lmerTest (version 3.1-3)

carrots: Consumer Preference Mapping of Carrots

Description

In a consumer study 103 consumers scored their preference of 12 danish carrot types on a scale from 1 to 7. Moreover the consumers scored the degree of sweetness, bitterness and crispiness in the products.

Usage

data(carrots)

Format

Consumer

factor with 103 levels: numbering identifying consumers.

Frequency

factor with 5 levels; "How often do you eat carrots?" 1: once a week or more, 2: once every two weeks, 3: once every three weeks, 4: at least once month, 5: less than once a month.

Gender

factor with 2 levels. 1: male, 2:female.

Age

factor with 4 levels. 1: less than 25 years, 2: 26-40 years, 3: 41-60 years, 4 more than 61 years.

Homesize

factor with two levels. Number of persons in the household. 1: 1 or 2 persons, 2: 3 or more persons.

Work

factor with 7 levels. different types of employment. 1: unskilled worker(no education), 2: skilled worker(with education), 3: office worker, 4: housewife (or man), 5: independent businessman/ self-employment, 6: student, 7: retired

Income

factor with 4 levels. 1: <150000, 2: 150000-300000, 3: 300000-500000, 4: >500000

Preference

consumer score on a seven-point scale.

Sweetness

consumer score on a seven-point scale.

Bitterness

consumer score on a seven-point scale.

Crispness

consumer score on a seven-point scale.

sens1

first sensory variable derived from a PCA.

sens2

second sensory variable derived from a PCA.

Product

factor on 12 levels.

Details

The carrots were harvested in autumn 1996 and tested in march 1997. In addition to the consumer survey, the carrot products were evaluated by a trained panel of tasters, the sensory panel, with respect to a number of sensory (taste, odour and texture) properties. Since usually a high number of (correlated) properties (variables) are used, in this case 14, it is a common procedure to use a few, often 2, combined variables that contain as much of the information in the sensory variables as possible. This is achieved by extracting the first two principal components in a principal components analysis (PCA) on the product-by-property panel average data matrix. In this data set the variables for the first two principal components are named (sens1 and sens2).

Examples

# NOT RUN {
fm <- lmer(Preference ~ sens2 + Homesize + (1 + sens2 | Consumer), data=carrots)
anova(fm)

# }