Ordination

TL;DR

• A set of statistical techniques for visualizing and analyzing relationships among variables in a dataset.
• Common methods include principal components analysis (PCA), multi-dimensional scaling (MDS), and correspondence analysis (CA).
• Frequently applied in ecology, sociology, and psychology to identify patterns and important variables.

Definition

Ordination is a statistical method used in data science to visualize and analyze the relationships among variables in a dataset. It is often used in the fields of ecology, sociology, and psychology to identify patterns and trends in data. There are several different techniques that can be used for ordination, including principal components analysis (PCA), multi-dimensional scaling (MDS), and correspondence analysis (CA).

Explanation

Ordination techniques transform complex, multivariable data into formats that make patterns and relationships easier to see and interpret. Different ordination methods achieve this in different ways:

• PCA reduces dimensionality by transforming original variables into a new set of uncorrelated variables called principal components, ordered by how much variation they explain.
• MDS represents objects as points in a map or plot so that distances among points reflect similarity or dissimilarity.
• CA is another ordination approach (listed among common techniques) used to explore relationships in categorical or contingency data.

All these methods help identify which variables are most important in determining patterns and provide visual representations that are easier to interpret.

Examples

Principal components analysis (PCA)

This technique is used to reduce the dimensionality of a dataset by transforming the variables into a new set of uncorrelated variables called principal components. These components are ranked in order of importance, with the first principal component explaining the most variation in the data, and subsequent components explaining progressively less. PCA can be used to identify patterns and trends in the data, and to identify the variables that are most important in determining the patterns.

Multi-dimensional scaling (MDS)

This technique is used to visualize the relationships between different objects in a dataset by creating a map or plot in which the objects are represented as points. The distance between the points on the map reflects the similarity or dissimilarity between the objects. MDS can be used to identify patterns and trends in the data, and to identify the variables that are most important in determining the patterns.

Use cases

• Ecology
• Sociology
• Psychology
• A wide range of applications in data science

Related terms

• Principal components analysis (PCA)
• Multi-dimensional scaling (MDS)
• Correspondence analysis (CA)