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

15.6K
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...
15.6K
Two-Way ANOVA01:17

Two-Way ANOVA

3.6K
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...
3.6K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

17.2K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
17.2K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

567
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...
567
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

4.2K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Alcohol-free and low-alcohol beverage consumption and concurrent and subsequent alcohol outcomes among young adults.

Addiction (Abingdon, England)·2026
Same author

Validity evidence for the Patient Reported Outcome Measurement Information System (PROMIS)<sup>®</sup> Cognitive Screener (PRO-CS) to detect risk for cognitive decline as part of the Medicare Annual Wellness Visit.

Advances in patient-reported outcomes·2026
Same author

Preventing alcohol and cannabis-impaired driving among adolescents: Effects of a web-intervention in a driver education program.

Journal of substance use and addiction treatment·2026
Same author

Characteristics associated with alcohol and cannabis-related impaired risky driving and riding behaviors among adolescents.

Traffic injury prevention·2026
Same author

Synergistic retinal UCHL1 dysregulation and synaptic vulnerability reflect Alzheimer's disease severity.

bioRxiv : the preprint server for biology·2026
Same author

Social networks, cultural pride, and historical loss among non-reservation American Indian/Alaska native emerging adults.

BMC public health·2025
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Mar 30, 2026

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

Evaluating bifactor models: Calculating and interpreting statistical indices.

Anthony Rodriguez1, Steven P Reise1, Mark G Haviland2

  • 1Department of Psychology, University of California.

Psychological Methods
|November 3, 2015
PubMed
Summary
This summary is machine-generated.

This study reviews statistical indices from bifactor measurement models, crucial for psychometric analysis of personality and psychopathology measures. These indices enhance understanding of composite scores and structural equation modeling quality.

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
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

Related Experiment Videos

Last Updated: Mar 30, 2026

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
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
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

Area of Science:

  • Psychometrics
  • Psychological Measurement
  • Structural Equation Modeling

Background:

  • Bifactor measurement models are increasingly used for personality and psychopathology.
  • Previous research often focuses on model fit, overlooking key psychometric indices.
  • Statistical indices offer deeper insights into measurement quality.

Purpose of the Study:

  • To review valuable statistical indices derived from bifactor models.
  • To demonstrate how these indices inform psychometric analysis.
  • To guide the interpretation of composite scores and model quality.

Main Methods:

  • Review of statistical indices from bifactor models.
  • Description of calculation methods for key indices.
  • Application of indices to assess score composites and measurement models.

Main Results:

  • Identified key indices: omega reliability, factor determinacy, construct reliability, explained common variance, and percentage of uncontaminated correlations.
  • Demonstrated utility of indices for evaluating unit-weighted and factor scores.
  • Showcased application in structural equation modeling for model specification and quality assessment.

Conclusions:

  • Bifactor model indices offer substantial improvements over solely relying on model fit.
  • These indices are essential for robust psychometric analysis and accurate interpretation of measurement data.
  • Utilizing these statistical insights enhances the quality of personality and psychopathology assessments.