## Correspondence Analysis.

This is the ultimate resource for correspondence analysis, visualization, and interpretation. So everything you need to know is here. From basic how-tos to tips and tricks to advanced concepts. With worked examples shown in Displayr, R, and Q.

Correspondence analysis is a data science technique. It turns large tables of data into easier to read visualizations. This, in turn, makes it easier to find key insights in data. Correspondence analysis is often used by market researchers. Who then create brand switching and positioning maps. Finally, scatter plots are one way to visualize results, while moon plots are easier to interpret.

Supplementary points of data can be added to a "core" correspondence analysis to aid interpretation without influencing the placement of core data.

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Correspondence analysis outputs are difficult to read correctly. Moonplots are a better alternative to brand maps because they are much easier to interpret.

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

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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. Examples are in R.

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In this post I discuss the special case of Correspondence Analysis with square tables. Such tables often arise in the context of brand switching.

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Advanced Customization of Bubble Charts for Correspondence Analysis in Displayr for when weak relationships are interesting.

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Correspondence Analysis in Q

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

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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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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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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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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 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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You can take your correspondence analysis plots to the next level by including images. Here is a guide to creating them in Q.

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You can take your correspondence analysis plots to the next level by including images. Better still, you can include them from the start of your analysis.

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You can take your correspondence analysis plots to the next level by including images. This post describes how to create this plot using R.

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