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

Factorial Design02:01

Factorial Design

15.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
15.1K
Two-Way ANOVA01:17

Two-Way ANOVA

3.5K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.5K
One-Way ANOVA01:18

One-Way ANOVA

14.1K
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...
14.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

538
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...
538
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.9K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.9K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Data driven pathway analysis and forecast of global warming and sea level rise.

Scientific reports·2023
Same author

Alpha, FACTT, and Beyond.

Psychometrika·2021
Same author

Distributionally weighted least squares in structural equation modeling.

Psychological methods·2021
Same author

Psychometric Properties of the Kidney Disease Quality of Life 36-Item Short-Form Survey (KDQOL-36) in the United States.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2017
Same author

Covariate-free and Covariate-dependent Reliability.

Psychometrika·2016
Same author

Specificity-enhanced reliability coefficients.

Psychological methods·2016
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Exploratory Bi-factor Analysis: The Oblique Case.

Robert I Jennrich1, Peter M Bentler2

  • 1University of California, Los Angeles, 3400 Purdue Ave., Los Angeles, CA, USA. rij@stat.ucla.edu.

Psychometrika
|August 14, 2016
PubMed
Summary
This summary is machine-generated.

Oblique rotation in exploratory bi-factor analysis offers a superior approximation of the bi-factor structure compared to orthogonal rotation. This method aids in identifying explicit structures for confirmatory bi-factor analysis.

Keywords:
bi-factor rotationgeneral factorgradient projection algorithmsgroup factoroblique rotationorthogonal rotation

More Related Videos

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

7.3K
A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

1.2K

Related Experiment Videos

Last Updated: Mar 16, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K
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

7.3K
A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

1.2K

Area of Science:

  • Psychometrics
  • Statistical Analysis

Background:

  • Bi-factor analysis, a type of confirmatory factor analysis, was initially proposed by Holzinger and Swineford in 1937.
  • The traditional bi-factor model incorporates a general factor, multiple group factors, and a defined bi-factor structure.
  • Jennrich and Bentler (2011) developed an exploratory version of bi-factor analysis, removing the need for a pre-specified structure.

Purpose of the Study:

  • To investigate the utility of oblique rotation within exploratory bi-factor analysis.
  • To compare the effectiveness of oblique rotation against orthogonal rotation in approximating bi-factor structures.
  • To explore the application of exploratory bi-factor analysis as a tool for defining structures in confirmatory analyses.

Main Methods:

  • Exploratory factor analysis was employed, utilizing a specific bi-factor rotation criterion.
  • The study focused on comparing orthogonal rotation with oblique rotation techniques.
  • Ideal data was used to evaluate the performance of oblique bi-factor rotation methods.

Main Results:

  • Oblique rotations demonstrated a better approximation of the bi-factor structure than orthogonal rotations.
  • This improved approximation is attributed to the greater number of available oblique rotations.
  • A notable and unexpected outcome was observed when applying oblique bi-factor rotation methods to ideal data.

Conclusions:

  • Oblique rotation is a more effective method for achieving an approximate bi-factor structure in exploratory analyses.
  • The findings suggest that oblique bi-factor rotation can serve as a valuable aid in establishing explicit structures for confirmatory bi-factor analysis.
  • Further research is warranted to understand the surprising results obtained with ideal data.