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

Survival Tree01:19

Survival Tree

125
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
125
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

648
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
648
Contingency Table01:29

Contingency Table

2.6K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.6K
Phylogenetic Trees03:21

Phylogenetic Trees

45.7K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
45.7K
Response Surface Methodology01:16

Response Surface Methodology

197
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
197
Complementation Tests00:49

Complementation Tests

5.0K
A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
Organisms heterozygous for different mutations are crossed pairwise in all combinations. If present on different genes, the mutations can complement each other by providing the missing...
5.0K

You might also read

Related Articles

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

Sort by
Same author

A time-varying interaction map approach for longitudinal assessments.

Psychological methods·2026
Same author

A Latent Space Graded Response Model for Likert-Scale Psychological Assessments.

Multivariate behavioral research·2025
Same author

Mapping the mHealth Nexus: A Semantic Analysis of mHealth Scholars' Research Propensities Following an Interdisciplinary Training Institute.

Applied sciences (Basel, Switzerland)·2025
Same author

Network Approaches to Binary Assessment Data: Network Psychometrics Versus Latent Space Item Response Models.

Educational and psychological measurement·2025
Same author

Integration of latent space and confirmatory factor analysis to explain unexplained person-item interactions.

Psychological methods·2025
Same author

Psychometric Properties of the Science Self-Efficacy Scale for STEMM Undergraduates.

European journal of investigation in health, psychology and education·2025
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: Aug 1, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K

Commentary: Explore Conditional Dependencies in Item Response Tree Data.

Minjeong Jeon1

  • 1University of California, Los Angeles, Los Angeles, USA. mjjeon@ucla.edu.

Psychometrika
|April 27, 2023
PubMed
Summary
This summary is machine-generated.

Conditional dependencies, not item-specific factors, may explain lower item slopes in item response tree (IRTree) models. This commentary explores this alternative explanation using a latent space model for IRTree data.

Keywords:
IRTree modelsconditional dependenciesinteraction maplatent space item response modelspolytomous item responses

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K
Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.6K

Related Experiment Videos

Last Updated: Aug 1, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K
Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.6K

Area of Science:

  • Psychometrics
  • Statistical modeling
  • Educational measurement

Background:

  • Item response tree (IRTree) models analyze polytomous item response data using a tree structure.
  • A recent study noted lower item slopes at later nodes in IRTree applications.
  • This phenomenon was previously attributed to item-specific factors.

Purpose of the Study:

  • To propose conditional dependencies as a more general explanation for lower item slopes in IRTree models.
  • To illustrate this alternative perspective with an empirical example.
  • To discuss the broader implications of exploring conditional dependencies in IRTree data.

Main Methods:

  • The commentary presents a theoretical argument for conditional dependencies.
  • An empirical example is provided using the latent space item response model.
  • The latent space model is used to visualize conditional dependencies within IRTree data.

Main Results:

  • The commentary argues that conditional dependencies offer a more general explanation for the observed phenomenon.
  • The latent space model effectively visualizes these conditional dependencies.
  • The analysis suggests that the phenomenon is not solely due to item-specific factors.

Conclusions:

  • Conditional dependencies provide a compelling alternative to item-specific factors for explaining lower item slopes in IRTree models.
  • The use of latent space models can reveal and visualize these dependencies.
  • Further research into conditional dependencies can enhance the understanding and application of IRTree models.