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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

555
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...
555
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

697
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...
697
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

57
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
57
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

90
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
90
Methods of Medium Optimization01:28

Methods of Medium Optimization

15
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...
15

You might also read

Related Articles

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

Sort by
Same author

Lavandin cell extract enriched in rosmarinic acid attenuates inflammation via AMPK/Nrf2/HO-1 activation and IKK/IκB/NF-κB inhibition.

BMC plant biology·2026
Same author

Avoid All the Competitive Ones: Dynamics of Altruistic Behavior, Mediators, and Moderators in an Evacuation Drill.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Comparison of Fludarabine Versus Bendamustine as a Lymphodepleting Chemotherapy Prior to CAR-T for Large Cell Lymphoma.

Transplantation and cellular therapy·2026
Same author

A ratiometric fluorescent reporter of mitochondrial sodium.

Nature chemical biology·2026
Same author

STAT6 inhibition of M2 macrophages suppresses tumor growth by modulating the tumor microenvironment in colon cancer model.

Frontiers in immunology·2026
Same author

Antioxidant Supplementation with Caffeine During Rescue In Vitro Maturation Improves Fertilization and Embryo Development in Women of Advanced Maternal Age.

Antioxidants (Basel, Switzerland)·2026
Same journal

Adverse and positive childhood experiences in relation to adolescent mental health: sequential indirect associations.

Frontiers in psychology·2026
Same journal

Personality profiles and usage experience are associated with trust and dependence on generative AI: a latent profile analysis.

Frontiers in psychology·2026
Same journal

Editorial: Promoting replicability: empowering method and applied researchers in driving reliable results.

Frontiers in psychology·2026
Same journal

The mediating roles of the challenge appraisal in the relationship between the coach-athlete relationship and adolescent athletes' burnout.

Frontiers in psychology·2026
Same journal

Unpacking GenAI-enabled deep learning engagement: role perceptions, human-GenAI synergy strategies, and underlying mechanisms.

Frontiers in psychology·2026
Same journal

Violence exposure and cyberbullying among Chinese adolescents: the mediating role of moral disengagement.

Frontiers in psychology·2026
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

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

3.8K

Comparing Bayesian random coefficient prediction and latent interaction models for multilevel moderated mediation.

Sooyong Lee1, Soyoung Kim2

  • 1Department of Education, University of Wisconsin-Madison, Madison, WI, United States.

Frontiers in Psychology
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

Bayesian random coefficient prediction (BRCP) and Bayesian latent interaction (BINT) models effectively detect moderated mediation in multilevel data. Both models show robustness, with BRCP being slightly preferable for smaller sample sizes in complex hierarchical analyses.

Keywords:
Bayesian estimationcross-level moderationmediated moderationmediationmultilevel structural equation modeling

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

6.4K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

4.5K

Related Experiment Videos

Last Updated: Mar 24, 2026

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

3.8K
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

6.4K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

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

Published on: September 11, 2021

4.5K

Area of Science:

  • Multilevel modeling
  • Statistical analysis
  • Psychometrics

Background:

  • Moderated mediation analysis is crucial for understanding complex relationships in hierarchical data.
  • Bayesian approaches offer robust methods for analyzing multilevel structures.
  • Comparing different Bayesian models is essential for identifying optimal analytical strategies.

Purpose of the Study:

  • To compare the performance of Bayesian random coefficient prediction (BRCP) and Bayesian latent interaction (BINT) models.
  • To evaluate the effectiveness of these models in detecting moderated mediation effects within multilevel contexts.
  • To assess model performance using both empirical and simulated data.

Main Methods:

  • Utilized the Trends in International Mathematics and Science Study (TIMSS2019) dataset for empirical analysis.
  • Employed simulated data to systematically evaluate model performance under various conditions.
  • Compared parameter estimates, bias, Type I error rates, and statistical power between BRCP and BINT models.

Main Results:

  • BRCP and BINT models yielded highly similar parameter estimates with minimal discrepancies.
  • Both models demonstrated consistent within- and between-level relationship findings in empirical data.
  • Simulation results showed acceptable bias and controlled Type I error rates, with sufficient power across most conditions, though smaller cluster sizes impacted performance.

Conclusions:

  • Bayesian multilevel modeling approaches, including BRCP and BINT, are robust for complex hierarchical data analysis.
  • BRCP may be slightly more suitable than BINT for analyses involving smaller sample sizes.
  • Further research into measurement error and complex moderator structures can enhance Bayesian multilevel modeling techniques.