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

37.5K
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...
37.5K
Ranks01:02

Ranks

579
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...
579
Review and Preview01:10

Review and Preview

8.9K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.9K
Nominal Level of Measurement00:56

Nominal Level of Measurement

42.4K
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. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
42.4K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

578
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...
578
Introduction to z Scores01:05

Introduction to z Scores

1.6K
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Synchronization of the 12-hour circatidal rhythm maintains the kidney through repeated cycles of warm reperfusion in the hibernating ground squirrel.

Function (Oxford, England)·2026
Same author

The Association of the Low-Income Housing Tax Credit Program and Intimate Partner Violence Related Emergency Department Visits.

Journal of family violence·2026
Same author

A Pre-Post Evaluation of the Sexual Communication and Consent Training Program in United States Air Force Basic Military Training, 2019-2020.

Journal of child sexual abuse·2025
Same author

Improving Access to HIV Prevention Services in Community Pharmacies in the US Southeast: Protocol for a Hybrid Type 1 Effectiveness-Implementation Study.

JMIR research protocols·2025
Same author

Intimate Partner Violence in Mid-Adulthood in the United States: Patterns by Sexual Orientation and Sex Assigned at Birth.

Perspectives on sexual and reproductive health·2025
Same author

Associations between IPV and non-communicable diseases: a systematic review.

BMC public health·2025

Related Experiment Video

Updated: Apr 7, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

1.3K

Numeric score-based conditional and overall change-in-status indices for ordered categorical data.

Robert H Lyles1, Lawrence L Kupper2, Huiman X Barnhart3

  • 1Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Mailstop 1518-002-3AA, Atlanta, 30322, GA, U.S.A.

Statistics in Medicine
|July 4, 2015
PubMed
Summary

This study introduces new change indices for ordinal outcomes, useful for assessing symptom severity changes. These indices provide a quantitative measure of change, aiding in the analysis of interventions and natural conditions.

Keywords:
Dirichletchange scoremultinomialordinal data

More Related Videos

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
Author Spotlight: Enhancing Women's Chronic Pelvic Pain Management Through Acupoint Catgut Embedding
02:41

Author Spotlight: Enhancing Women's Chronic Pelvic Pain Management Through Acupoint Catgut Embedding

Published on: May 3, 2024

2.4K

Related Experiment Videos

Last Updated: Apr 7, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

1.3K
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
Author Spotlight: Enhancing Women's Chronic Pelvic Pain Management Through Acupoint Catgut Embedding
02:41

Author Spotlight: Enhancing Women's Chronic Pelvic Pain Management Through Acupoint Catgut Embedding

Published on: May 3, 2024

2.4K

Area of Science:

  • Statistics
  • Biostatistics
  • Ordinal Data Analysis

Background:

  • Assessing changes in ordinal categorical outcomes (e.g., symptom severity) is common in research.
  • Existing methods may not adequately capture the magnitude of change in ordinal data.
  • A need exists for robust indices to quantify changes in ordered categories.

Purpose of the Study:

  • To define novel change indices for ordinal categorical outcomes.
  • To differentiate between conditional and overall change assessments.
  • To develop indices with desirable statistical properties for small samples.

Main Methods:

  • Utilized a multinomial model for analyzing changes within baseline categories.
  • Developed two overall change indices: one for expected population change and one scaled from -1 to +1.
  • Employed a Dirichlet-multinomial model for Bayesian credible intervals of the conditional change index.

Main Results:

  • The proposed conditional change index is informative irrespective of baseline sampling.
  • Overall change indices are relevant for randomly sampled populations or with baseline distribution assumptions.
  • Bayesian credible intervals for the conditional index demonstrated favorable small-sample frequentist properties.

Conclusions:

  • The developed change indices offer a flexible and quantitative approach to analyzing ordinal outcome changes.
  • These methods are applicable to various fields, including sleep deprivation and activities of daily living studies.
  • The Bayesian approach provides reliable estimates, especially in small samples.