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

3.4K
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...
3.4K
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
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.9K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.9K
Regression Analysis01:11

Regression Analysis

8.4K
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:
8.4K
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
Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.0K

You might also read

Related Articles

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

Sort by
Same author

"Robust" but Tiny: Methodological Influences and Inter-Individual (Un)Stability of the Serial Order Effect in Creativity.

Journal of Intelligence·2026
Same author

A systematic review and meta-analysis of interventions addressing sexual and gender minority stress.

Clinical psychology review·2026
Same author

Risk of Bias in Experiments, Quasi-Experiments and Natural Experiments Across Disciplines: Discussion Paper and Assessment Framework.

Campbell systematic reviews·2026
Same author

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes.

Multivariate behavioral research·2026
Same author

What helps and hinders reproducible research? Researchers' perspectives from a cross-disciplinary interview study.

PloS one·2026
Same author

Meta-regression with categorical moderators and dependent effect sizes: A simulation study.

Research synthesis methods·2026

Related Experiment Video

Updated: Feb 4, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.2K

Concealed correlations meta-analysis: A new method for synthesizing standardized regression coefficients.

Belén Fernández-Castilla1,2, Ariel M Aloe3, Lies Declercq4,5

  • 1Faculty of Psychology and Educational Sciences, KU Leuven, University of Leuven, Kortrijk, Belgium. belen.fernandezcastilla@kuleuven.be.

Behavior Research Methods
|September 26, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces concealed correlations meta-analysis for combining standardized regression coefficients from different models. The new method improves the precision of combined estimates, offering more accurate results in meta-analysis.

Keywords:
Effect sizeMeta-analysisStandardized regression coefficients

More Related Videos

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.2K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.6K

Related Experiment Videos

Last Updated: Feb 4, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.2K
Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.2K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.6K

Area of Science:

  • Statistics
  • Psychometrics
  • Biostatistics

Background:

  • Standardized regression coefficients (SRCs) from diverse regression models are challenging to synthesize in meta-analysis.
  • Directly combining SRCs from models with different covariates is statistically invalid as they represent different parameters.

Purpose of the Study:

  • To propose a novel meta-analysis approach, concealed correlations meta-analysis, for accurately combining SRCs from varied regression models.
  • To enhance the precision of combined focal SRC estimates by leveraging shared information.

Main Methods:

  • A simulation study was conducted to compare the proposed concealed correlations meta-analysis with two existing methods.
  • The alternative methods included separate meta-analyses for similar models and meta-regression with model type as a moderator.

Main Results:

  • The concealed correlations meta-analysis approach demonstrated superior accuracy in estimating combined SRCs.
  • This improved accuracy was observed under both random-effects and fixed-effects meta-analysis models.

Conclusions:

  • The proposed concealed correlations meta-analysis is an effective method for synthesizing SRCs from studies with differing covariate sets.
  • This approach offers a statistically sound and more precise alternative for meta-analytic research involving SRCs.