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

Variance01:15

Variance

12.0K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
12.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

485
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...
485
Linear Circuits01:17

Linear Circuits

837
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
837
Linear Momentum00:55

Linear Momentum

17.6K
The term momentum is used in various ways in everyday language, most of which are consistent with the precise scientific definition. Generally, momentum implies a tendency to continue on course—to move in the same direction; we tend to speak of sports teams or politicians gaining and maintaining the momentum to win.  Momentum is also associated with great mass and speed and is often considered when talking about collisions. For example, when rugby players collide and fall to the...
17.6K
Linearization and Approximation01:26

Linearization and Approximation

45
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
45
Components of Stress01:23

Components of Stress

509
Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
Interestingly, the hidden cube faces also experience these stresses, equal and...
509

You might also read

Related Articles

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

Sort by
Same author

A comparison of multivariate and univariate meta-analysis.

Behavior research methods·2026
Same author

The Wor2 phenotypic switching regulator controls biofilm formation in Candida auris.

NPJ biofilms and microbiomes·2026
Same author

Global emergence and rapid spread of <i>Candidozyma auris</i> (syn. <i>Candida auris</i>): epidemiology, biology, and antifungal resistance.

Clinical microbiology reviews·2026
Same author

Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populations.

Nature communications·2026
Same author

Modeling cyclic patterns using a two-stage hybrid Bayesian approach.

Psychological methods·2026
Same author

Overexpression of AS<i>vicR</i> combined with the antibacterial monomer DMAHDM interferes with the VicRK two-component system to attenuate the cariogenicity of <i>Streptococcus mutans</i>.

Frontiers in cellular and infection microbiology·2026
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 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

3.7K

Testing Variance Components in Linear Mixed Modeling Using Permutation.

Han Du1, Lijuan Wang2

  • 1Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA.

Multivariate Behavioral Research
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a permutation test for variance components in linear mixed models (LMMs) to address boundary issues. The unconstrained permutation test with a one-sided p-value is recommended as a practical alternative to likelihood ratio tests.

Keywords:
Hierarchical linear modelingnonparametric statisticsvariance testing

More Related Videos

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.3K

Related Experiment Videos

Last Updated: Jan 22, 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

3.7K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Methods

Background:

  • Linear mixed models (LMMs) are used to infer variance components, indicating individual or cluster heterogeneity.
  • Non-negative variance constraints in LMMs create parameter space boundaries, complicating standard statistical inference.
  • Existing inference methods may be problematic when testing variance components at the boundary (zero variance).

Purpose of the Study:

  • To introduce a practical permutation method for variance component inference in LMMs, specifically addressing the boundary issue.
  • To evaluate the performance of various permutation test settings under different data conditions (normal and non-normal).
  • To compare permutation tests with likelihood ratio (LR) tests for variance component inference.

Main Methods:

  • Development and examination of permutation tests with varied settings: constrained vs. unconstrained estimation, specific vs. generalized tests, and different p-value/permutation calculation methods.
  • Evaluation of test performance using both normal and non-normal data.
  • Comparison of permutation tests against LR tests using a mixture of chi-squared distributions as the reference distribution.

Main Results:

  • The unconstrained permutation test with a one-sided p-value approach demonstrated superior performance compared to other permutation test variations.
  • Permutation tests are identified as a valuable alternative when LR tests are not suitable for boundary-related inference in LMMs.
  • An R function is provided to simplify the implementation of these permutation tests in practical research.

Conclusions:

  • The unconstrained permutation test offers a robust and feasible solution for variance component inference in LMMs, particularly when dealing with boundary constraints.
  • Researchers can utilize the provided R function and findings to select appropriate statistical tests for their LMM analyses.
  • This work enhances the toolkit for analyzing heterogeneity in clustered or longitudinal data using LMMs.