Competitive Topography is another turnkey solution created to explore a number of entities, such as brands, that can be rated on a list of attributes.

In a default setup respondents are presented with a series of screens, each for a separate entity, asking to rate attributes using 5-star rating. Rating scheme can be changed to 7-star or 9-star rating. Alternatively, researcher may use Sliders instead. 

The ratings are then processed and displayed in three main views: Multidimensional Scale, Topography View, and Quadrant View. Rating process consists of two stages. First, a simple transformation is applied  - each star increment gives 10 score points for star rating, and each slider increment adds 10 score points for slider rating. Secondly, Bayesian Averaging is applied over the mean scores in order to prevent excessive values at low sample sizes:

where parameter C is set to 100, and  m is mean attribute score; more: wiki

Multidimensional Scale view displays results of the Nonmetric multidimensional scaling (NMDS) ordination method. Each entity and attribute is placed on a 2-d plane while preserving their pairwise distances in the original multidimensional entity portfolio in brand matrix. It is important to note that for the purposes of the ordination a portfolio of attributes in each brand is studied.
If an entity point is close to an attribute point, then the entity has higher share of the attribute in its portfolio compared to other entities. If an entity point is far from an attribute point, then the entity has lower share of the attribute in its portfolio compared to other entities.
If an entity is close to another entity, their attribute portfolios are similar. If an entity is far from another entity, their attribute portfolios are different.

Topography View is an extension of the Multidimensional Scale view, where besides the original 2-d NMDS points for entities and attributes, a third dimension is used to display average scores for either entities or attributes. In this view it is possible to find patterns in entity and attribute evaluation.

Quadrant View is designed to give freedom of selecting arbitrary slice of data and presenting it on a 2-d plot, with possibility of additional variables being added through point size and color.

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