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

Response Surface Methodology01:16

Response Surface Methodology

62
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:
62
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

59
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
59
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

43
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
43
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

51
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
51
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

56
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,...
56

You might also read

Related Articles

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

Sort by
Same author

Safety, tolerability, pharmacokinetics, and pharmacodynamics of the oral allosteric TYK2 inhibitor ESK-001 using a randomized, double-blind, placebo-controlled study design.

Clinical and translational science·2024
Same author

Investigating Directional Invariance in an Item Response Tree Model for Extreme Response Style and Trait-Based Unfolding Responses.

Applied psychological measurement·2024
Same author

Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.

Psychometrika·2023
Same author

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy.

The British journal of mathematical and statistical psychology·2023
Same author

Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.

Psychometrika·2023
Same author

Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.

Psychometrika·2023
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Video

Updated: May 8, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.0K

A sparse latent class model incorporating response times.

Siqi He1, Steven Andrew Culpepper1, Jeffrey A Douglas1

  • 1University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.

The British Journal of Mathematical and Statistical Psychology
|December 26, 2024
PubMed
Summary
This summary is machine-generated.

This study integrates response times (RTs) into sparse latent class models (SLCM) for diagnostic modeling. The enhanced framework offers a flexible approach to jointly analyze item responses and RTs in assessments.

Keywords:
Gibbs samplingpersonality assessmentsresponse timesparse latent class models

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

463
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K

Related Experiment Videos

Last Updated: May 8, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.0K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

463
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Assessment

Background:

  • Diagnostic models (DM) are crucial for classifying latent attributes in assessments.
  • Integrating response times (RTs) with DM provides deeper insights into problem-solving behaviors.
  • Existing sparse latent class models (SLCM) primarily focus on item responses, with limited exploration of RT data.

Purpose of the Study:

  • To extend the sparse latent class model (SLCM) framework by incorporating response time (RT) data.
  • To develop a more flexible model for jointly analyzing item responses and RTs, relaxing conditional independence assumptions.
  • To apply the novel model to personality assessments, specifically the Fisher Temperament Inventory, and explore its utility.

Main Methods:

  • Extension of the sparse latent class model (SLCM) to include response time (RT) data.
  • Relaxation of the conditional independence assumption between RT and latent attributes, conditional on individual speed.
  • Development and implementation of a Gibbs sampling algorithm for parameter estimation.

Main Results:

  • The proposed extended SLCM framework effectively integrates response time (RT) data with item responses.
  • Application to the Fisher Temperament Inventory provided novel insights into personality assessment using DM with RT.
  • Monte Carlo simulations confirmed the accuracy and efficiency of the proposed Gibbs sampling algorithm for parameter estimation.

Conclusions:

  • The extended SLCM offers a flexible and powerful approach for jointly modeling item responses and response times in diagnostic assessments.
  • This methodology provides a novel perspective for personality assessments and can be valuable in educational and psychological measurement.
  • The validated Gibbs sampling algorithm ensures reliable parameter estimation for the proposed model.