**3. Background to the studies presented in this chapter**

During the first decade of the 21st century, from 2001 to 2005, author HRM was involved in the development of Mind Genomics as an integrated database of the mind [5, 10]. During those 4 years, Moskowitz and colleagues created the It! studies, each It! study comprising 20–30 parallel studies of a food or beverage. The studies themselves were designed according to a common experimental design, typically comprising four basic questions (silo), and nine answers (element) to each basic question. Each respondent evaluated a total of 60 vignettes, each vignette comprising 2–4 elements, at most one element from each silo. Furthermore, each respondent evaluated every element the same number of times. One of the important innovations of the Mind Genomics approach is that each respondent evaluated the same number of vignettes, 60, but the 60 vignettes for each respondent differed from the 60 vignettes for every other respondent. Indeed, the Mind Genomics algorithm ensured that most of the vignettes in a study were seen at most two or three times across the several hundred respondents, and the several thousand vignettes.

The design, permuted orthogonal design [11], was the most important feature of the Mind Genomics approach, instantiated in these different sets of 60 vignettes each, one set for each respondent. The second innovation providing the statistical power, and the potential for deep understanding was that the 60 vignettes were arrayed according to an experimental design, at the level of the individual respondent. This is called the within-subjects design. Each respondent could be investigated alone, without the need of other respondents.

The mathematical structure of the vignettes, comprising individual, permuted experimental designs, means that the 36 independent variables, so-called 'elements', were likely to be statistically independent of each other, whether the data were considered in their original form for one respondent (4 questions, 9 answers per question), or the data combined the results from any combination of respondents.

The respondents evaluated each rating on an anchored 1–9-point scale, a socalled category, or Likert Scale. The scale allows the respondent to act as a measuring instrument. However, for the subsequent analysis, the 9-point scale was divided into two parts. Ratings of 1–6 were converted to 0 to denote little or no level of the attribute being rated (e.g., desirability or crave-ability of the product denoted by the scale). Ratings of 7–9 were converted to 0 to denote a great deal of the attribute being rated. The conversion of ratings from the more granular 1–9 scale to the less granular 0/100 scale was done following the basic worldview of consumer researchers, wherein managers prefer all-or-none or yes-no answers. The binary transformed ratings, generating a 0/100 output, makes it easy for the manager to understand and use the data. Some of the granularity is lost, however. A good practice is to work with at least 5-–0 respondents with different patterns of response AFTER the binary transform has been done.

The mathematics of the design allowed the researcher to use OLS (ordinary least-squares) regression to the presence/absence of the element (Eq. (1)):

$$\text{Rating} = \text{k0} + \text{k1(A2)} + \text{k2(A2)} \dots \text{k36(D9)}\tag{1}$$

The foregoing model allows the researcher to learn, quite quickly, which of the elements (A1–D9) are key to driving the rating.

Moving beyond the general model, the Mind Genomics software created individual level models, one model or equation for each respondent. This analysis is possible because the underlying experimental design was 'complete' for each respondent. That is, one needed only the ratings from each respondent to create an equation for that respondent. The individual models were then clustered [12], so that similar patterns of the 36 coefficients were put into the same cluster or group. Clustering itself is a form of exploratory data analysis. The objective of clustering is simply to identify, in the manner of a heuristic, generally groups showing distinct, and interpretable patterns. The composition of the clusters is a function of the data itself, and the form of clustering.

Each data set was subjected to the same clustering approach whereby the first two clusters were generated, and then three clusters. We chose the fewest number of clusters, subject to the requirement that the clusters could be interpreted, that is, in such a way that it told a coherent, seemingly reasonable story. Generally, the 'three-cluster-solution' best fulfilled the joint goals of parsimony (fewer clusters are better) and interpretability (the clusters told a story, which made sense at a logical level).

#### **4. Understanding the results**

We begin the analysis with a summary table showing how the Mind Genomics process identified the relevant mind-sets for 10 dairy products. **Table 1** shows the different, emerging mind-sets for each product. The remainder of this chapter will discuss how these mind-sets were discovered and used for understanding how people think of dairy products, and how the minds of 41 students were 'sequenced' to identify the pattern of mind-sets for dairy for each student.

We now go in depth to show how these mind-sets were developed, and the rich data underlying the table. **Table 2** shows the three clusters emerging from the clustering for healthful yogurt. The clustering was done on all 36 elements, in the original study, to generate either two or three different clusters (mind-sets). Author SD then selected 16 elements from the data to present. Keep in mind that each data table, **Table 2**, is a reduced version of a larger 4 � 9 table. The 16 elements were

*Sequencing the 'Dairy Mind' Using Mind Genomics to Create an "MRI of Consumer Decisions" DOI: http://dx.doi.org/10.5772/intechopen.101422*


#### **Table 1.**

*The different mind-sets (MS) for 10 dairy products.*



#### **Table 2.**

*Summary data for the healthful yogurt product. The table shows the original 4* � *9 design, truncated to a 4* � *4.*

then associated with four questions, so that each 'question' was associated with four different, but related elements. This analysis was done AFTER the research was completed. The four questions and the four answers to each question, created on a post-hoc basis, do not affect the results at all, but simply represent an easy way to deal with the data for subsequent analyses.

**Table 2** presents the data in three columns, one column for each mind-set. The base size shows the number of respondents in the cluster. Note that the clustering program attempts to separate the respondents into either two or three groups based upon the pattern of the 36 coefficients. The base sizes do not have to be equal. Furthermore, the clustering program is 'agnostic' in terms of the 'meaning' of the elements and the reason for their membership in a cluster. The only consideration is the satisfaction of the mathematical criterion.

The additive constant is the estimated value of a vignette without any elements. Since all vignettes comprised elements, the additive constant is a purely estimated parameter. The OLS regression relating the presence/absence of the 36 elements to the binary response returns with one number for each element. This element is the coefficient. The coefficients can be both positive and negative. For positive coefficients, the interpretation is that putting the element into a vignette sways an additional percent of the respondents to assign the rating of 7**–**9. Furthermore, the coefficient, whether positive or negative (see below) can be added to the constant to estimate the percent of times that the vignette would be assigned a value of 7**–**9 on a 9-point scale, when the vignette comprises the specific element(s).

*For example, for Mind-Set 1, the additive constant is 30. A vignette comprising E1, E5, E9, would be expected to get ratings of 7***–***9 (30 + 21 + 23 + 12) or about 86% of the time. The same vignette would be far lower in Mind-Set 2 because two of the coefficients are either 0 or negative (not shown), and only one coefficient (E9) is positive.*

Looking at mind-set 1, we see that the additive constant for healthful yogurt is 30. In the absence of elements, we expect 30% of the responses to be ratings of 7**–**9, and the other 70% of response to be 1**–**6. Again, keep in mind that the additive constant is a purely theoretical parameter, computed by the OLS regression. Mindset 2 is a bit more positive. The additive constant is 55, meaning that in the absence of elements, we expect to see 55% of the responses from the mindset to be between 7 and 9. Finally, Mind-set 3, with 89 respondents, shows an additive constant of 39, in the middle. This implies that in the absence of elements, we see 39% of the responses from this mindset to lie between 7 and 9.

Our first conclusions are that there are three interpretable mind-sets. The basic interest in healthful yogurt spans a range from low (mind-set 1) to reasonably high (mind-set 2). What we do not know is the nature of the mind-sets. The remainder

*Sequencing the 'Dairy Mind' Using Mind Genomics to Create an "MRI of Consumer Decisions" DOI: http://dx.doi.org/10.5772/intechopen.101422*


#### **Table 3.**

*Summary data for the healthful yogurt product. The elements have been sorted from highest to lowest rating.*

of the table shows coefficients from 16 of the 36 elements, or 44% of the original data, the specific elements in the table being chosen because it is 'actionable', viz., describing the nature of the product. Some elements score very strongly across all mind-sets. An example is 'The delicious, classic fruit flavors like raspberry, strawberry banana, and blueberry.' Some elements score very strongly, but perhaps only with one mind-set. They may or may not even score positively among other mindsets. A good example is 'Contains the essential nutrient choline … shown to improve memory and learning', performing well in mind-set 1, virtually irrelevant in mindset 2, and perhaps totally irrelevant, and even damaging in mind-set 3.

**Table 2** can be made more informative by sorting the table by mind-set; this can be done based upon the strong performing elements (coefficient => 8). The sorted table shows the strong performing elements for each mind-set, with elements performing strongly in two mind-sets appearing twice or thrice, once for each mind-set in which the element performs strongly. The duplicates are not important. **Table 3** shows the sorted data.

When we look at the 10 tables of data, we see 10 different sets of mind-sets, generally three mind-sets for a study, but sometimes two mind-sets. We look at the mind-sets for the 4 � 4 matrices, and in our analysis develop a name for each mindset, based upon the elements that perform most strongly. It is important once again to reiterate the fact that the clustering program does not name the mind-set. Rather, the researcher does. All that the clustering does is to create the different groups based upon statistical criteria.
