Correspondence Analysis – what does it all mean?

One of the most commonly used forms of Brand mapping is correspondence analysis. Brand maps are often used to illustrate customers’ images of the market by placing products and attributes together on a map together on a graph. This allows close interpretation of company perceptions with a variety of product and service attributes that are closest to them on the map. If products are placed close to each other on the graph, it means they have a similar image or profile in the market. This helps to inform strategy, like where to take the client’s product in the future.

Some questions that can be answered with a correspondence analysis are:

- What attributes does the brand own?
- What attributes do competitors own?
- Are there gaps in the market that may be filled by the client brand?
- How should the brand be positioned to be both relevant to the market and differentiated from the competition?

Correspondence analysis is an exploratory data analytic technique that allows rows and columns of a cross-tabulation (two-way and multi-way) to be displayed as points in two-dimensional space. The information from the cross-tab, as seen in the example below, is usually collected using simple multi-coded grid questions or semantic rating scales (typically 5 or 10 point scales).

It reduces a complicated set of data to a graphical display that is immediately and easily interpretable. The graph contains a point for each row and each column of the cross-tab. Rows with similar patterns of counts produce points that are close together, and columns with similar patterns of counts also produce points close together.

Running a correspondence analysis on the above cross-tab yields the following graphical display:

In this example, McDonald’s is more synonymous with Low Prices. Pizza Hut and El Pollo Loco can be associated with Overall satisfaction, as well as Taste of the Food and Quality of Food for the Money.

Correspondence analysis is a valuable tool that can be applied to many situations.

Visualization techniques – graphic visualization vs. presentation

You’ve just completed fielding your latest survey research project, now you need to analyze the data and communicate the results.  As a complement to cross tabulation and statistical analysis of the survey data, you’ll likely incorporate graphics within your analysis and reporting process.

In fact, you should consider incorporating graphics in two different ways; using graphics as a visualization tool to assist with your analysis, and graphics as a presentation tool to clearly communicate the results of your analysis.

These two separate functions or “graphical roles” require different types of graphics as well as different methods of viewing or analyzing the graphics.

Graphic of my LinkedIn network – useful as a visualization tool, but not as a presentation tool.

Visualization graphics typically incorporate a relatively large number of data points, enabling the analyst to view respondent segments, trends or outliers in a manner that may not be obvious from the viewpoint of tabular reports.

Presentation on the other hand, embodies the the art of communication, and presentation graphics are therefore designed to quickly and persuasively depict a key point or conclusion from the analysis of the survey results

Visualization graphics are typically “noisy”, containing lots of information and capturing several dimensions including multiple axes, quadrants, or variations of text or data point sizes.  Visualization graphics typically require careful study and consideration to identify their underlying meaning.

On the other hand, presentation graphics need to immediately convey the point that the graphic is making.  They are purposefully succinct and focused, and as such presentation graphics typically avoid multiple dimensions.  Good presentation graphics preclude the need for supporting explanatory text to convey their message.

The experience analyst will be thinking of presentation graphics at every step of the data analysis process, including while utilizing visualization graphics as an analysis tool.  The point of the analyst’s effort is to ultimately communicate the results of the analysis to busy decision makers in a manner they can readily comprehend.

Stay tuned to this blog as we explore new and interesting ways to use graphics to both analyze and present the results of survey data.

If you’d like to learn more about how to prepare survey data for analysis, please download our whitepaper  “10 essential prerequisites for survey data analysis”.