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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

370
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...
370
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

5.9K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
5.9K
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.7K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.7K
Variability: Analysis01:11

Variability: Analysis

280
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
280
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

219
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
219
Contingency Table01:29

Contingency Table

3.3K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Molecular Genetic Characterization of the Diet of Limestone and Rainforest Langurs.

Ecology and evolution·2026
Same author

Political Liberation, Hope, and Social Competition Are the Motor of Secular Trends in Height.

American journal of human biology : the official journal of the Human Biology Council·2025
Same author

Avoiding "Too Tall" and "Too Short": The Effect of the Community on the Regulation of Body Height.

American journal of human biology : the official journal of the Human Biology Council·2025
Same author

Stop stunting-A misguided campaign by well-meaning nutritionists.

American journal of human biology : the official journal of the Human Biology Council·2024
Same author

Winner-loser effects improve social network efficiency between competitors with equal resource holding power.

Scientific reports·2023
Same author

A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models.

Psychometrika·2022

Related Experiment Video

Updated: Nov 15, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Chain Reversion for Detecting Associations in Interacting Variables-St. Nicolas House Analysis.

Michael Hermanussen1, Christian Aßmann2,3, Detlef Groth4

  • 1University of Kiel, Aschauhof, 24340 Eckernförde-Altenhof, Germany.

International Journal of Environmental Research and Public Health
|March 6, 2021
PubMed
Summary

We introduce St. Nicolas House Analysis (SNHA), a novel statistical method for identifying variable interactions. SNHA visualizes these interactions as "association chains," offering a robust approach for secondary data analysis.

Keywords:
St. Nicolas House Analysisassociation chainsbivariate correlation coefficientsdata matricesnetwork graphs

More Related Videos

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.1K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.1K

Related Experiment Videos

Last Updated: Nov 15, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.1K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.1K

Area of Science:

  • Statistics
  • Data Analysis
  • Network Science

Background:

  • Existing statistical methods may struggle with complex variable interactions.
  • There is a need for robust methods to detect and visualize variable relationships, especially in secondary data analysis.

Purpose of the Study:

  • To present a new statistical approach, St. Nicolas House Analysis (SNHA), for detecting and visualizing extensive interactions among variables.
  • To introduce the concept of "association chains" for characterizing dependence structures.

Main Methods:

  • Ranking absolute bivariate correlation coefficients by magnitude.
  • Creating hierarchic "association chains" based on sequence ordering.
  • Visualizing association chains and overlapping chains as network graphs.

Main Results:

  • SNHA effectively depicts association chains in both highly and weakly correlated data.
  • The method demonstrates robustness against spurious associations.
  • SNHA detects fewer associations between independent variables compared to standard methods.

Conclusions:

  • Reversible association chains offer a principle for detecting variable dependencies.
  • SNHA is a non-parametric statistical method suitable for secondary data analysis using correlation matrices.
  • The method provides an initial approach for clarifying potential associations for subsequent hypothesis testing.