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

Coefficient of Correlation01:12

Coefficient of Correlation

9.2K
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...
9.2K
Two-Way ANOVA01:17

Two-Way ANOVA

3.7K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.7K
Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Contingency Table01:29

Contingency Table

5.0K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
5.0K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.5K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.5K
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:
8.5K

You might also read

Related Articles

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

Sort by
Same author

Dysfunctional large-scale networks linking PTSD and cognitive impairment.

Journal of psychiatric research·2026
Same author

Relationship between new-onset falls and neuroimaging markers of Alzheimer's disease in the UK Biobank.

Journal of Alzheimer's disease : JAD·2026
Same author

Generation and Single-Cell Transcriptomic Analysis of Hepatocellular Carcinoma Organoids following Drug Treatment.

Journal of visualized experiments : JoVE·2026
Same author

[Research progress in vagus nerve stimulation for the intervention of osteoarthritis].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2026
Same author

Evaluation for Potential Inequities in Efficacy and Patient Outcomes Following Implementation of a STAT Outpatient Neuroimaging Pathway for Evaluation of Patients with Optic Disc Swelling.

Neuro-ophthalmology (Aeolus Press)·2026
Same author

Disproportionately elevated sulcal index (DESI): An automatically driven index representing disproportionate subarachnoid space enlargement in brain MRI scans.

Brain research bulletin·2026
Same journal

Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data.

Computational statistics & data analysis·2026
Same journal

A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models.

Computational statistics & data analysis·2025
Same journal

MIXANDMIX: numerical techniques for the computation of empirical spectral distributions of population mixtures.

Computational statistics & data analysis·2024
Same journal

Locally sparse quantile estimation for a partially functional interaction model.

Computational statistics & data analysis·2024
Same journal

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.

Computational statistics & data analysis·2024
Same journal

GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.

Computational statistics & data analysis·2024
See all related articles

Related Experiment Video

Updated: Apr 6, 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

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models.

Chen Yue1, Shaojie Chen1, Haris I Sair2

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205.

Computational Statistics & Data Analysis
|July 21, 2015
PubMed
Summary
This summary is machine-generated.

Quantifying graphical measurement reproducibility is crucial for scientific data integrity. This study introduces the graphical intra-class correlation coefficient (GICC) to address this, enhancing data reliability.

Keywords:
MCMCEMgraphical intra class correlation coefficientmultivariate probit-linear mixed model

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

863
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Apr 6, 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
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

863
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Biostatistics
  • Scientific Methodology
  • Data Science

Background:

  • Data reproducibility is a significant challenge across scientific disciplines.
  • Quantifying the reproducibility of graphical measurements presents unique difficulties.
  • Existing methods may not adequately capture the nuances of graphical data.

Purpose of the Study:

  • To propose a novel statistical method for quantifying the reproducibility of graphical measurements.
  • To generalize the image intra-class correlation coefficient (I2C2) for broader application.
  • To introduce the graphical intra-class correlation coefficient (GICC) as a reliable metric.

Main Methods:

  • Development of the graphical intra-class correlation coefficient (GICC) based on multivariate probit-linear mixed effect models.
  • Implementation of a Markov Chain Monte Carlo EM (mcm-cEM) algorithm for GICC estimation.
  • Validation through simulation studies with diverse parameter settings.

Main Results:

  • The proposed GICC provides a robust measure for assessing graphical measurement reproducibility.
  • Simulation results demonstrate the method's efficacy across various conditions.
  • Successful application of the GICC to the KIRBY21 test-retest dataset.

Conclusions:

  • The GICC offers a valuable tool for enhancing data reproducibility in scientific research involving graphical data.
  • The mcm-cEM algorithm provides an effective means for estimating GICC.
  • This work contributes to improving the rigor and reliability of scientific findings.