Also known as Tick all that Apply (TATA), CATA offers a simple way for us to investigate ‘why' people like or do not like a product. The alternatives are open-ended questions – laborious to code and often returning lack of consensus and limited insight- or a long series of pre-determined intensity and Just about Right scales . The disadvantage of long lists of scales is that each scale requires a response whether the attribute in question is of relevance to the respondent or not. This creates noise in the data and long questionnaires make for tired and bored consumers. CATA serves to provide a compromise here – 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 if a term is not relevant, it can be ignored. The provision of a list of words can be helpful to those respondents who find it difficult to verbalise their perception.
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Yes, I’ve run into the same issue. My first choice is to place the labels on top of the tiny dots (so there aren’t actually dots showing at all, just the numbers or percentages — very sleek!). But some datasets have values that fall so close together, and then everything gets smushed, so I’ll arrange the labels to the left or right. In later editing stages of the document, I make sure all the dots plots have the exact same format, ., you wouldn’t have one dot plot with labels on top of the dots and another with labels to the left and right.