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

86
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...
86
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Group Design02:01

Group Design

9.6K
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...
9.6K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

287
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...
287
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
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...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Evaluating differences in latent means across studies: Extending meta-analytic confirmatory factor analysis with the analysis of means.

Research synthesis methods·2026
Same author

Six ways to handle dependent effect sizes in meta-analytic structural equation modeling: Is there a gold standard?

Research synthesis methods·2026
Same author

Evaluating the ICD-11 PTSD and Complex PTSD Constructs: A Meta-Analytic Confirmatory Factor Analysis of the International Trauma Questionnaire.

Assessment·2025
Same author

Short- and Long-Term Effectiveness of Brief Intensive Trauma Treatment for Adolescents With Posttraumatic Stress Disorder and Their Caregivers: Protocol for a Multicenter Randomized Controlled Trial.

JMIR research protocols·2025
Same author

Is a more selective exit exam related to shadow education use? An analysis of two cohorts of final-year secondary school students in the Netherlands.

Oxford review of education·2025
Same author

Author Correction: Analytical power calculations for structural equation modeling: A tutorial and Shiny app.

Behavior research methods·2025
Same journal

Maximum Likelihood and Bayesian Estimation in Cross-Domain Latent Growth Curve Modeling: The Impact of Reliability, Sample Size, and Missing Data.

Structural equation modeling : a multidisciplinary journal·2026
Same journal

Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.

Structural equation modeling : a multidisciplinary journal·2026
Same journal

Accommodating Continuous Time Metrics within the Discrete-time Latent Change Score Model Using Definition Variables.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations.

Structural equation modeling : a multidisciplinary journal·2025
Same journal

Unsupervised Model Construction in Continuous-Time.

Structural equation modeling : a multidisciplinary journal·2025
See all related articles

Related Experiment Video

Updated: Sep 13, 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

7.0K

Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in

Lennert J Groot1, Kees Jan Kan1, Suzanne Jak1

  • 1University of Amsterdam.

Structural Equation Modeling : a Multidisciplinary Journal
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

Cluster-robust estimation in individual participant data meta-analysis (IPD MASEM) can distort findings by misrepresenting within-study effects and standard errors. Careful selection of IPD MASEM methods is crucial for accurate results.

Keywords:
IPDmeta-analysisraw data synthesissimulationstructural equation modeling

More Related Videos

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

3.4K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Related Experiment Videos

Last Updated: Sep 13, 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

7.0K
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

3.4K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Meta-Analysis

Background:

  • Individual participant data meta-analysis (IPD MASEM) offers advanced modeling capabilities.
  • Several methods exist for IPD MASEM, including cluster-robust estimation, two-level SEM, and One-Stage MASEM (OSMASEM).
  • Cluster-robust estimation is popular but may produce divergent results compared to other techniques.

Purpose of the Study:

  • To compare the performance of different IPD MASEM methods.
  • To evaluate the accuracy and biases associated with cluster-robust estimation versus other approaches.
  • To provide guidance on selecting appropriate IPD MASEM methods.

Main Methods:

  • The study employed simulated data for meta-analytical structural equation modeling (MASEM).
  • Simulations varied key factors: intraclass correlations, parameter equality, number of studies, and missing data.
  • Performance was assessed by comparing within-study estimates, standard errors, and model fit across methods.

Main Results:

  • Cluster-robust estimation frequently misrepresented within-study estimates.
  • Biased standard errors were commonly observed with cluster-robust estimation.
  • Cluster-robust estimation tended to incorrectly reject model fit more often than other methods.

Conclusions:

  • Cluster-robust estimation may not be suitable for all IPD MASEM applications due to potential biases.
  • The findings underscore the importance of method selection in IPD MASEM.
  • Researchers should carefully consider alternative methods to ensure accurate meta-analytical structural equation modeling.