Projective techniques seek to get to the subconscious. They work as follows:
- Participants are asked to project their feelings and thoughts onto other things. For example: If Coca-Cola was an animal, which animal would it be?. Common projective techniques used include:
- Participants are then asked to explain their answers. This 'why' question is the important part of using projective techniques, as the projective techniques are designed to release the sub-conscious though rather than to be, in themselves, revealing. Probing is used to try and uncover the real explanations. For example, if a Coca-Cola was seen as a cow, the explanation may be that the respondent sees it as fat, slow moving and uninspiring.
Projective techniques are fun. They are widely used, with clients, respondents and researchers all finding them a welcome change from the humdrum of traditional market research questions. However, considerable care needs to be used when asking such questions. As with measuring abstract things, our inability to phrase a perfect question causes confusion between truth and error. Measuring the subconscious is obviously much more difficult than measuring something abstract, as the problem of abstraction is that it is hard to find the right words, whereas the problem with the subconscious is that people do not even think about it.
Projective techniques were developed in psychology. Concerns about their reliability have caused them to be, by and large, the provenance of 'alternative' psychology practitioners. In market research they remain mainstream, as other techniques often fail to generate any insight at all into brand buying. Of course, being perceived as useful does not solve the problem of validity. Three things are done to prevent spurious conclusions being reached using projective techniques:
- Probing is used to confirm any interpretations.
- Multiple techniques are used and results are concluded to be reliable when the same conclusions are drawn from multiple techniques (which is equivalent to the approach to abstract things, where multiple questions are asked).
- Combining the data of multiple people (although almost all qualitative research makes this assumption it is, somewhat inexplicably, rarely mentioned). If the same responses are obtained from lots of people, then it follows that the responses likely contain a kernel of truth. From a theoretical perspective, combining the answers of multiple people to one question is equivalent to getting one person to answer multiple questions if it is assumed that the differences between the people are random and the differences between how a person answers multiple questions are also random.