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

McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

You might also read

Related Articles

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

Sort by
Same author

Statistical models for Alzheimer's disease clinical trials: Lessons learned from the DIAN-TU Platform Trial.

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

Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates.

Statistical methods in medical research·2026
Same author

Impact of male genital tract infections on semen quality: a systematic review and meta-analysis.

Fertility and sterility·2026
Same author

Testing disease progression under the proportional reduction in decline in Alzheimer's disease studies.

Journal of applied statistics·2026
Same author

Likelihood ratio test for the disease progression model to measure saved time in Alzheimer's disease.

Statistical methods in medical research·2026
Same author

Assessing safety and efficacy in subpopulations in Alzheimer's disease clinical trials: contextualizing representativeness.

Alzheimer's & dementia (New York, N. Y.)·2025
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

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

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Unconditional tests for comparing two ordered multinomials.

Guogen Shan1, Changxing Ma2

  • 1Department of Environmental and Occupational Health, Epidemiology and Biostatistics Program, University of Nevada Las Vegas, Las Vegas, NV 89154, USA guogen.shan@unlv.edu.

Statistical Methods in Medical Research
|June 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces exact unconditional procedures for analyzing ordered categorical data in multinomial comparisons. The recommended method offers greater statistical power for detecting differences in ordered data.

Keywords:
Exact testsE + M p-valuenuisance parameterstwo ordered multinomialsunconditional test

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: May 21, 2026

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Ordered categorical data analysis is crucial in many fields, including clinical trials.
  • Comparing two multinomial distributions with ordered categories presents unique statistical challenges.
  • Existing methods may lack sufficient power or rely on asymptotic assumptions.

Purpose of the Study:

  • To introduce and evaluate exact unconditional procedures for testing differences between two ordered multinomial distributions.
  • To compare the performance of these new procedures against established methods like the Wilcoxon mid-rank test and proportional odds model.
  • To identify the most powerful and reliable procedure for practical application.

Main Methods:

  • Development of two exact unconditional procedures for ordered multinomial data.
  • Comparison with existing methods using a real-world arthritis pain study dataset.
  • Extensive numerical simulations to assess type I error rates and statistical power.
  • Evaluation under an unconditional statistical framework.

Main Results:

  • The exact unconditional procedure based on estimation followed by maximization demonstrated superior statistical power.
  • Type I error rates were controlled effectively across the evaluated procedures.
  • The proposed methods provide accurate results without relying on large sample approximations.

Conclusions:

  • Exact unconditional procedures offer a robust framework for analyzing ordered categorical data.
  • The estimation-maximization based exact unconditional procedure is recommended for its high power and reliability.
  • These findings enhance the toolkit for statistical analysis in comparative studies with ordered outcomes.