Technical Notes on Market Research
Three brief slides on the history of Scaling in Marketing Research and the reasons behind the development of Linescale. For the technically minded…

• Market Research scaling methods over the past 60 years were greatly influenced by work done by Pilgrim and Kamen and others for the U.S. Army Quartermaster Corps during World War ll. Kraft General Foods, Pillsbury, General Mills and many of the other great packaged food companies of the mid-twentieth century - and their advertising agencies - adopted hedonic scale techniques pioneered there. Classic research used nine-point scales, either balanced or unbalanced, to measure food preferences of military personnel. Part of the aim was to measure preferences for categories of foods. For example, were rutabagas preferred to parsnips? Potatoes to rice? Some foods score high; some low. In this classic testing of apples versus oranges, they settled on metric scaling as a convenient, reliable way to measure hedonic norms for categories of foods. These scales were later carried into post-war commercial research for the food industry as packaging and branding of food marketing rolled out.

• Experimentation was done by the more capable research departments, but was generally limited to the number of scale items - nine, seven, six or five items- word choice, balanced or unbalanced scales, graphic representation of the scales, compression and expansion of the psychological space, etc. But with rare exception, such as Eric Marder's constant sum technique, metric scales have been industry practice.

• But scale scores have severe limitations for practical use. Metric scales need large numbers of respondents. Why? They are very "noisy". The noise stems from individual differences. Some people tend to score things high; some score them low. Some score big differences between things, some small. Worse, the same individual will score the same item higher or lower at different times. The net effect is unintended differences that need to be canceled out by large numbers and statistics. Compounding the problem, each product category also carries its own norms; desserts and novelties score high; root vegetables and necessities score low. This forces serious researchers to use large samples. Traditional solutions to this problem include looking at "top box' or "top two boxes". This cancels out much of the scale noise, but also loses information. Canceling out the noise with large samples gets expensive quickly. The dilemma has been forego research, do focus groups or spend a bundle for a large-sample one time study.