conditions
08:21

A Simple Method for the Size Controlled Synthesis of Stable Oligomeric Clusters of Gold Nanoparticles under Ambient Conditions

Published on: February 5, 2016

22.6K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.8K

Area of Science:

  • Social Sciences
  • Psychology
  • Intervention Research

Background:

  • Multilevel mediation analyses are crucial for understanding intervention mechanisms and impact.
  • Current knowledge is limited regarding study design for adequate power in multilevel mediation.

Purpose of the Study:

  • To describe methods for prospectively estimating statistical power and sample sizes for multilevel mediation.
  • To provide researchers with tools to design studies capable of detecting multilevel mediation effects.

Main Methods:

  • Developed a simple approach using summary statistics from existing literature and expected effect sizes.
  • Illustrated methods with a running example covering various mediation types.
  • Implemented power formulas in the R package PowerUpR and PowerUp software.

Main Results:

  • Provided a practical framework for power estimation in multilevel mediation studies.
  • Demonstrated how to identify sufficient sample sizes for detecting mediation effects.

Conclusions:

  • Researchers can now better plan studies to ensure sufficient power for multilevel mediation analyses.
  • The developed methods and software facilitate the design of more robust intervention studies.