Special Topics - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 221

greater the distance. Table 9.8 shows an example of what that might look like.
In this example, only 2 of the participants put the oranges and peaches in dif-
ferent groups, whereas all 20 of the participants put the bananas and tomatoes
into different groups.

Table 9.8 Overall distance matrix for 20 participants in the fruit card-sorting study.
Apples Oranges Strawberries Bananas Peaches Plums Tomatoes Pears Grapes Cherries

Apples — 5 11 16 4 10 12 8 11 10

Oranges — 17 14 2 12 15 11 12 14

Strawberries — 17 16 8 18 15 4 8

Bananas — 17 15 20 11 14 16

Peaches — 9 11 6 15 13

Plums — 12 10 9 7

Tomatoes — 16 18 14

Pears — 12 14

Grapes — 3

Cherries —

Table 9.8 Overall distance matrix for 20 participants in the fruit card-sorting study.

This overall matrix can then be analyzed using any of several standard sta-
tistical methods for studying distance (or similarity) matrices. Two that we find
useful are hierarchical cluster analysis (e.g., Aldenderfer & Blashfield, 1984)
and multidimensional scaling (MDS)(e.g., Kruskal & Wish, 2006). Both are
available in a variety of commercial statisti-
cal analysis packages, including SAS ( http://
www.sas.com ) , IBM SPSS ( http://www.spss.
com ) , and NCSS ( http://www.ncss.com/ ), as
well as some add-on packages for Excel (e.g.,
Unistat, http://www.unistat.com ; XLStat,
http://www.xlstat.com ).

bananas

tomatoes

plums

cherries

grapes

HIERARCHICAL CLUSTER ANALYSIS
The goal of hierarchical cluster analysis is
to build a tree diagram where the cards that
were viewed as most similar by the partici-
pants in the study are placed on branches
that are close together. For example, Figure
9.4 shows the result of a hierarchical cluster
analysis of the data in Table 9.8 . The key to
interpreting a hierarchical cluster analysis is
to look at the point at which any given pair
of cards “join together” in the tree diagram.

strawberries

pears

peaches

oranges

apples

0 5 10 15 20

Dissimilarity

Figure 9.4 Result of a hierarchical cluster analysis of data shown in
Table 9.8 .