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

Correlation and Regression00:53

Correlation and Regression

2.6K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.6K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Correlations02:20

Correlations

34.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
34.7K
Regression Analysis01:11

Regression Analysis

6.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.5K
Correlation01:09

Correlation

13.0K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
13.0K
Residual Plots01:07

Residual Plots

5.2K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Cognitive-affective and behavioral pain mechanisms in individuals with chronic low back pain: a network analysis.

Pain·2026
Same author

Comparing variable selection and model averaging methods for logistic regression.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Non-random patterns in the co-occurrence and accumulation of adverse life events in two national panel datasets.

Communications psychology·2026
Same author

Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models.

Psychometrika·2025
Same author

Jointly estimating individual and group networks from fMRI data.

Network neuroscience (Cambridge, Mass.)·2025
Same author

Mean-field theory of the general-spin Ising model.

The European physical journal. B·2025
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Oct 24, 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

6.5K

Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model.

Lourens Waldorp1, Maarten Marsman1

  • 1Faculty of Social and Behavioral Sciences, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Multivariate Behavioral Research
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

Gaussian graphical models (GGMs) analyzing psychological networks are debated. This study proves GGMs estimated from partial correlations do not remove shared variance, contrary to prior concerns, and correctly represent unidimensional latent variable models.

Keywords:
Gaussian graphical modeltype I and II sum of squaresunidimensional latent variable model

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

3.5K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K

Related Experiment Videos

Last Updated: Oct 24, 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

6.5K
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

3.5K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K

Area of Science:

  • Psychological network analysis
  • Statistical modeling
  • Psychometrics

Background:

  • Gaussian graphical models (GGMs) are widely used for psychological network analysis.
  • Concerns exist that GGMs estimated from partial correlations may erroneously remove shared variance.
  • This could invalidate GGM applications, particularly for unidimensional latent variable models (ULVMs).

Purpose of the Study:

  • To address concerns about variance removal in GGMs estimated from partial correlations.
  • To clarify the relationship between ULVMs and GGMs.
  • To demonstrate that GGMs accurately represent networks derived from ULVMs.

Main Methods:

  • Establishing a theoretical connection between ULVMs and GGMs.
  • Utilizing this connection to mathematically prove network structure.
  • Leveraging the relationship between GGMs and linear regression.

Main Results:

  • A fully-connected network is associated with data from a ULVM, not an empty network.
  • Partial correlations, as used in GGMs, do not remove common variance.
  • The findings refute the claim that GGMs wrongfully discard shared variance.

Conclusions:

  • GGMs estimated from partial correlations accurately capture network structures, including those from ULVMs.
  • The concern that GGMs remove essential shared variance is unfounded.
  • GGMs remain a valid and powerful tool for psychological network analysis.