This paper is a call to action to think about bias in a different way - by going back to basics. It is an outline about how to develop greater empathy from the beginning of a project to the end. Far from a touchy-feely or soft luxury, empathy is a critical tool that must be carefully cultivated in order to provide the understanding and explanation of data that yields truly meaningful and effective consumer insight and thereby helps overcome bias and stereotyping.
This paper looks at the various sources of bias, discusses how to reduce them or, at least, how to measure them.
A special study consisting of a survey among 326 housewives, has shown that the friendliness effect in product tests is minimised if the method below is used: The interviewer shows three packs with the same contents, but with different code numbers/letters. The housewife is given the suggestion that two of the packs contain a now product and the third pack is the product she is always using, without identifying the packs. The housewife herself selects the pack she will test, presumably under the impression that she has a chance of one third to get the product she is always using. A control group, where all packs contain the product she is always using, interviewed with the same questionnaire, provides the level of the bias which always remains. This level is the zero level for the results of the product test for the new product. With this system the friendliness-bias can be reduced considerably. In this study the reduction of the bias was about 50%.
The paper deals with two broad areas of bias in the preconceptions of the respondent and the preconceptions of the researcher. In relation to the former the specific areas of bias dealt with are: A. the motivation of the respondent; B. the presentation of product samples; C. the words used to name product qualities. In relation to the latter preconceptions the specific areas of bias dealt with are: A. relationships selected for interpretation by the researcher; B. the questionnaire design. The basic principles in each of these specific areas are discussed and an attempt is made to illustrate how the principles may be translated into operational terms to eliminate these sources of bias, for example: A. Three call method of comparative testing; i. e. placing only one product at a time for a consumer's evaluation; B. Prescriptive scales; i. e. the respondent indicates how he would like the product to be rather than describing the way he thinks it is; C. Hierarchical statistical analyses; i. e. analyses which are structured in a manner corresponding to the hypotheses and questionnaire.
This paper discusses the following main points: 1. Either for combining findings from different countries, or for comparing them it is essential to have truly comparable data. 2. The use of the same methods in different countries does not by itself ensure comparable data because unknown biases may interact differently with different local factors, thus creating differential biases and non-comparable findings. 3. The only way to ensure comparability is to obtain valid data, or data where there are known biases, which can be corrected or allowed for. 4. This known degree of validity can best be attained by using in each country that method which is best and best tested there, first as a valid method, and secondly as a reliable method. 5. The principle of deciding which method to use in each country simply on the merits of the local situation, also leads to considerable cost advantages. 6. The use of different methods in the different countries of a multi-country survey also provides valuable opportunities for checking that one is not simply obtaining consistent wrong answers.