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

Econometric Views (EViews)01:29

Econometric Views (EViews)

562
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
562
Survival Tree01:19

Survival Tree

390
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
390
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

250
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...
250
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.0K
Structural Classification of Joints01:20

Structural Classification of Joints

7.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

503
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,...
503

You might also read

Related Articles

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

Sort by
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Measurement invariance of the Strengths and Difficulties Questionnaire (SDQ) across age groups in a German representative sample: An application of confirmatory factor analysis using k-fold cross-validation.

Psychological assessment·2026
Same author

Beyond the cross-section: Rethinking the intention-behaviour gap through a conceptual and methodological lens.

British journal of health psychology·2025
Same author

Electrophysiological resting-state signatures link polygenic scores to general intelligence.

Scientific reports·2025
Same author

Antibacterial Polymers Based on Two Orthogonal Binding Motifs Coalesce with Bacterial Matter.

ACS applied bio materials·2025
Same author

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage·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: Jan 18, 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

3.7K

Continuous-time structural equation model forests.

Pablo F Cáncer1, Manuel Arnold2, Eduardo Estrada3

  • 1UNINPSI Clinical Psychology Center, Department of Psychology, Universidad Pontificia Comillas.

Psychological Methods
|June 5, 2025
PubMed
Summary
This summary is machine-generated.

Continuous-time structural equation model (CT-SEM) forests offer a novel approach to analyzing longitudinal data with irregular intervals. This method improves upon discrete-time models, providing more accurate insights into individual differences in change and dynamics.

More Related Videos

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

6.7K

Related Experiment Videos

Last Updated: Jan 18, 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

3.7K
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

6.7K

Area of Science:

  • Longitudinal data analysis
  • Structural equation modeling
  • Psychometrics

Background:

  • Traditional longitudinal SEM trees/forests used discrete-time models.
  • Discrete-time models yield biased estimates with unevenly spaced longitudinal data.
  • Previous continuous-time SEM (CT-SEM) implementations were computationally intensive and produced biased results.

Purpose of the Study:

  • Introduce a novel, computationally feasible implementation of CT-SEM forests.
  • Address limitations of discrete-time models in longitudinal research.
  • Investigate heterogeneity in change and dynamics using CT-SEM forests.

Main Methods:

  • Combined ctsemOMX package for CT modeling.
  • Utilized semtree package for recursive partitioning.
  • Integrated score-guided covariate testing from strucchange package.
  • Conducted a Monte Carlo simulation study.
  • Applied the method to empirical data from the Survey of Health, Ageing, and Retirement in Europe.

Main Results:

  • The novel implementation of CT-SEM forests is computationally feasible.
  • The approach effectively handles irregular sampling schemes in longitudinal data.
  • Demonstrated the utility of CT-SEM forests on real-world data.

Conclusions:

  • CT-SEM forests provide a robust method for analyzing longitudinal data with irregular time intervals.
  • This approach enhances the investigation of individual differences in developmental trajectories.
  • Offers a valuable tool for researchers studying change and dynamics in complex datasets.