March 2014

Dear Reader,

There is no doubt that Check all that Apply (CATA) questionnaires are increasing in popularity in consumer research. At Sensory Dimensions, more and more of our clients are using CATA data, and we're not alone – the topic was the subject of a whole workshop at last year’s 10th Pangborn Sensory Science Symposium held in Rio de Janeiro.

So, what are the benefits of CATA, and what are the pitfalls? Read on to find out.

Best wishes,

The Team at Sensory Dimensions

The Benefits of CATA

CATA (which is also known as Tick all that Apply or TATA) offers the researcher a route to recovering the elusive ‘why’ people like or do not like a product. The alternatives so far have been open-ended questions, which often return a lack of consensus and limited insight, or a long series of pre-determined intensity and ‘Just about Right’ scales. Requiring a response, even when not relevant to the respondent, scales can generate noise in the data; and the long questionnaire makes for tired and bored consumers.

CATA provides a compromise – respondents are presented with a pre-determined list of terms but only have to check, or tick, those that they consider applicable to the test product. No measure of intensity is required, and they can ignore terms they consider irrelevant. Furthermore, by offering a list of terms, CATA also helps in those situations where consumers find it hard to encapsulate their perception.

Terms used in CATA lists can be purely sensory – such as sweet, bitter, crunchy; or emotional – fresh, energising; or functional – good for breakfast. With sensory terms there is also the possibility of exploring the ideal, such as too sweet or not sour enough. The technique has the advantage that it is suitable for any product, is quick and simple for consumers to apply, and the data is amenable to statistical interpretation, even Preference Mapping.

So What are the Pitfalls?

This all sounds fantastic…but what are the pitfalls? At the Pangborn 2013 workshop a few key messages came out. The number one consideration is how you list the terms. Research using eye tracking glasses has shown that respondents read CATA word lists from top left to bottom right, in other words, they follow normal reading behaviour. Attention drops as they read through the list, but changing the order of the words leads to consumers looking for longer and taking longer to choose. Eye tracking has also shown that less time is spent on later samples. So it is essential to change the order of the word lists between samples and between assessors.

Secondly, we need to consider the number of terms used and the way we group them on the questionnaire. Ares and colleagues (2013) found that splitting terms by modality (flavour, texture, appearance) led to use of more terms and to increased discrimination between products. They also found that asking people to check the list whilst eating the product gave better discrimination than post-eating checking. The question of how many terms still needs further research. In conclusion, shorter CATA lists split by modality are better, but you still need to randomise across samples and assessors.

All supplementary questions that we ask in a consumer study have the potential to bias the overall liking response. CATA has the same potential as a series of attribute intensity or JAR scales, but some researchers argue that the potential for bias is lessened with CATA as the technique requires less engagement by the consumer. Certainly Jaegar and Ares (2014) have found no impact of CATA on overall liking response but then again, found no effect of asking other types of question either.

Analysing the Data

So what about analysing CATA data? The simplest way to deal with these data is to present simple frequency plots showing the relative usage rates for each term across samples. Statistical analysis of these frequencies can be carried out using a sign or McNemar’s test, or Cochran’s Q Test for three or more samples. This means that we can use CATA to know if products are different. To understand what makes the products different, correspondence analysis allows us to map products based on CATA data, effectively summarising our frequency plots into a two- or three-dimensional solution. Multi-dimensional scaling will determine correlation between attributes. Penalty analysis is also being used with CATA data to understand the impact of attributes on liking and, by using benchmark products, we can determine which attributes need modifying to improve a product. These techniques are further explained here.

Overall CATA provides an exciting and powerful approach to understanding consumer response to products and, where appropriate, is worthy of serious consideration as part of a well-designed consumer study.

References

Ares, G., Jaegar, S.R., Bava, C.M., Chheang, S. L., Jin, D., Gimenez, A., Vidal, L., Fiszman, S.M. and Varela, P. (2013) Food Quality & Preference Volume 30, Pages 114-127

Jaegar, S. R. and Ares, G. (2014) Food Quality & Preference Volume 35, Pages 1-5, In press

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