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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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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...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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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...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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:
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Sample Size Calculation01:19

Sample Size Calculation

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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...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Power and Sample Size Determination for Multilevel Mediation in Three-Level Cluster-Randomized Trials.

Ben Kelcey1, Yanli Xie1, Jessaca Spybrook2

  • 1College of Education, Criminal Justice, Human Services and Information Technology, University of Cincinnati.

Multivariate Behavioral Research
|April 16, 2020
PubMed
Summary

This study introduces methods for calculating statistical power in three-level cluster-randomized mediation studies. These methods help researchers plan studies examining individual, intermediate, or cluster-level mediators.

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Mediationexperimental designindirect effectsmultilevel modelspowersample size determination

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Psychometrics

Background:

  • Mediation analysis is crucial for understanding treatment pathways and mechanisms of action.
  • Existing guidance for planning mediation studies with complex hierarchical or clustered structures is limited.
  • Evaluating mediation effects alongside total effects in experimental designs is increasingly common.

Purpose of the Study:

  • To provide methods for computing statistical power to detect mediation effects in three-level cluster-randomized designs.
  • To address the need for guidance in planning mediation studies with multi-tiered hierarchical structures.

Main Methods:

  • Development of power computation methods for three-level cluster-randomized designs.
  • Assessment of the proposed methods through simulation studies.
  • Application of methods using the R package PowerUpR and its Shiny application for clinic-randomized studies.

Main Results:

  • The study provides practical tools for power calculations in complex mediation designs.
  • Demonstration of application in a three-level clinic-randomized study context.
  • The R package PowerUpR facilitates the implementation of these power computation methods.

Conclusions:

  • The developed methods and tools support researchers in planning robust mediation studies with clustered data.
  • This work addresses a critical gap in the methodology for evaluating mediation in complex hierarchical designs.
  • Facilitates more accurate and efficient study design for mediation research in nested data structures.