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

Parallel-axis Theorem01:06

Parallel-axis Theorem

8.6K
The parallel-axis theorem provides a convenient and quick method of finding the moment of inertia of an object about an axis parallel to the axis passing through its center of mass. Consider a thin rod as an example. There is a striking similarity between the process of finding the moment of inertia of a thin rod about an axis through its middle, where the center of mass lies, and about an axis through its end using the conventional method. In the conventional method, the concept of linear mass...
8.6K
One-Way ANOVA01:18

One-Way ANOVA

14.5K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
14.5K
Parallel Processing01:20

Parallel Processing

875
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
875
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

2.0K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
2.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
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...
4.4K
Parallel-Axis Theorem for an Area01:12

Parallel-Axis Theorem for an Area

3.3K
The moment of inertia is a fundamental concept in mechanical engineering that plays a significant role in designing rotationally symmetric objects such as flywheels, gears, and other mechanical systems. In this context, we will discuss the moment of inertia of a flywheel rotating about its centroidal axis and how it relates to the moment of inertia about an axis parallel to it.
For a flywheel approximated as a solid disc, consider an infinitesimal differential element with an arbitrary distance...
3.3K

You might also read

Related Articles

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

Sort by
Same author

The Italian hand hygiene project: A joint-commission Italian network analysis.

Journal of infection and public health·2026
Same author

Digital Health Interventions to Promote Physical Activity in Community-Dwelling Older Adults: A Systematic Review and Semiquantitative Analysis.

International journal of public health·2025
Same author

Machine learning to predict overall short-term mortality in cutaneous melanoma.

Discover oncology·2023
Same author

[Fragility in a Public Health perspective: principles and tools for a "life course" approach prevention- oriented].

Igiene e sanita pubblica·2021
Same author

Real-world data for direct stage-specific costs of melanoma healthcare.

The British journal of dermatology·2020
Same author

Uncompleted Emergency Department Care (UEDC): a 5-year population-based study in the Veneto Region, Italy.

Journal of preventive medicine and hygiene·2019
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: Mar 26, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.3K

Remarks on Parallel Analysis.

A Buja, N Eyuboglu

    Multivariate Behavioral Research
    |January 27, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Parallel analysis (PA) offers a quasi-inferential method for determining the number of factors. This approach accounts for bias and variability, improving factor selection accuracy in statistical analyses.

    More Related Videos

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    2.0K
    2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
    08:23

    2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

    Published on: August 6, 2018

    12.1K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.3K
    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    2.0K
    2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
    08:23

    2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

    Published on: August 6, 2018

    12.1K

    Area of Science:

    • Statistics
    • Psychometrics
    • Data Analysis

    Background:

    • The number-of-factors problem is crucial in statistical analysis, impacting the interpretation of data.
    • Existing methods for factor selection may not adequately account for sampling variability and bias.
    • Parallel analysis (PA) is a widely used selection rule, but its inferential properties require further examination.

    Purpose of the Study:

    • To investigate parallel analysis (PA) from a permutation assessment perspective.
    • To develop a quasi-inferential, non-parametric version of PA that addresses finite-sample bias and sampling variability.
    • To compare different applications of PA, including principal components, principal factor analysis, resistant correlations, and loadings.

    Main Methods:

    • Applying permutation test principles to parallel analysis (PA).
    • Developing a quasi-inferential PA using normal random variates.
    • Comparing PA of principal components with PA of principal factor analysis.
    • Applying PA to resistant correlations and factor loadings.

    Main Results:

    • A quasi-inferential, non-parametric PA was developed, accounting for bias and sampling variability.
    • Quasi-inferential PA using normal random variates shows independence from distributional assumptions.
    • PA of principal factors may overselect factors compared to PA of principal components.
    • PA applied to resistant correlations offers a more conservative factor selection method.
    • PA applied to loadings provides data-sensitive benchmark values, improving on fixed thresholds.

    Conclusions:

    • The quasi-inferential, non-parametric PA provides a robust method for factor selection.
    • PA applied to loadings offers a significant improvement over conventional fixed thresholds for interpreting factor analysis results.
    • The study justifies the use of quasi-inferential PA and provides practical guidelines for its application.