Volume Two - Academic Conferences
Volume Two - Academic Conferences
Volume Two - Academic Conferences
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Fauziah Redzuan et al.<br />
understand the correlation of Kansei words, as well as to calculate factor analysis, in order to<br />
understand the significant factors that were formed from the evaluation results. In addition, based on<br />
the<br />
average score, specimens that fitted best to Kansei were identified.<br />
Figure 2 shows some examples of the average evaluation results based on the 35 subjects’<br />
evaluations. Correlation analyses, based on a pairwise correlation method were performed on the<br />
data, in order to understand the strength of the correlation between two or more Kansei words.<br />
Correlation Coefficient Analysis (CCA) is the measure of strength between variables. CCA is<br />
important to identify smaller, but precise sets of correlated Kansei words, from the larger set of<br />
emotion words (Lokman and Ibrahim<br />
2010). Figure 3 depicts the correlation matrix of the Kansei<br />
words with their correlation values.<br />
Subject No<br />
Specimen No<br />
Average Data 2<br />
KW<br />
1 2 3 4 5 6 7 8 9 10<br />
1 Relaxed 3.028571 2.685714 4.142857 3.457143 3.647059 3.857143 3.457143 3.8 2.828571 3.942857<br />
2 Intimidated 2.285714 2.742857 2.441176 2.828571 2.852941 2.885714 2.8 3.2 2.885714 3.257143<br />
3 Happy 2.657143 2.742857 4.085714 3.171429 3.676471 3.514286 3.342857 3.771429 2.885714 3.485714<br />
4 Merry 2.314286 2.514286 3.914286 3.114286 3.294118 3.142857 3.142857 3.6 2.657143 3.457143<br />
5 Unpleasant 2.742857 2.705882 2.257143 2.657143 2.441176 2.457143 2.771429 2.6 3 2.771429<br />
6 Leisurely 2.857143 2.828571 3.742857 3.028571 3.333333 3.428571 3.4 3.342857 3.114286 3.314286<br />
7 Soothing 3.085714 3.085714 3.529412 2.914286 2.970588 3.085714 3.2 3.114286 3.142857 3.228571<br />
8 Unsafe 2.371429 2.514286 2.085714 2.257143 2.176471 2.428571 2.371429 2.2 2.628571 2.428571<br />
9 Sensational 2.514286 2.857143 3.571429 3 3.235294 3.257143 3.028571 3.628571 2.714286 3.257143<br />
10 Surprised 2.542857 2.714286 3.285714 2.771429 2.852941 3.085714 3.257143 3.514286 2.742857 3.028571<br />
11 Unwise 2.571429 2.342857 2.228571 2.514286 2.235294 2.457143 2.205882 2.485714 2.885714 2.428571<br />
12 Heavy 3.171429 3.147059 2 2.8 2.705882 2.828571 3 3 3.342857 2.828571<br />
13 Learnable 4.352941 3.828571 4.285714 3.942857 4.264706 4.314286 3.971429 4.228571 3.714286 3.971429<br />
14 Solemn 3.764706 3.114286 2.676471 3.371429 3.058824 3.114286 3 3.114286 2.857143 3<br />
15 Touched 2.323529 2.514286 3 2.942857 2.823529 2.857143 2.914286 3.314286 2.857143 3<br />
16 Restless 2.882353 2.8 2.542857 2.828571 2.852941 2.685714 2.828571 3.028571 2.6 2.657143<br />
17 Proud 3 2.914286 3.8 3.057143 3.294118 3.228571 3.342857 3.542857 2.885714 3.4<br />
18 Mad 2.058824 2.428571 1.714286 2.142857 2.029412 2 2.142857 2.057143 2.514286 2.085714<br />
19 Pleasant 2.939394 3.257143 3.8 3.342857 3.529412 3.2 3.285714 3.514286 3.171429 3.257143<br />
20 Unwilling 2.606061 2.657143 1.914286 2.714286 2.617647 2.428571 2.628571 2.314286 2.914286 2.342857<br />
21 Neutral 3.617647 3.085714 3.514286 3.514286 3.470588 3.771429 3.828571 3.742857 3.542857 3.714286<br />
22 Irritated 2.647059 3.028571 2.2 2.742857 2.205882 2.371429 2.514286 2.6 3.028571 2.457143<br />
23 Curious 3.441176 3.485714 3.514286 3.514286 3.794118 3.4 3.371429 3.285714 3.428571 3.485714<br />
24 Despairing 2.764706 2.771429 2.428571 2.771429 2.588235 2.742857 2.771429 2.771429 3.114286 2.771429<br />
25 Competent 3.176471 3.257143 3.457143 3.314286 3.323529 3.371429 3.314286 3.142857 3.314286 3.485714<br />
26 Unique 2.647059 2.971429 3.971429 2.8 3.205882 3.514286 3.4 4.085714 2.8 3.542857<br />
27 Involved 3.411765 3.257143 3.971429 3.457143 3.647059 3.542857 3.6 3.6 3.057143 3.657143<br />
28 Confused 2.970588 3.114286 2.114286 2.885714 2.088235 2.257143 2.857143 2.685714 2.828571 2.571429<br />
29 Inefficient 2.617647 2.857143 2.205882 2.6 1.911765 2.2 2.685714 2.342857 3.057143 2.114286<br />
30 Inconvenient 2.588235 2.885714 2 2.628571 2 2.114286 2.685714 2.514286 2.828571 2.171429<br />
31 Unresponsive 2.441176 2.828571 2 2.714286 2.176471 2.257143 2.485714 2.314286 3.028571 2.4<br />
32 Desirable 2.941176 3.342857 3.882353 3.142857 3.588235 3.457143 3.371429 3.314286 3.2 3.457143<br />
33 Perfect 2.735294 2.857143 3.735294 3.2 3.823529 3.657143 3.257143 3.742857 2.685714 3.714286<br />
34 Uncooperative 2.588235 2.6 2.058824 2.571429 2.323529 2.090909 2.4 2.485714 3 2.257143<br />
35 Ready 3.588235 3.285714 3.852941 3.428571 3.823529 3.714286 3.371429 3.8 3 3.771429<br />
36 Unconscious 2.382353 2.6 2.176471 2.714286 2.5 2.4 2.628571 2.342857 3.028571 2.542857<br />
Figure 2: Example of the calculated average values for each specimen based on the Kansei words<br />
A correlation value above 0.8 was considered strong; therefore, some strong correlations did exist<br />
between specific Kansei words. This detail is important to understand, in order to extract the highlevel<br />
Kansei words, or in other words, to identify the most significant Kansei words. Words in the<br />
correlation matrix with a correlation value of 0.8 and above were extracted and further grouped<br />
together, based on each of the Kansei words. This finding serves as the basis for identifying the highlevel<br />
Kansei words necessary to reduce the number of Kansei words from 478 to between 50 and<br />
around 100 words, for the secondary experiment, as was suggested by (Nagamachi and Lokman<br />
2011).<br />
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