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

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...

Factorial Design

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

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...

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

Statistical Methods to Analyze Parametric Data: ANOVA

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...

You might also read

Related Articles

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

Sort by

Same author

Recruiting spatial-numerical representations to increase arithmetic fluency in low-income students.

Developmental psychology·2025

Same author

Power Analysis for Moderator Effects in Longitudinal Cluster Randomized Designs.

Educational and psychological measurement·2023

Same author

Using Quantile Regression to Estimate Intervention Effects Beyond the Mean.

Educational and psychological measurement·2019

Same author

Effects of Interim Assessments Across the Achievement Distribution: Evidence From an Experiment.

Educational and psychological measurement·2018

Same author

Power Analysis for Models of Change in Cluster Randomized Designs.

Educational and psychological measurement·2018

Same author

Separate but correlated: The latent structure of space and mathematics across development.

Journal of experimental psychology. General·2016

Search research articles

Related Experiment Video

Updated: Apr 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Fixed effects and variance components estimation in three-level meta-analysis.

Spyros Konstantopoulos¹

¹Michigan State University, East Lansing, MI, United States. spyros@msu.edu.

Research Synthesis Methods

|June 11, 2015

Summary

This study applies Fisher scoring methods to multilevel meta-analysis, accounting for hierarchical data structures common in education and social sciences. The approach effectively analyzes random variation across multiple levels, enhancing research synthesis.

Keywords:

effect sizes meta-analysis multilevel models variance components

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

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

Related Experiment Videos

Last Updated: Apr 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

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

Area of Science:

Statistics
Education Research
Social Sciences

Background:

Meta-analytic data often exhibit hierarchical structures, with effect sizes nested within studies and studies within higher-level units.
Multilevel models provide a suitable framework for analyzing such complex, nested data in meta-analysis.

Purpose of the Study:

To discuss and demonstrate the application of a Fisher scoring method for two-level and three-level meta-analysis.
To incorporate random variation at both the second and third levels of hierarchical data.

Main Methods:

Application of a Fisher scoring method within a multilevel modeling framework.
Analysis of hierarchically structured meta-analytic data, considering random effects at multiple levels.
Utilized statistical software such as SAS PROC MIXED and HLM for computation.

Main Results:

The Fisher scoring method effectively handles random variation in two-level and three-level meta-analysis.
The model's utility was demonstrated using empirical data on school calendar types.
Accurate estimation of fixed effects and variance components is achievable.

Conclusions:

Multilevel models, particularly with Fisher scoring, are powerful tools for complex meta-analytic data.
This method enhances the accuracy and robustness of meta-analytic findings in hierarchical research designs.
The approach is applicable across various fields including education and social sciences.