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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

475
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...
475
Factorial Design02:01

Factorial Design

13.7K
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...
13.7K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

488
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
488
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

278
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
278
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

237
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
237

You might also read

Related Articles

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

Sort by
Same author

A Genetic Algorithm for Automated Assembly of Linear Multidimensional Forced-Choice Questionnaires.

Psychometrika·2026
Same author

Can the Generalized Graded Unfolding Model Fit Dominance Responses?

Applied psychological measurement·2025
Same author

Information Functions of Rank-2PL Models for Forced-Choice Questionnaires.

Journal of educational measurement·2025
Same author

Item and Test Characteristic Curves of Rank-2PL Models for Multidimensional Forced-Choice Questionnaires.

Applied measurement in education·2025
Same author

The Rank-2PL IRT Models for Forced-Choice Questionnaires: Maximum Marginal Likelihood Estimation with an EM Algorithm.

Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association·2025
Same author

From Likert to Forced Choice: Statement Parameter Invariance and Context Effects in Personality Assessment.

Measurement : interdisciplinary research and perspectives·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: Jan 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Multidimensional Generalized Partial Preference Model for Forced-Choice Items.

Daniel C Furr1, Jianbin Fu2

  • 1Transfr, USA.

Psychometrika
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

A new ranking pattern approach and multidimensional generalized partial preference model (MGPPM) enhance item response theory (IRT) for forced-choice (FC) items. This flexible modeling improves upon existing methods for analyzing complex survey data.

Keywords:
dominance modelforced-choice questionnaireitem response theory model

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Related Experiment Videos

Last Updated: Jan 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • Item response theory (IRT) models are crucial for analyzing survey data.
  • Forced-choice (FC) items present unique challenges for traditional IRT models.
  • Existing IRT approaches for FC items include sequential selection and Thurstone's law of pairwise comparison.

Purpose of the Study:

  • To introduce a novel ranking pattern approach for developing IRT models for FC items.
  • To propose a new dominance IRT model, the multidimensional generalized partial preference model (MGPPM), for FC items with multiple statements.
  • To develop estimation methods for the proposed MGPPM.

Main Methods:

  • Development of a ranking pattern approach for IRT modeling of FC items.
  • Proposal of the multidimensional generalized partial preference model (MGPPM).
  • Implementation of maximum marginal likelihood estimation using an expectation-maximization algorithm (MML-EM) and Markov chain Monte Carlo (MCMC) estimation.
  • Conducting simulation studies with triplet and tetrad data.
  • Comparison of the MGPPM with Thurstonian IRT (TIRT) and Triplet-2PLM models using simulated and real data.

Main Results:

  • The proposed ranking pattern approach offers more flexible IRT modeling for FC items compared to existing methods.
  • The MGPPM demonstrates satisfactory parameter recovery in simulation studies.
  • The MGPPM is found to be statistically more elegant than the TIRT and Triplet-2PLM models.
  • Empirical comparisons show the advantages of the new approach and model.

Conclusions:

  • The ranking pattern approach provides a flexible framework for IRT modeling of FC items.
  • The MGPPM is a statistically sound and elegant model for analyzing FC data with multiple statements.
  • This new methodology advances the analysis of complex survey instruments and psychological measurements.