Correspondence Analysis.

This is the ultimate guide for correspondence analysis, visualization, and interpretation. Everything you need to know is here: from basic how tos to tips and tricks that will aid in interpretation to advanced concepts. Worked examples are shown in Displayr, R and Q.

Correspondence analysis is a popular data science technique. It turns large tables of data into relatively easy to read visualizations. Which makes it easier to find key insights in the data. Correspondence analysis is also commonly used by market researchers to create brand switching and positioning maps. Scatter plots are the most common way to visualize results. However, moon plots have the same conclusions, are often even easier to interpret.

Focusing the Results of Correspondence Analysis in Displayr
29 October 2018 | by Jake Hoare
Correspondence analysis is often used to visualize a table of data. The goal is to represent as much information as possible, as accurately as possible. However,...

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3D Correspondence Analysis Plots in Q
27 September 2018 | by Tim Bock
The default correspondence analysis charts in Q are two-dimensional scatterplots (scroll down to see an example). However, you can create a three-dimensional plot by writing...

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3d correspondence analysis
3D Correspondence Analysis Plots in Displayr
14 September 2018 | by Tim Bock
In this post I show you to create 3D visualizations of correspondence analysis using Displayr. This allows you to view an extra dimension of your...

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Adding Supplementary Points to a Correspondence Analysis
17 August 2017 | by Jake Hoare

Retrospectively adding supplementary points to a correspondence analysis can greatly assist in the interpretation of results. In other words, including supplementary row or column points

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Moonplot to show brand mapping from a correspondence analysis
Moonplots: A Better Visualization for Brand Maps
15 August 2017 | by Tim Bock
Moonplots are a better way to visualize brand maps than standard correspondence analysis outputs, which are often difficult to read correctly. The Moonplot resolves the...

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Scaled world map showing longitude and latitude
Normalization and Scaling in Correspondence Analysis
08 August 2017 | by Tim Bock

This post gives recommendations for the best approach to normalization for different situations, making correspondence plots less misleading.

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correspondence analysis example
Understanding the Math of Correspondence Analysis
08 August 2017 | by Tim Bock
If you've ever wanted a deeper understanding of what's going on behind the scenes of correspondence analysis, then this post is for you. Correspondence analysis...

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Correspondence Analysis for brand switching
Correspondence Analysis of Square Tables
25 July 2017 | by Jake Hoare

Square tables are data tables where the rows and columns have the same labels, commonly seen as a crosstab of brand switching or brand repertoire data.

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Using Displayr
Customization of Bubble Charts for Correspondence Analysis in Displayr
08 July 2017 | by Tim Bock

When you insert a bubble chart in Displayr (Insert > Visualization > Bubbleplot), you can customize some aspects of its appearance from the controls that appear in the object

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Using Q
Customization of Bubble Charts for Correspondence Analysis in Q
08 July 2017 | by Tim Bock

When you insert a bubble chart in Q (Create > Charts > Visualization > Labeled Bubbleplot), you can customize some aspects of its appearance from the

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correspondence analysis
Using Bubble Charts to Show Significant Relationships and Residuals in Correspondence Analysis
08 July 2017 | by Tim Bock

While correspondence analysis does a great job at highlighting relationships in large tables, a practical problem is that correspondence analysis only shows the strongest relationships, and sometimes

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correspondence-analysis-sales
When to Use, and Not Use, Correspondence Analysis
23 May 2017 | by Tim Bock

Correspondence analysis is one of those rare data science tools which make things simpler. You start with a big table that is too hard to

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correspondence analysis movements
Using Correspondence Analysis to Compare Sub-Groups and Understand Trends
22 May 2017 | by Tim Bock

This post shows how to use correspondence analysis to compare sub-groups. It focuses on one of the most interesting types of sub-groups: data at different points

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Multiple Correspondence Analysis
Correspondence Analysis Versus Multiple Correspondence Analysis: Which to Use and When?
22 May 2017 | by Tim Bock
Let me cut to the chase. Multiple correspondence analysis sounds better than correspondence analysis. But, for 99% of real-world data problems, correspondence analysis is the...

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How Correspondence Analysis Works (A Simple Explanation)
19 May 2017 | by Tim Bock

Correspondence analysis is a data science tool for summarizing tables. This post explains the basics of how it works. It focuses on how to understand the underlying

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correspondence analysis movements
How to Interpret Correspondence Analysis Plots (It Probably Isn’t the Way You Think)
19 May 2017 | by Tim Bock

Correspondence analysis is a popular data science technique. It takes a large table, and turns it into a seemingly easy-to-read visualization. Unfortunately, it is not quite

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Easily Add Logos to a Correspondence Analysis Map in Q
17 May 2017 | by Tim Bock

You can take your correspondence analysis plots to the next level by including images. Better still, you don’t need to paste in the images after

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correspondence analysis
Easily Add Images to a Correspondence Analysis Map in Displayr
17 May 2017 | by Tim Bock

You can take your correspondence analysis plots to the next level by including images. Better still, you don’t need to paste in the images after

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