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

Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.3K
Coefficient of Correlation01:12

Coefficient of Correlation

8.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.7K
Correlation of Experimental Data01:23

Correlation of Experimental Data

493
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
493
Correlation and Regression00:53

Correlation and Regression

3.5K
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...
3.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

514
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
514
Regression Analysis01:11

Regression Analysis

8.5K
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:
8.5K

You might also read

Related Articles

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

Sort by
Same author

Differential utility of immediate versus delayed memory measures for the identification of episodic memory impairment: Systematic review and meta-analysis.

Psychological assessment·2026
Same author

Evaluating differences in latent means across studies: Extending meta-analytic confirmatory factor analysis with the analysis of means.

Research synthesis methods·2026
Same author

Can we include dichotomous variables in meta-analytic structural equation modeling? Mind the prevalence.

Behavior research methods·2026
Same author

Six ways to handle dependent effect sizes in meta-analytic structural equation modeling: Is there a gold standard?

Research synthesis methods·2026
Same author

A Meta-Analysis of Social and Contextual Correlates of Migrant Adaptation to Living in Receiving Societies.

Nature communications·2025
Same author

Corrigendum to 'Investigating the mediating effect of myokines on exercise-induced cognitive changes in older adults: A living systematic review and meta-analysis' [Neurosci. Biobehav. Rev., vol. 178, (November 2025) 106381].

Neuroscience and biobehavioral reviews·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: Feb 17, 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

3.8K

Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM.

Suzanne Jak1, Mike W-L Cheung2

  • 1a University of Amsterdam.

Multivariate Behavioral Research
|December 9, 2017
PubMed
Summary
This summary is machine-generated.

Meta-analytic structural equation modeling (MASEM) advances theory by synthesizing findings. This study improves MASEM for missing correlations, finding multivariate methods outperform univariate ones.

Keywords:
Meta-analytic structural equation modelingTSSEMmeta-analysismissing data

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

3.3K

Related Experiment Videos

Last Updated: Feb 17, 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

3.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

3.3K

Area of Science:

  • Psychology
  • Social Sciences
  • Quantitative Methods

Background:

  • Meta-analytic structural equation modeling (MASEM) synthesizes research findings to advance theories.
  • MASEM involves pooling correlation matrices and fitting structural models.
  • Missing correlation coefficients in primary studies pose a challenge for MASEM.

Purpose of the Study:

  • To modify and evaluate an optimal MASEM method for handling missing correlation coefficients.
  • To compare the performance of the modified MASEM method against existing approaches.
  • To assess the impact of varying levels of missing correlations on fixed-effects MASEM methods.

Main Methods:

  • Modification of the optimal MASEM method to accommodate missing correlation data.
  • Comparison of the modified method with existing univariate and multivariate techniques.
  • Evaluation of method performance under different degrees of missing correlation coefficients.

Main Results:

  • Univariate methods for handling missing correlations in MASEM demonstrated poor performance.
  • Multivariate methods generally performed well in synthesizing data with missing correlations.
  • The modified MASEM approach effectively addressed missing correlation coefficients.

Conclusions:

  • The proposed modification enhances MASEM's utility when dealing with incomplete correlation data.
  • Multivariate approaches are recommended over univariate methods for MASEM with missing correlations.
  • Further research should explore advanced methods for robust MASEM in the presence of missing data.