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

Longitudinal Studies01:26

Longitudinal Studies

191
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...
191
Longitudinal Research02:20

Longitudinal Research

12.0K
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...
12.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

66
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...
66
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

193
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,...
193
Cross-Sectional Research01:50

Cross-Sectional Research

11.4K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.4K
Typical Model Studies01:30

Typical Model Studies

385
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
385

You might also read

Related Articles

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

Sort by
Same author

Neuropsychological impairments in emotion recognition compared to general cognition: profiles across six different neurological disorders.

Journal of neurology·2026
Same author

External Validation of a Prognostic Model for Outcome After Mild Traumatic Brain Injury at 6 Months Post Injury.

European journal of neurology·2026
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Augmenting virtual reality exposure for PTSD with physical activity: study protocol of a randomised controlled trial.

European journal of psychotraumatology·2026
Same author

Understanding the structure of coping strategies in context: a psychometric validation of the Brief-COPE among Colombian adults.

Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS·2025
Same author

An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models.

The British journal of mathematical and statistical psychology·2025
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Jul 26, 2025

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

6.4K

A comprehensive model framework for between-individual differences in longitudinal data.

Anja F Ernst1, Casper J Albers1, Marieke E Timmerman1

  • 1Department Psychometrics and Statistics, University of Groningen.

Psychological Methods
|June 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a unified framework for comparing longitudinal models, simplifying their application and interpretation. The framework integrates various models, aiding researchers in understanding and selecting appropriate methods for analyzing change over time.

More Related Videos

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

3.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K

Related Experiment Videos

Last Updated: Jul 26, 2025

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

6.4K
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

3.4K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K

Area of Science:

  • Statistics
  • Psychology
  • Data Analysis

Background:

  • Longitudinal models vary widely in structure and terminology, hindering cross-study comparisons.
  • Existing models often lack a unified approach for analyzing within- and between-individual differences over time.

Purpose of the Study:

  • To propose a comprehensive model framework for comparing diverse longitudinal models.
  • To simplify the empirical application and interpretation of longitudinal data analysis.
  • To provide guidance for researchers on selecting and specifying models accounting for individual differences.

Main Methods:

  • Developed a general model framework integrating within- and between-individual analyses.
  • Incorporated attributes like growth, decline, cyclical trends, and variable interplay.
  • Included continuous and categorical latent variables for between-individual differences.
  • Demonstrated framework's utility by unifying multilevel regression, growth curve, growth mixture, and vector-autoregressive models.

Main Results:

  • The proposed framework successfully unifies several well-established longitudinal models.
  • It allows for clear comparisons between different modeling approaches.
  • The framework accommodates complex longitudinal data structures and individual variations.

Conclusions:

  • A unified framework enhances the understanding and application of longitudinal models.
  • This approach aids researchers in selecting appropriate methods for analyzing change and individual differences.
  • The framework offers extensions and recommendations for empirical research.