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

State Space Representation01:27

State Space Representation

367
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
367
Modeling in Therapy01:26

Modeling in Therapy

246
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
246
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

249
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
249
Steps in the Modeling Process01:14

Steps in the Modeling Process

464
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
464
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

359
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,...
359
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

151
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...
151

You might also read

Related Articles

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

Sort by
Same author

Long-Acting HIV Pre-exposure Prophylaxis Preferences Through Pregnancy and the Postpartum Period Among Women in Kenya: A Latent Transition Analysis.

AIDS and behavior·2026
Same author

Age and gender differences in the factor structure of cognitive monitoring.

The Journal of general psychology·2026
Same author

Prior Authorization of Medication and Its Influence on Provider Behavior: Latent Class Analysis.

Journal of medical Internet research·2025
Same author

Integration of a brief, transdiagnostic psychological intervention in the care of adolescents and young adults with HIV in Kenya: Protocol for a cluster randomized clinical trial.

PloS one·2025
Same author

Latent Profiles of Sport Motivation in Czech University Students: An Exploratory Person-Centered Approach Using the Sport Motivation Scale.

European journal of sport science·2025
Same author

Commentary: The Need for Theories of Change in Training and Technical Assistance: Where the Rubber Meets the Road.

Evaluation & the health professions·2024

Related Experiment Video

Updated: Nov 18, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.7K

Modeling Behavior as Dynamic Sequential States: Introduction to the Special Issue.

Brian P Flaherty1, Lawrence M Scheier2,3

  • 1Quantitative Psychology, 7284University of Washington, Seattle, WA, USA.

Evaluation & the Health Professions
|February 4, 2021
PubMed
Summary
This summary is machine-generated.

Latent transition analysis (LTA) models discrete changes over time in complex states like drug use patterns. This special issue explores new applications and extensions of LTA, highlighting its flexibility and underutilization in research.

Keywords:
developmental studiesdynamic sequential stateslatent class analysislatent transition analysismodeling change states

More Related Videos

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.4K

Related Experiment Videos

Last Updated: Nov 18, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.7K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.4K

Area of Science:

  • * Behavioral Sciences
  • * Health Professions
  • * Longitudinal Data Analysis

Background:

  • * Latent Class (LC) models analyze discrete states.
  • * Latent Transition Analysis (LTA) extends LC models to track changes over time.
  • * LTA is underutilized despite its flexibility in modeling qualitative change.

Purpose of the Study:

  • * To showcase diverse applications of Latent Transition Analysis (LTA).
  • * To present extensions enhancing LTA's capabilities for modeling discrete change.
  • * To promote the use of LTA in analyzing dynamic patterns over time.

Main Methods:

  • * Application of Latent Transition Analysis (LTA) to various datasets.
  • * Development of novel extensions to the LTA framework.
  • * Longitudinal modeling of discrete changes in response patterns.

Main Results:

  • * Demonstrated flexibility of LTA in capturing complex state changes.
  • * Identified new ways to extend LTA for advanced longitudinal analysis.
  • * Showcased successful applications across different research areas.

Conclusions:

  • * LTA is a powerful, yet underutilized, tool for analyzing dynamic processes.
  • * Further research and application of LTA can yield significant insights.
  • * This special issue provides valuable contributions to LTA methodology and practice.