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 of Experimental Data01:23

Correlation of Experimental Data

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, and...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Correlation and Regression00:53

Correlation and Regression

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 negative...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Multiple Regression01:25

Multiple Regression

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...

You might also read

Related Articles

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

Sort by
Same author

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same author

Genome evolution through polyploidy: Enhancing plant stress resilience in agriculture.

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

Comparative Oligo-FISH Mapping Illuminates Chromosomal Evolution Among Rutaceae Species Diverged Over 50 Million Years.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Chromatin accessibility landscape and its association with heterosis in maize hybrids.

Nature communications·2026
Same author

Super-enhancer-mediated transcriptional regulation of gene clusters in plants.

Current opinion in plant biology·2026
Same author

Fine Motor Development Growth Curves for Down Syndrome.

Journal of child neurology·2025

Related Experiment Video

Updated: May 28, 2026

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

Simple estimation of hidden correlation in repeated measures.

Thuan Nguyen1, Jiming Jiang

  • 1Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR, USA. nguythua@ohsu.edu

Statistics in Medicine
|October 15, 2011
PubMed
Summary
This summary is machine-generated.

Researchers developed a simple method to estimate hidden correlation between unobservable traits using repeated measures. This new approach is computationally efficient and performs similarly to complex statistical models.

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

Related Experiment Videos

Last Updated: May 28, 2026

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

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

Area of Science:

  • Statistical modeling
  • Biostatistics
  • Psychometrics

Background:

  • Assessing correlations between unobservable characteristics is crucial in medical and social studies.
  • Repeated measures are often available, but direct correlation differs from the true underlying trait correlation due to measurement errors.
  • Existing methods rely on strong assumptions about measurement errors or complex statistical models like mixed-effects models.

Purpose of the Study:

  • To propose a simple, computationally efficient estimator for the hidden correlation between unobservable characteristics.
  • To provide an estimator with a closed-form expression, unlike existing complex models.
  • To validate the proposed estimator's performance against established methods.

Main Methods:

  • Developed a novel estimator for hidden correlation, analogous to the Pearson correlation coefficient.
  • Utilized simulation studies to compare the proposed estimator with restricted maximum likelihood (REML) estimators in mixed models.
  • Included a comparative analysis with the standard Pearson correlation.

Main Results:

  • The proposed simple estimator demonstrated performance comparable to the REML estimator.
  • The new estimator is significantly more computationally efficient than REML.
  • Simulation results confirmed the effectiveness of the proposed method.

Conclusions:

  • The proposed simple estimator offers an effective and efficient alternative for assessing hidden correlations.
  • This method relaxes the stringent assumptions required by traditional mixed-effects models.
  • The estimator provides a practical tool for analyzing complex data with unobservable traits.