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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

294
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
294
Group Design02:01

Group Design

10.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

485
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
485
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.9K
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...
3.9K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.5K
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:
6.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

428
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...
428

You might also read

Related Articles

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

Sort by
Same author

A tutorial for software options to aid in assessing functional relations in single-case experimental designs.

Behavior research methods·2026
Same author

Comparing masked and traditional visual analysis of multiple-baseline designs.

Journal of applied behavior analysis·2025
Same author

Fine-grained effect sizes.

School psychology (Washington, D.C.)·2024
Same author

Type I error rates and power of two randomization test procedures for the changing criterion design.

Behavior research methods·2023
Same author

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework.

Behavior research methods·2023
Same author

Systematic Review of Descriptions and Justifications Provided for Single-Case Quantification Techniques.

Behavior modification·2023

Related Experiment Video

Updated: Dec 20, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.2K

Assessing consistency of effects when applying multilevel models to single-case data.

Rumen Manolov1, John M Ferron2

  • 1Department of Social Psychology and Quantitative Psychology, University of Barcelona, Barcelona, Spain. rrumenov13@ub.edu.

Behavior Research Methods
|May 23, 2020
PubMed
Summary

Replication is key in single-case experimental designs. This study proposes using multilevel models to assess effect consistency within studies, ensuring reliable intervention outcomes.

Keywords:
ConsistencyMultilevel modelsRandom effectsReplicationSingle-case design

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Dec 20, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.2K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.6K

Area of Science:

  • Psychology
  • Behavioral Science
  • Research Methodology

Background:

  • Replication is vital in single-case experimental designs for demonstrating experimental control and intervention generality.
  • The current "replicability crisis" underscores the importance of assessing consistency in research findings.
  • Focusing on within-study replication, this work addresses the consistency of intervention effects.

Purpose of the Study:

  • To propose a novel method for assessing the consistency of intervention effects within single-case experimental designs.
  • To leverage multilevel models (hierarchical linear models/mixed-effects models) for evaluating effect consistency.
  • To provide a more informative approach to replication assessment than merely checking for non-null individual effects.

Main Methods:

  • Utilizing multilevel models to analyze data from single-case experimental designs.
  • Proposing the assessment of confidence intervals for random effects for each case.
  • Contrasting this approach with checking confidence intervals for individual treatment effects.

Main Results:

  • Assessing whether the confidence interval for random effects includes zero offers a more informative measure of effect consistency.
  • This method ensures that the fixed effect is non-zero and that individual effects are consistent in size.
  • The proposed approach was illustrated with real data.

Conclusions:

  • Multilevel models provide a robust framework for assessing the consistency of intervention effects in single-case experimental designs.
  • Evaluating random effects confidence intervals enhances the assessment of within-study replication and intervention reliability.
  • The methodology is implemented in accessible, free software for practical application.