Technical Report: How to Set Identicality Norms for No Preference Data

Expectations, or norms, are of crucial importance in science as they allow researchers to interpret results within a larger theoretical framework. For example, in 1866 Gregor Mendel published his now famous Mendelian ratio, which provided expected results that were instrumental in the early development of genetics. The importance of norms is just as great in product research where norms are essential for meaningful data interpretation. In particular, without an expectation regarding the level of no preference one has no metric with which to gauge the meaningfulness of no preference responses. For example, the ASTM advertising claims guide currently recommends that unqualified advertising claims should not be made if more than 20% of the responses are no preference. While this recommendation correctly recognizes the importance of having an expected level of no preference responses, one could argue that the figure of 20% has been chosen somewhat arbitrarily. A greater level of awareness as to the meaning of no preference responses is required, and in this report we will discuss methods to obtain such an improved level of awareness before paired testing tools are applied. Specifically, we discuss the establishment of a metric that we call an identicality norm that is based on the expected level of no preference votes for identical products and we show how such a norm can be established from historical data through the use of Thurstonian tools.

This technical report appears as:
Ennis, D. M. and Ennis, J. M. (2011). How to Set Identicality Norms for No Preference Data. IFPress, 14(1) 3-4.

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How to Set Identicality Norms for No Preference Data

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This technical report also appears in our book, Tools and Applications of Sensory and Consumer Science.