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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

23.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
23.6K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

762
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
762
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

185
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...
185
Coefficient of Correlation01:12

Coefficient of Correlation

6.1K
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...
6.1K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
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:
5.9K
Ranks01:02

Ranks

236
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
236

You might also read

Related Articles

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

Sort by
Same author

Characteristics and outcomes of secondary hematological malignancies following autologous stem cell transplantation for multiple myeloma.

Blood cancer journal·2026
Same author

Prevalence of Unmet Service Needs and Associated Person-Reported Outcomes in Long-Term Services and Supports in the United States.

Journal of the American Geriatrics Society·2026
Same author

Telehealth Use Among Older Adults Receiving Home- and Community-Based Services: Cross-Sectional Analysis Using the National Core Indicators-Aging and Disabilities Survey.

JMIR human factors·2026
Same author

Combined occurrence of cardiovascular and bone events in individuals with kidney stone disease.

Clinical kidney journal·2026
Same author

Plasma Inflammatory Biomarkers and Risk of Incident Tinnitus: A Longitudinal Study.

Ear and hearing·2026
Same author

Baseline Urinary Calcium and the Efficacy of Thiazide Diuretics for Kidney Stone Prevention.

The Journal of urology·2026
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Estimating intracluster correlation for ordinal data.

Benjamin W Langworthy1,2, Zhaoxun Hou1, Gary C Curhan2,3,4

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Journal of Applied Statistics
|June 12, 2024
PubMed
Summary
This summary is machine-generated.

Estimating intracluster correlation for ordinal hearing data is crucial for reliability. Using cumulative logistic or probit models, unlike linear models, reduces bias in these important test/retest reliability estimates.

Keywords:
Test/retest reliabilityintracluster correlationordinal datapure-tone audiometryreliability and validity

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Related Experiment Videos

Last Updated: Jun 24, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Audiology

Background:

  • Intracluster correlation (ICC) is vital for assessing reliability in clustered or repeated measures data.
  • Ordinal data, common in audiology (e.g., hearing thresholds), presents unique challenges for ICC estimation.
  • Existing methods often assume continuous data, potentially biasing ICC estimates for ordinal outcomes.

Purpose of the Study:

  • To evaluate methods for estimating intracluster correlation (ICC) specifically for ordinal hearing threshold data.
  • To compare the performance of mixed effects cumulative logistic/probit models against mixed effects linear models for ICC estimation with ordinal data.
  • To assess the test-retest reliability of iPhone-based hearing assessment applications using appropriate statistical models.

Main Methods:

  • Development and application of mixed effects cumulative logistic and probit models for ordinal outcome data.
  • Simulation studies to compare bias and performance of different ICC estimation methods.
  • Estimation of ICC for hearing thresholds from iPhone-based applications.

Main Results:

  • Mixed effects linear models, assuming continuous data, exhibit negative finite sample bias when applied to ordinal data.
  • Mixed effects cumulative logistic and probit models significantly reduce this bias for ordinal ICC estimation.
  • ICC estimates for iPhone hearing tests were higher using cumulative logistic/probit models compared to linear models, indicating improved reliability assessment.

Conclusions:

  • For ordinal data, particularly in audiology, mixed effects cumulative logistic or probit models are superior to linear models for estimating intracluster correlation.
  • These ordinal models provide less biased and potentially more accurate measures of test-retest reliability for applications like iPhone hearing assessments.
  • The findings advocate for the use of appropriate statistical models tailored to the data's distributional properties for reliable scientific conclusions.