Example: Perceptions of Coffee Brands
The previous example involved a small table of hypothetical data. Actual applications often involve much larger tables. In this example, you will use data pertaining to perceived images of six iced-coffee brands 1. This dataset can be found in coffee.sav. See the topic Sample Files for more information.
For each of 23 iced-coffee image attributes, people selected all brands that were described by the attribute. The six brands are denoted as AA, BB, CC, DD, EE, and FF to preserve confidentiality.
| Image attribute | Label | Image attribute | Label |
|---|---|---|---|
| good hangover cure | cure | fattening brand | fattening |
| low fat/calorie brand | low fat | appeals to men | men |
| brand for children | children | South Australian brand | South Australian |
| working class brand | working | traditional/old fashioned brand | traditional |
| rich/sweet brand | sweet | premium quality brand | premium |
| unpopular brand | unpopular | healthy brand | healthy |
| brand for fat/ugly people | ugly | high caffeine brand | caffeine |
| very fresh | fresh | new brand | new |
| brand for yuppies | yuppies | brand for attractive people | attractive |
| nutritious brand | nutritious | tough brand | tough |
| brand for women | women | popular brand | popular |
| minor brand | minor |
Initially, you will focus on how the attributes are related to each other and how the brands are related to each other. Using principal normalization spreads the total inertia once over the rows and once over the columns. Although this prevents biplot interpretation, the distances between the categories for each variable can be examined.