beersBlindSorting: Novices and Experts sorted 3 types of beers from 3 different...

In DistatisR: DiSTATIS Three Way Metric Multidimensional Scaling

Novices and Experts sorted 3 types of beers from 3 different brewers without and without seeing the beers.

Description

beersBlindSorting: several different groups of Novices and Beer-Experts sorted 9 beers with (Vision) or without (Blind) visual information. The 9 beers were 3 types of beers (blond, amber, and dark) obtained from 3 different brewers (Pelforth, Chti, & Leffe).

Usage

beersBlindSorting

Format

A list with 11 lists each storing a 9*9*N_k cubeOfDistance and one 9*9 distance table. Specifically:

$EV

9*9* 17 Experts, Vision

$EBr1

9*9* 13 Experts, Blind, rep 1

$EBr2

9*9* 13 Experts, Blind, rep 2

$EBr3

9*9* 13 Experts, Blind, rep 3

$EBr4

9*9* 13 Experts, Blind, rep 4

$NV

9*9* 21 Novices, Vision

$NBr1

9*9* 18 Novices, Blind, rep 1

$NBr2

9*9* 18 Novices, Blind, rep 2

$NBr3

9*9* 18 Novices, Blind, rep 3

$NBr4

9*9* 18 Novices, Blind, rep 4

$N2B

9*9* 37 Novices, Blind. (Group 2)

Details

Nine different commercial beers (denoted PelfBL, PelfA, PelfBR, ChtiBL, ChtiA, ChtiBR, LeffBL, LeffA, and LeffBR) were evaluated. These beers came from three different breweries: Pelforth (noted Pelf), Chti, (Chti), and Leffe (Leff), and each brewery provided three types of beer: blond (BL), amber (A), and dark (BR).

For each sorting task the data file gives the sorting distance matrix: A 9-beers by 9-beers distance matrix in which at the intersection of a row (representing one beer) and a column (representing another beer) a value of 0 indicates that these two beers were sorted in the same group and a value of 1 indicates that these two beers were sorted in different groups.

Multiple groups of novices and experts participated to the experiments. In the blind condition, the group of experts and one group of novices repeated four times the sorting taks (replication 1 to 4).

Author(s)

Maud Lelièvre, Sylvie Chollet , Hervé Abdi, and Dominique Valentin.

Source

A longer description of the data, story, first analysis, etc. can be found in: Lelièvre M., Chollet, S., Abdi, H., & Valentin, B. (2009). Beer trained and untrained assessors rely more on vision than on taste when they categorize beers. Chemosensory Perception, 2, 143-153. available from https://personal.utdallas.edu/~herve/abdi-lcav09-inpress.pdf

DistatisR documentation built on Dec. 5, 2022, 9:05 a.m.