Related Experiment Video
Updated: Apr 23, 2026
08:13
Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
Published on: May 18, 2020
8.0K
A SAS(®) macro implementation of a multiple comparison post hoc test for a Kruskal-Wallis analysis
Alan C Elliott1, Linda S Hynan
1Department of Clinical Sciences, Division of Biostatistics, UT Southwestern Medical Center, Dallas, TX 75390-8822, United States. alan.elliott@utsouthwestern.edu
Computer Methods and Programs in Biomedicine
|December 15, 2010
Summary
The Kruskal-Wallis (KW) test identifies group differences but lacks specific comparisons. This study introduces a SAS macro for post hoc tests, offering a nonparametric alternative to parametric methods.
Area of Science:
- Statistics
- Biostatistics
- Nonparametric Statistics
Background:
- Standard one-way ANOVA assumes normal data.
- Kruskal-Wallis (KW) test is used for non-normal data.
- KW test lacks specific post hoc pairwise comparisons.
Purpose of the Study:
- To provide a SAS macro for nonparametric multiple comparison tests.
- To implement post hoc tests following significant Kruskal-Wallis results.
- To compare the proposed nonparametric method with parametric tests.
Main Methods:
- Developed a SAS macro for multiple comparison tests.
- Macro based on significant Kruskal-Wallis results from SAS NPAR1WAY.
- Used Monte-Carlo simulation for comparison.
Main Results:
- The SAS macro facilitates post hoc pairwise comparisons after a significant KW test.
- The nonparametric procedure was compared against common parametric multiple comparison tests.
- Simulation results demonstrated the utility of the nonparametric approach.
Conclusions:
- The developed SAS macro provides a valuable tool for nonparametric multiple comparisons.
- This method is suitable for analyzing data from up to 20 groups.
- Offers a valid alternative to parametric post hoc tests when normality is violated.
Related Concept Videos
01:15One-Way ANOVA: Unequal Sample Sizes
5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
01:13Multiple Comparison Tests
3.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.4K
01:19Kruskal-Wallis Test
1.3K
The Kruskal-Wallis test, also known as the Kruskal-Wallis H test, serves as a nonparametric alternative to the one-way ANOVA, offering a solution for analyzing the differences across three or more independent groups based on a single, ordinal-dependent variable. This statistical test is particularly valuable in scenarios where the data does not meet the normal distribution assumption required by its parametric counterparts. Kruskal-Wallis test is designed typically to handle ordinal data or...
1.3K
01:10Bonferroni Test
2.5K
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...
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.5K
01:21Friedman Two-way Analysis of Variance by Ranks
588
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...
588
01:20Comparing the Survival Analysis of Two or More Groups
706
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
706