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

13.6K
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...
13.6K
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

335
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
335
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.5K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

417
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...
417
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

183
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
183
Cause and Effect01:53

Cause and Effect

11.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
11.9K

You might also read

Related Articles

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

Sort by
Same author

Predictors of Retention and Sustained Engagement With a 12-Month Asthma Self-Management App in Underserved Adults.

Respiratory care·2026
Same author

Content-specific engagement with a mobile asthma education application: a real-world analysis of ASTHMAXcel PRO.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same author

Complete chloroplast genome and phylogenetic analysis of <i>Cardamine leucantha</i> L. 1836 (Brassicaceae).

Mitochondrial DNA. Part B, Resources·2026
Same author

Characterization and phylogenetic analysis of the chloroplast genome of <i>Conium maculatum</i> L. 1753 (Apiaceae).

Mitochondrial DNA. Part B, Resources·2025
Same author

Assembly and comparative analysis of four complete mitochondrial genomes of Pulsatilla species.

BMC plant biology·2025
Same author

Extracting Neutron-Neutron Interaction Strength and Spatiotemporal Dynamics of Neutron Emission from the Two-Particle Correlation Function.

Physical review letters·2025

Related Experiment Video

Updated: Dec 15, 2025

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

981

Empirical Underidentification with the Bifactor Model: A Case Study.

Samuel Green1, Yanyun Yang2

  • 1Arizona State University, Tempe, AZ, USA.

Educational and Psychological Measurement
|July 14, 2020
PubMed
Summary

This study examines empirical underidentification in bifactor models, common in psychology and education. It offers insights and strategies for researchers using structural equation modeling to address these identification issues.

Keywords:
bifactor modelempirical underidentificationstructural equation modeling

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

7.2K
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

1.1K

Related Experiment Videos

Last Updated: Dec 15, 2025

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

981
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

7.2K
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

1.1K

Area of Science:

  • Psychometrics
  • Structural Equation Modeling
  • Educational Measurement

Background:

  • Bifactor models are widely used to evaluate psychological and educational constructs.
  • Empirical underidentification poses challenges when fitting these models to specific datasets.

Purpose of the Study:

  • To enhance understanding of empirical identification issues in bifactor models.
  • To provide insights into empirical underidentification specific to bifactor models.
  • To propose practical strategies for addressing underidentification in structural equation modeling.

Main Methods:

  • Analysis of empirical underidentification problems.
  • Exploration of bifactor model fitting challenges.
  • Development of strategies for structural equation model users.

Main Results:

  • Identified common underidentification issues in bifactor models.
  • Provided a framework for understanding these identification problems.
  • Offered practical solutions for researchers.

Conclusions:

  • Empirical underidentification is a critical consideration for bifactor models.
  • Methodologists and users of structural equation modeling can benefit from proposed strategies.
  • Addressing these issues ensures more robust construct assessment.