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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

408
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...
408
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K
Quadratic Models01:23

Quadratic Models

333
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
333
Bonferroni Test01:10

Bonferroni Test

3.6K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.6K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.6K
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...
4.6K
Block Diagram Reduction01:22

Block Diagram Reduction

671
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
671

You might also read

Related Articles

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

Sort by
Same author

Why social media research has failed policy-makers.

Nature human behaviour·2026
Same author

Inference for Disattenuated Correlations.

Applied psychological measurement·2026
Same author

Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models.

Psychometrika·2026
Same author

Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models.

Psychometrika·2024
Same author

Measures of Agreement with Multiple Raters: Fréchet Variances and Inference.

Psychometrika·2024
Same author

Measuring Agreement Using Guessing Models and Knowledge Coefficients.

Psychometrika·2023
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Apr 10, 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

Penalized eigenvalue block averaging: Extension to nested model comparison and Monte Carlo evaluations.

Njål Foldnes1, Steffen Grønneberg2, Jonas Moss3

  • 1Norwegian Centre for Reading Education and Research, University of Stavanger, Stavanger, Norway. njal.foldnes@gmail.com.

Behavior Research Methods
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

Penalized eigenvalue block averaging (pEBA) enhances confirmatory factor analysis model testing under non-normality. New pEBA methods improve goodness-of-fit and invariance testing, offering robust psychometric analysis tools.

Keywords:
Factor modelGoodness-of-fit testMeasurement invarianceNested model comparisonNon-normalitypEBA

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Related Experiment Videos

Last Updated: Apr 10, 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
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Area of Science:

  • Psychometrics
  • Statistical modeling
  • Structural equation modeling

Background:

  • Confirmatory factor analysis (CFA) is crucial for psychometric research, but testing models under non-normality presents challenges.
  • Traditional methods struggle with type I error control and power in complex CFA models.
  • A penalized eigenvalue block averaging (pEBA) procedure showed promise for goodness-of-fit testing in prior research.

Purpose of the Study:

  • To extend and evaluate penalized eigenvalue block averaging (pEBA) methods for goodness-of-fit and nested model comparison in confirmatory factor analysis (CFA).
  • To assess type I error control and statistical power of pEBA variants and traditional statistics under higher-dimensional non-normal conditions.
  • To develop and validate pEBA procedures for weak invariance testing within a multi-group CFA framework.

Main Methods:

  • Extensive Monte Carlo simulations were conducted to evaluate numerous pEBA variants and established statistics.
  • Simulations varied dimensions of latent and observed vectors, base statistics (ML, Browne's RLS), and bias correction of covariance matrix estimators.
  • pEBA methods were specifically developed and tested for nested model comparison and weak invariance testing.

Main Results:

  • For goodness-of-fit testing, pEBA with four blocks, using Browne's RLS statistic without bias correction, demonstrated superior performance.
  • For weak invariance testing, pEBA with singleton blocks, employing the ML statistic with an unbiased covariance matrix estimator, yielded the best results.
  • Performance was evaluated across various type I error rates and statistical power levels.

Conclusions:

  • Penalized eigenvalue block averaging (pEBA) offers a flexible and effective approach for confirmatory factor analysis model testing, particularly under non-normality.
  • Specific pEBA configurations are recommended for optimizing goodness-of-fit and measurement invariance testing.
  • The developed pEBA procedures are accessible through the new R package, semTests, facilitating their application in psychometric research.