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

Survival Tree01:19

Survival Tree

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 survival tree begins...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
Typical Model Studies01:30

Typical Model Studies

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Automated emotion recognition via video-based semantic embeddings.

Frontiers in digital health·2026
Same author

Stable individual differences dominate adult brain volume variation until later life.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

To Be FAIR: Theory Specification Needs an Update.

Perspectives on psychological science : a journal of the Association for Psychological Science·2026
Same author

Expertise-Dependent Brain Network Organization During Music Perception.

Human brain mapping·2025
Same author

Vulnerability to memory decline in aging revealed by a mega-analysis of structural brain change.

Nature communications·2025
Same author

Sex differences in healthy brain aging are unlikely to explain higher Alzheimer's disease prevalence in women.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R.

Psychological methods·2026
Same journal

Heterogeneous variance models with Gaussian processes.

Psychological methods·2026
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
See all related articles

Related Experiment Video

Updated: May 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

Structural equation model trees.

Andreas M Brandmaier1, Timo von Oertzen, John J McArdle

  • 1Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany. brandmaier@mpib-berlin.mpg.de

Psychological Methods
|September 19, 2012
PubMed
Summary
This summary is machine-generated.

SEM Trees integrate structural equation models (SEMs) with decision trees to identify subgroups with distinct SEM parameter estimates. This method reveals covariates and interactions influencing structural parameters in observed and latent spaces.

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

Related Experiment Videos

Last Updated: May 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

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

Area of Science:

  • Behavioral and social sciences
  • Quantitative psychology
  • Statistical modeling

Background:

  • Structural Equation Models (SEMs) are standard for analyzing relationships between latent and observed variables in social sciences.
  • SEMs unify various multivariate analysis techniques.
  • A need exists for methods that explore data heterogeneity and identify predictors of parameter variations.

Purpose of the Study:

  • Introduce SEM Trees, a novel approach combining SEMs and decision trees.
  • Demonstrate how SEM Trees recursively partition data into subsets with differing SEM parameter estimates.
  • Highlight the utility of SEM Trees in identifying covariates and interactions affecting structural parameters.

Main Methods:

  • SEM Trees recursively partition data based on heterogeneity in SEM parameter estimates.
  • The methodology integrates decision tree algorithms with SEM.
  • Applications are demonstrated using a factor model and a linear growth curve model.

Main Results:

  • SEM Trees successfully identify subgroups with significantly different SEM parameter estimates.
  • The approach reveals covariates and their interactions that predict variations in structural parameters.
  • Demonstrated effectiveness in both observed and latent variable spaces.

Conclusions:

  • SEM Trees offer a powerful tool for theory-guided data exploration in SEM.
  • This method enhances understanding of heterogeneity in structural relationships.
  • Facilitates the discovery of complex covariate effects within SEM frameworks.