Correspondence analysis frequently asked questions

Correspondence analysis frequently asked questions

What is Correspondence Analysis?
Correspondence Analysis is a statistical technique used to analyze categorical data by identifying relationships between rows and columns in a contingency table. It helps visualize these associations in a low-dimensional space.
What is Sigma Magic, and how does it help in Correspondence Analysis?
Sigma Magic is an analysis software that provides various statistical tools, including Correspondence Analysis, to process categorical data, generate visual plots, and interpret relationships between variables effectively.
What are the key applications of Correspondence Analysis?

CA is used in market research, consumer preference analysis, survey data interpretation, social sciences, and political studies to examine relationships between categorical variables.

What type of data is required for Correspondence Analysis in Sigma Magic?
CA in Sigma Magic requires categorical data in the form of a contingency table (cross-tabulation of variables).
What are eigenvalues in Correspondence Analysis?
Eigenvalues represent the amount of variance explained by each dimension. Higher eigenvalues indicate that the dimension captures more of the relationships between the categories.
How do I interpret the dimensions in the CA output?
The first dimension (highest eigenvalue) captures the most variance, while the second and subsequent dimensions capture less. The position of points in the correspondence plot shows how strongly they are related.
What is inertia in Correspondence Analysis?

Inertia is a measure of the explained variance. It quantifies how much of the total variance in the data is captured by each dimension.

How do I perform Correspondence Analysis in Sigma Magic?

You can navigate to the CA module, input the contingency table, choose the number of dimensions, and run the analysis. Sigma Magic then generates the correspondence plot and summary statistics.

How do I interpret a Correspondence Plot in Sigma Magic?
  • Categories close to each other are similar.
  • Opposite categories on a dimension indicate contrast.
  • Categories far from the origin contribute more to the variance.
    • What are the main assumptions of Correspondence Analysis?
  • The data must be categorical.
  • The contingency table should have non-negative values.
  • The chi-square assumption of independence applies.
    • How do I compare multiple CA models in Sigma Magic?
    You can run separate CA analyses on different datasets and compare the plots and eigenvalues to understand variations in relationships.
     
    ​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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