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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
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...
290
Longitudinal Studies01:26

Longitudinal Studies

563
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
563
Longitudinal Research02:20

Longitudinal Research

13.5K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.5K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

578
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
578
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

277
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...
277
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

614
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,...
614

You might also read

Related Articles

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

Sort by
Same author

Identifying careless responding in ecological momentary assessment: Inconsistent signals from different detection methods in the WARN-D Data.

Psychological methods·2026
Same author

Remembering affect between moments: assessing peak-end effects in continuous affect measures.

Cognition & emotion·2026
Same author

Love in motion: Bringing temporal and interpersonal dynamics into the formula of love.

The Behavioral and brain sciences·2026
Same author

"Thinking about Tomorrow Clears Away the Cobwebs and the Sorrow:<sup>"</sup>Daily Anticipation, Positive Affect, and Stressor Related Negative Affect.

Affective science·2026
Same author

Hypertension in Young Adults: Social Determinants of Prevalence, Awareness, Treatment, and Control.

American journal of hypertension·2025
Same author

Correlated Residuals in Lagged-Effects Models: What They (Do Not) Represent in the Case of a Continuous-Time Process.

Multivariate behavioral research·2025

Related Experiment Video

Updated: Feb 22, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

On the Use of Mixed Markov Models for Intensive Longitudinal Data.

S de Haan-Rietdijk1, P Kuppens2, C S Bergeman3

  • 1a Methodology and Statistics, Faculty of Social and Behavioural Sciences , Utrecht University.

Multivariate Behavioral Research
|September 29, 2017
PubMed
Summary

This study introduces mixed Markov models for analyzing state-switching processes in intensive longitudinal data. These models effectively capture individual differences and heterogeneity in psychological research.

Keywords:
Intensive longitudinal datalatent Markov modelmixed Markov modelstate switchingtime series analysis

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Related Experiment Videos

Last Updated: Feb 22, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Area of Science:

  • Psychology
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Markov modeling is a flexible framework for analyzing state-switching processes over time.
  • Intensive longitudinal data in psychology increasingly requires sophisticated analytical methods.
  • Subject-specific random effects can account for heterogeneity in these processes.

Purpose of the Study:

  • To apply mixed Markov models to intensive longitudinal data in psychology.
  • To examine how model specifications change with continuous random effect distributions.
  • To demonstrate the utility of mixed Markov models in psychological research.

Main Methods:

  • Application of mixed Markov models to intensive longitudinal data.
  • Inclusion of continuous random effect distributions.
  • Bayesian estimation techniques.

Main Results:

  • Mixed Markov models provide a rich description of individual processes over time.
  • Model specifications are adaptable when incorporating continuous random effects.
  • Bayesian estimation offers advantages for complex models.

Conclusions:

  • Mixed Markov models are a valuable tool for analyzing complex state-switching dynamics in psychological research.
  • The approach is well-suited for intensive longitudinal datasets.
  • Empirical applications illustrate the practical utility of the method.