Correspondence Analysis Overview

Correspondence Analysis Overview

Correspondence Analysis (CA) is a multivariate statistical technique used to analyze categorical data presented in contingency tables. It helps visualize relationships between rows and columns by transforming the data into a low-dimensional space, making patterns and associations easier to interpret.

Key Objectives of Correspondence Analysis:

  • Identify associations between categorical variables.
  • Represent data graphically in a two-dimensional or three-dimensional space.
  • Reduce dimensionality while retaining key relationships in the dataset.
  • Provide insights for market research, survey analysis, and social sciences.

How It Works:

  1. Data Preparation: A contingency table is created with row and column categories.
  2. Standardization: The data is converted into relative frequencies.
  3. Singular Value Decomposition (SVD): The technique decomposes the table into principal components.
  4. Dimension Reduction: The data is projected onto a lower-dimensional space.
  5. Visualization: A biplot or scatter plot is used to interpret relationships between row and column variables.

Types of Correspondence Analysis:

  • Simple Correspondence Analysis (SCA): Analyzes a single two-way contingency table.
  • Multiple Correspondence Analysis (MCA): Extends SCA to more than two categorical variables.

Applications of Correspondence Analysis:

  • Market Research: Understanding brand positioning based on consumer preferences.
  • Survey Analysis: Identifying patterns in responses.
  • Social Sciences: Exploring relationships between demographic and behavioral data.
  • Political Science: Examining voting behavior based on different factors.

Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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