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

Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

3.3K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
3.3K
Phase Diagrams02:39

Phase Diagrams

50.2K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
50.2K
Mean free path and Mean free time01:22

Mean free path and Mean free time

5.2K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
5.2K
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

4.0K
Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
4.0K
Modeling with Differential Equations01:25

Modeling with Differential Equations

84
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
84
Chemical Equations03:10

Chemical Equations

81.6K
Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and right)...
81.6K

You might also read

Related Articles

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

Sort by
Same author

Synchrony in therapist's and patient's vocally encoded arousal and its association with the quality of the therapeutic relationship.

Psychotherapy research : journal of the Society for Psychotherapy Research·2026
Same author

Registered Report Stage II: A within-person model of distress tolerance and non-suicidal self-injury among adults with recurrent self-injury.

Journal of research in personality·2026
Same author

How to Use Residual Dynamic Structural Equation Modeling to Study Individual Differences and Intraindividual Variability in Experimental Factorial Designs: A Tutorial.

Multivariate behavioral research·2026
Same author

Association of unhealthy alcohol use reported in routine outpatient screening with 30-day hospital readmission risk.

Journal of substance use and addiction treatment·2026
Same author

"You're Hoping for the Best, but Preparing for the Worst": Discussions of Starting Buprenorphine in the Context of Fentanyl Use with Clinicians and People Who Use Fentanyl.

Journal of general internal medicine·2026
Same author

Using machine learning to identify unique predictors of alcohol and cannabis impaired driving.

Alcohol, clinical & experimental research·2026
Same journal

Life satisfaction across patterns of cigarette and e-cigarette use among adolescents: evidence from a national school-based survey.

Addictive behaviors·2026
Same journal

The prospective relationship between craving and the likelihood of "unknown" substance use motive endorsement.

Addictive behaviors·2026
Same journal

An evaluation of anxiety and depressive symptoms in terms of smoking among Black adults.

Addictive behaviors·2026
Same journal

Loot box purchases are associated with problem gambling severity and harms beyond traditional gambling activities.

Addictive behaviors·2026
Same journal

Harm perceptions of smoking versus vaping cannabis and correlates: national surveys of youth and young adults in England, Canada, and the United States.

Addictive behaviors·2026
Same journal

Examining ecological momentary assessment (EMA) alone versus EMA with personalized feedback for hazardous drinking among Korean college students.

Addictive behaviors·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.6K

Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization.

Kevin A Hallgren1, Connor J McCabe2, Kevin M King2

  • 1Department of Psychiatry and Behavioral Sciences, University of Washington, USA.

Addictive Behaviors
|September 17, 2018
PubMed
Summary
This summary is machine-generated.

Data visualization enhances structural equation modeling (SEM) in addictive behaviors research by improving transparency and communication. This method visualizes data points, unlike traditional path diagrams, aiding understanding for diverse stakeholders.

Keywords:
Applied data analysisData visualizationLatent variable modelingMediationModerationStructural equation model

More Related Videos

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.1K
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.9K

Related Experiment Videos

Last Updated: Feb 5, 2026

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.6K
Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.1K
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.9K

Area of Science:

  • Behavioral Science
  • Data Science
  • Statistical Modeling

Background:

  • Structural Equation Modeling (SEM) is widely used in addictive behaviors research.
  • Traditional SEM reporting relies on tables and path diagrams, lacking data point transparency.
  • Current methods can obscure anomalies and hinder communication with non-statistical audiences.

Purpose of the Study:

  • To demonstrate how data visualization can improve SEM in addictive behaviors research.
  • To differentiate data visualization from standard SEM model visualizations.
  • To provide practical methods for integrating data visualization into SEM.

Main Methods:

  • Introduction to SEM and data visualization techniques.
  • Distinction between data visualization and model visualization (e.g., path diagrams).
  • Examples of visualizing latent variables and multivariate relations using R syntax for correlation, regression, moderation, and mediation.

Main Results:

  • Data visualization offers a clearer representation of underlying data in SEM.
  • Visualizations can reveal unexpected data patterns and heterogeneity.
  • R syntax enables the generation of visualizations for common SEM effects.

Conclusions:

  • Data visualization can significantly enhance methodological transparency in SEM research.
  • Implementing data visualization improves the communication of SEM findings to broader audiences.
  • This approach has the potential to increase the impact of SEM-based research on addictive behaviors.