Card Sort cluster analysis tool 1.1 results

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Users

Users
Name Email Login
Logout
Reply time Q1 reply Q2 reply Q3 reply
anon1@anon.com 2008-10-13 03:39:00
2008-10-13 03:41:37
00:02:37
anon2@anon.com 2008-10-13 03:42:00
2008-10-13 03:43:49
00:01:49
anon3@anon.com 2008-10-13 03:43:00
2008-10-13 03:45:04
00:02:04
anon4@anon.com 2008-10-13 03:45:00
2008-10-13 03:46:44
00:01:44
anon5@anon.com 2008-10-13 03:46:00
2008-10-13 03:48:37
00:02:37
anon7@anon.com 2008-10-13 03:51:00
2008-10-13 03:53:04
00:02:04
anon9@anon.com 2008-10-13 03:56:00
2008-10-13 03:57:15
00:01:15
anon10@anon.com 2008-10-13 03:57:00
2008-10-13 03:59:42
00:02:42
anon11@anon.com 2008-10-13 03:59:00
2008-10-13 04:02:32
00:03:32
9 replies, average reply time 00:02:16

User feedback

Cards

The following cards were used:


Similarity matrix

Each card is compared to each other card. The higher the number, the more similar the cards. The maximum score is 9.

Similarity matrix
train
9 tram
8 8 bus
7 7 6 subway
3 3 4 3 taxi
3 3 3 3 2 ship
1 1 1 2 2 3 helicopter
1 1 1 1 2 4 5 zeppelin
2 2 2 2 2 2 5 5 private jet
3 3 3 3 4 2 4 3 7 airplane
4 4 4 2 1 1 1 1 2 1 rollerskates
3 3 3 1 1 2 3 1 1   6 bicycle
1 1 1     1 2 2 3 1 5 5 segway
1 1 2 1 3   3 1 2 1 5 4 5 motorcycle
1 1 2 1 4   3 1 2 1 4 3 3 7 car
1 1 1   1 3 3 2 2 2 4 3 5 4 3 boat
1 1 1 1   3 2 3 1   4 4 4 2 3 3 feet

Dendrogram

The dendrogram shows a hierarchy based on the groupingsmade by the respondents.

SVG format dendrogram. Try hovering your cursor over the green labels.

Note: If you use Internet Explorer, you need to install Adobe SVG Viewer (no longer supported) to see SVG files. Most likely you also need to install a hotfix and to modify the registry. To download the hotfix, you need to give them your email address as the hotfix is password protected. After 2 hours of struggling I still could not get Internet Explorer 7 to work with embedded SVG images. My warm recommendation is to trash Internet Explorer and switch to a better browser, such as Firefox. It will take much less time and nerves. SVG was recommended by W3C in 2001 but as of January 2009 Internet Explorer still does not support it, being the only major browser not to.

JPG format dendrogram

Dendrogram

Clusters based on similarity

In the table below the cards are divided into different number of groups based on card similarity.

Clustering the cards based on similarity with each other
2 clusters
Cluster ID 32 29  
Suggested names
  • operated by someone else (100,0%)
  • Open to public (82,4%)
  • several wheels (70,6%)
  • operated by me (100,0%)
  • Private (82,4%)
  • for local movement (72,7%)
Match within cluster 23% 37%
Contains cards
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (15) ship
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
  • (17) feet
3 clusters
Cluster ID 29 31 26  
Suggested names
  • operated by me (100,0%)
  • Private (82,4%)
  • for local movement (72,7%)
  • Open to public (92,3%)
  • Workdays (80,0%)
  • public transport (80,0%)
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
Match within cluster 37% 31% 47%
Contains cards
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
  • (17) feet
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (15) ship
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
4 clusters
Cluster ID 29 26 30 15  
Suggested names
  • operated by me (100,0%)
  • Private (82,4%)
  • for local movement (72,7%)
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
  • Workdays (88,9%)
  • public transport (88,9%)
  • Open to public (83,3%)
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
Match within cluster 37% 47% 36% 100%
Contains cards
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
  • (17) feet
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (15) ship
5 clusters
Cluster ID 29 26 15 22 12  
Suggested names
  • operated by me (100,0%)
  • Private (82,4%)
  • for local movement (72,7%)
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
Match within cluster 37% 47% 100% 74% 100%
Contains cards
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
  • (17) feet
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
6 clusters
Cluster ID 26 15 22 12 28 17  
Suggested names
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
  • operated by me (92,3%)
  • fun (80,0%)
  • Private (75,0%)
  • unsorted (100,0%)
  • daily (66,7%)
  • Other (66,7%)
Match within cluster 47% 100% 74% 100% 42% 100%
Contains cards
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
  • (17) feet
7 clusters
Cluster ID 26 15 22 12 17 27 5  
Suggested names
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
  • unsorted (100,0%)
  • daily (66,7%)
  • Other (66,7%)
  • operated by me (83,3%)
  • Weekends (75,0%)
  • two wheels (75,0%)
  • in water (66,7%)
  • Water (66,7%)
  • On trips (50,0%)
Match within cluster 47% 100% 74% 100% 100% 44% 100%
Contains cards
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (17) feet
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (5) boat
8 clusters
Cluster ID 26 15 22 12 17 5 24 21  
Suggested names
  • in the air (100,0%)
  • Air (100,0%)
  • Very fast (66,7%)
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
  • unsorted (100,0%)
  • daily (66,7%)
  • Other (66,7%)
  • in water (66,7%)
  • Water (66,7%)
  • On trips (50,0%)
  • for local movement (85,7%)
  • two wheels (66,7%)
  • Slow (60,0%)
  • Weekends (80,0%)
  • on wheels (66,7%)
  • Fast (57,1%)
Match within cluster 47% 100% 74% 100% 100% 100% 56% 78%
Contains cards
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (17) feet
  • (5) boat
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
9 clusters
Cluster ID 15 22 12 17 5 24 21 25 20  
Suggested names
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
  • unsorted (100,0%)
  • daily (66,7%)
  • Other (66,7%)
  • in water (66,7%)
  • Water (66,7%)
  • On trips (50,0%)
  • for local movement (85,7%)
  • two wheels (66,7%)
  • Slow (60,0%)
  • Weekends (80,0%)
  • on wheels (66,7%)
  • Fast (57,1%)
  • Air (66,7%)
  • history (66,7%)
  • in the air (66,7%)
  • Very fast (100,0%)
  • travel (80,0%)
  • in the air (66,7%)
Match within cluster 100% 74% 100% 100% 100% 56% 78% 56% 78%
Contains cards
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (17) feet
  • (5) boat
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (13) helicopter
  • (8) zeppelin
  • (7) private jet
  • (6) airplane
10 clusters
Cluster ID 15 22 12 17 5 24 21 20 13 8
Suggested names
  • Water (66,7%)
  • in water (66,7%)
  • other (50,0%)
  • Workdays (100,0%)
  • public transport (100,0%)
  • on rails (85,7%)
  • Other (66,7%)
  • four wheels (66,7%)
  • On trips (50,0%)
  • unsorted (100,0%)
  • daily (66,7%)
  • Other (66,7%)
  • in water (66,7%)
  • Water (66,7%)
  • On trips (50,0%)
  • for local movement (85,7%)
  • two wheels (66,7%)
  • Slow (60,0%)
  • Weekends (80,0%)
  • on wheels (66,7%)
  • Fast (57,1%)
  • Very fast (100,0%)
  • travel (80,0%)
  • in the air (66,7%)
  • Not used by me (66,7%)
  • other (50,0%)
  • Air (40,0%)
  • history (100,0%)
  • Other (66,7%)
  • Too slow (50,0%)
Match within cluster 100% 74% 100% 100% 100% 56% 78% 78% 100% 100%
Contains cards
  • (15) ship
  • (16) train
  • (11) tram
  • (4) bus
  • (10) subway
  • (12) taxi
  • (17) feet
  • (5) boat
  • (9) rollerskates
  • (3) bicycle
  • (14) segway
  • (2) motorcycle
  • (1) car
  • (7) private jet
  • (6) airplane
  • (13) helicopter
  • (8) zeppelin

List of clusters

Cluster ID = the ID number of the cluster

Contains card IDs = a list of all cards in the cluster. The card ID is the same as the cluster ID (first column).

Score = the average number of pairings between the cards in the group. If three users have participated, and two of the users have grouped two cards together, the score is 2 (out of maximum of 3). The higher the score, the more similar the cards within the cluster. The maximum score is the number of participants (users), 9 in this study. If there is only one card in the cluster, the score can not be calculated as there can be no pairs of single cards.

Match = score / number of participants (9). A percentage indicating how similar the cards within the group are. If all participans have grouped to cards into the same group, match equals 100%.

Member of = the ID of the 'mother' cluster, i.e. the cluster to which this cluster belongs to

Name(s) = the top three best matching user-given name for each cluster

List of clusters
Cluster ID Contains cards Score Match Member of Name(s)
1
  • car (1)
- - 21
  • daily (66.7%)
  • four wheels (66.7%)
  • Weekends (50.0%)
2
  • motorcycle (2)
- - 21
  • Weekends (50.0%)
  • two wheels (50.0%)
  • fun (40.0%)
3
  • bicycle (3)
- - 23
  • Not used by me (66.7%)
  • other (50.0%)
  • two wheels (50.0%)
4
  • bus (4)
- - 19
  • on wheels (40.0%)
  • public transport (40.0%)
  • Workdays (40.0%)
5
  • boat (5)
- - 28
  • in water (66.7%)
  • Water (66.7%)
  • On trips (50.0%)
6
  • airplane (6)
- - 20
  • Very fast (66.7%)
  • On trips (50.0%)
  • travel (50.0%)
7
  • private jet (7)
- - 20
  • Very fast (66.7%)
  • travel (50.0%)
  • Never used (40.0%)
8
  • zeppelin (8)
- - 25
  • history (100.0%)
  • Other (66.7%)
  • Too slow (50.0%)
9
  • rollerskates (9)
- - 23
  • Weekends (50.0%)
  • for local movement (40.0%)
  • fun (40.0%)
10
  • subway (10)
- - 22
  • Underground (100.0%)
  • on rails (50.0%)
  • Workdays (40.0%)
11
  • tram (11)
- - 18
  • on rails (50.0%)
  • public transport (40.0%)
  • Workdays (40.0%)
12
  • taxi (12)
- - 30
  • Other (66.7%)
  • four wheels (66.7%)
  • On trips (50.0%)
13
  • helicopter (13)
- - 25
  • Not used by me (66.7%)
  • other (50.0%)
  • Air (40.0%)
14
  • segway (14)
- - 24
  • Other (66.7%)
  • two wheels (50.0%)
  • for local movement (40.0%)
15
  • ship (15)
- - 31
  • Water (66.7%)
  • in water (66.7%)
  • other (50.0%)
16
  • train (16)
- - 18
  • on rails (50.0%)
  • public transport (40.0%)
  • Workdays (40.0%)
17
  • feet (17)
- - 29
  • unsorted (100.0%)
  • daily (66.7%)
  • Other (66.7%)
18
  • train (16)
  • tram (11)
9.00 100.0% 19
  • on rails (80.0%)
  • public transport (66.7%)
  • Workdays (66.7%)
19
  • train (16)
  • tram (11)
  • bus (4)
8.00 88.9% 22
  • Workdays (85.7%)
  • public transport (85.7%)
  • on rails (66.7%)
20
  • private jet (7)
  • airplane (6)
7.00 77.8% 26
  • Very fast (100.0%)
  • travel (80.0%)
  • in the air (66.7%)
21
  • motorcycle (2)
  • car (1)
7.00 77.8% 27
  • Weekends (80.0%)
  • on wheels (66.7%)
  • Fast (57.1%)
22
  • train (16)
  • tram (11)
  • bus (4)
  • subway (10)
6.67 74.1% 30
  • Workdays (100.0%)
  • public transport (100.0%)
  • on rails (85.7%)
23
  • rollerskates (9)
  • bicycle (3)
6.00 66.7% 24
  • for local movement (66.7%)
  • Not used by me (50.0%)
  • operated by me (44.4%)
24
  • rollerskates (9)
  • bicycle (3)
  • segway (14)
5.00 55.6% 27
  • for local movement (85.7%)
  • two wheels (66.7%)
  • Slow (60.0%)
25
  • helicopter (13)
  • zeppelin (8)
5.00 55.6% 26
  • Air (66.7%)
  • history (66.7%)
  • in the air (66.7%)
26
  • helicopter (13)
  • zeppelin (8)
  • private jet (7)
  • airplane (6)
4.25 47.2% 32
  • in the air (100.0%)
  • Air (100.0%)
  • Very fast (66.7%)
27
  • rollerskates (9)
  • bicycle (3)
  • segway (14)
  • motorcycle (2)
  • car (1)
4.00 44.4% 28
  • operated by me (83.3%)
  • Weekends (75.0%)
  • two wheels (75.0%)
28
  • rollerskates (9)
  • bicycle (3)
  • segway (14)
  • motorcycle (2)
  • car (1)
  • boat (5)
3.80 42.2% 29
  • operated by me (92.3%)
  • fun (80.0%)
  • Private (75.0%)
29
  • rollerskates (9)
  • bicycle (3)
  • segway (14)
  • motorcycle (2)
  • car (1)
  • boat (5)
  • feet (17)
3.33 37.0% 33
  • operated by me (100.0%)
  • Private (82.4%)
  • for local movement (72.7%)
30
  • train (16)
  • tram (11)
  • bus (4)
  • subway (10)
  • taxi (12)
3.25 36.1% 31
  • Workdays (88.9%)
  • public transport (88.9%)
  • Open to public (83.3%)
31
  • train (16)
  • tram (11)
  • bus (4)
  • subway (10)
  • taxi (12)
  • ship (15)
2.80 31.1% 32
  • Open to public (92.3%)
  • Workdays (80.0%)
  • public transport (80.0%)
32
  • train (16)
  • tram (11)
  • bus (4)
  • subway (10)
  • taxi (12)
  • ship (15)
  • helicopter (13)
  • zeppelin (8)
  • private jet (7)
  • airplane (6)
2.08 23.1% 33
  • operated by someone else (100.0%)
  • Open to public (82.4%)
  • several wheels (70.6%)
33
  • train (16)
  • tram (11)
  • bus (4)
  • subway (10)
  • taxi (12)
  • ship (15)
  • helicopter (13)
  • zeppelin (8)
  • private jet (7)
  • airplane (6)
  • rollerskates (9)
  • bicycle (3)
  • segway (14)
  • motorcycle (2)
  • car (1)
  • boat (5)
  • feet (17)
1.59 17.6%
  • Private (74.1%)
  • operated by someone else (74.1%)
  • Ecological (64.0%)