Chapter 9: How does Attitude Influence the Perception of Time?
9.2.2 Analysis of Clusters
In this section the responses of the respondents grouped by the cluster analysis are discussed. Consistent with the Section 9.5, the Pearson Chi-square two-sided significance test was used to test for statistically significant differences. The median of the responses of the statements by clusters and the level of statistical significances are shown in Table 9.4.
Recalling Section 8.2, the four clusters of travellers were as follows: 1. Frequent mature males on business.
2. Frequent young employed on business. 3. Infrequent elderly males for leisure. 4. Infrequent mature females on business.
Table 9.4 shows that the responses to the statements numbers 4, 5, 14, and 16 were statistically significantly different at the 95% level of confidence, whilst the responses to the statements, numbers 3, 11, and 15, were statistically significantly different at the 90% level of confidence among clusters. Respondents in cluster 3 were more likely to be neutral to the statement number 3 suggesting that this group of respondents were not familiar with/ do not use personal electronic devices, therefore this question is inappropriate. Delays seemed to be less frustrating to those in cluster 2 when free Wi-Fi is available on-board.
Table 9.4 The statistics based on cluster analysis
No. Attitude
Median by Clusters
Sig. α
1 2 3 4
1 I have the opportunity to work during my journey today.
6.00 6.00 6.00 6.00 0.72 2 Working on the train is more
productive than in the office.
3.00 3.50 4.00 4.00 0.18 3 ε The journey seems to pass more
quickly when I am using my
personal electronic devices, such as a laptop or iPhone.
5.00 6.00 4.50 5.00 0.06
4 β Delays are less frustrating when free Wi-Fi is available on board
4.00 6.00 4.00 4.00 0.01 5 β A small increase in travel time would
be acceptable as long as free Wi-Fi is available on-board.
3.00 4.00 2.50 3.00 0.00
6 A small increase in the cost of the ticket would be acceptable as long as free Wi-Fi is available on-board.
2.00 2.00 2.00 2.00 0.29
7 The duration of this journey is too
long. 4.00 4.00 4.00 4.00 0.84
8 This train is comfortable. 5.00 6.00 5.50 6.00 0.25 9 I worry about my personal safety
when I travel by train.
1.00 2.00 1.00 2.00 0.14 10 Travelling by train makes me
nervous.
1.00 1.00 1.00 1.00 0.56 11 ε Travelling by train is tiring. 3.00 4.00 2.00 3.00 0.08 12 I sometimes feel unwell when
travelling by train.
1.00 1.00 1.00 1.00 0.16 13 I sometimes feel uncomfortable
being around people I don’t know on the train.
2.00 1.00 1.00 2.00 0.85
14 β I would encourage people to use this
train service. 5.00 6.00 6.00 6.00 0.04 15 ε I do not like to juggle several
activities at the same time.
4.00 4.00 4.00 2.00 0.07 16 β People should not try to do many
things at once.
4.00 3.00 4.00 2.00 0.00 17 When I am at my office desk, I work
on one project at a time.
3.00 3.00 4.00 3.00 0.62 18 I am comfortable doing several
things at the same time.
6.00 6.00 6.00 6.00 0.58
α Pearson Chi-square two-sided significance test of the raw data β Significant at the 95% level of confidence
Although the responses were slightly different among clusters, all of the respondents did not agree with the statements number 5 (increase in travel time is more acceptable if free Wi-Fi is available) and 11 (travelling by train is tiring) except those who in cluster 2 were more neutral. All groups preferred to encourage people to use the train; however, the preference of cluster 1 was not as strong as the others. All clusters except cluster 4 were indifferent to the statements number 15 (I do not like to juggle several activities) and 16 (people should not try to do many things at once); this suggests that women were more likely to enjoy multitasking.
9.3 Factor Analysis
Preliminary data analysis indicates that responses to some statements were similar within groups suggesting an “overlap” of some variables. Therefore, Factor Analysis (FA) was conducted to reduce the number of variables to be considered in the analysis. All responses received for the 18-statement list were entered as variables in to the factor analysis using SPSS version 19. The method of extraction selected was principal components analysis and the rotation was varimax. The principal components analysis (PCA) and varimax rotation were chosen because it is the most widely used FA documented in the literature (Kline, 1994) and recommended for data reduction (Moi and Sarstedt, 2011). The result of the analysis was compared to other techniques to assess the strength of the underlying constructs of the final solution. The factor score was saved as the variables for further analysis.
In order to assess the sufficiency of the variables for FA, an evaluation of the correlations matrix, KMO, variable-specific measures of sampling adequacy (MSA) on the diagonal of the anti-image correlation matrix were carried out. The correlation matrix revealed that each variable was statistically significantly correlated at the 95% level of confidence to at least seven others. The MSA scores varied in the range between 0.523 to 0.829, which were above the threshold value of 0.50 suggested by Moi and Sarstedt (2011). The Kaiser–Meyer–Olkin (KMO) was 0.724, which was referred as middling adequacy of correlation. Furthermore, the Bartlett’s test of sphericity is significant (p < 0.05) and therefore FA is appropriate.
Table 9.4 Percentage of variance accounted
Figure 9.1 Scree plot of PCA with the selected factors in oval shape
The decision on the number of factors extracted was based on the Kaiser Criterion, where all components with eigenvalues under 1.0 were dropped (Kaiser, 1960) and the scree plot (Cattell, 1966). The eigenvalue larger than a 1.0 approach revealed extraction of 6 factors, which accounted for 64% of variance as shown in Table 9.4. The scree plot shown in Figure 9.1 revealed that the eigenvalues drop largely on the first three factors and an elbow formed at the fourth suggesting extraction of three factors. However, extraction of the three factors only accounted for 44% of the variance and obtained larger redundant residuals (52%). Another elbow is formed at the 7th factors suggesting extraction of six factors, and this is similar to the result of the Kaiser Criterion approach. Therefore, six factors were selected for this study.
Both of these methods, the scree plot and the eigenvalues indicated that the number of factors to extract was 6 factors and accounted for 63.7% of the total variance in the data set and as such 37.3% of the variance was not extracted. Factor 1 to 6 accounted for 19.88%, 13.60%, 10.71%, 6.93%, 6.42%, and 6.08% of the variance respectively with eigenvalues greater than 1.0. The communalities scores and factor-loading of the final solution of the FA is shown in Table 9.6. The communality values varied in the range of 0.460 to 0.720. The high scores of communalities and factor loadings suggested a good model fit to the data and indicated a strong internal stability.
Comparison of the PCA result with other extraction methods such as unweighted least squares, generalised least squares, and image factoring, revealed similar factor loading patterns with extraction of 6 factors suggesting that strong underlying constructs within the data were present. However, the total accounted variances of the extracted factors obtained from those techniques were lower than the PCA. Conversely, principal axis factoring (PAF), maximum likelihood, and alpha factoring could not extract any factors because the communality of a variable is exceeded 1.0. In term of the rotation methods, oblique rotation methods revealed that none of the factors were statistically significantly correlated (>0.30). Therefore, the use of varimax was justified.
Table 9.5 The factor solution
Factors St.
Number Components Communality
Factor loading
Factor 1 9 I worry about my personal safety when I travel by train.
0.649 0.772 10 Travelling by train makes me nervous. 0.644 0.745 11 Travelling by train is tiring. 0.526 0.633 12 I sometimes feel unwell when
travelling by train.
0.643 0.780 13 I sometimes feel uncomfortable being
around people I don’t know on the train.
0.552 0.724
Factor 2 15 I do not like to juggle several activities at the same time.
0.721 0.839 16 People should not try to do many
things at once.
0.670 0.806 17 When I am at my office desk, I work
on one project at a time.
0.460 0.612 18 I am comfortable doing several things
at the same time.
0.650 -0.762 Factor 3 3a The journey seems to pass more
quickly when I am using my personal electronic devices, such as a laptop or iPhone.
0.628 0.525
4 Delays are less frustrating when free Wi-Fi is available on board
0.687 0.778 5 A small increase in travel time would
be acceptable as long as free Wi-Fi is available on-board.
0.825 0.892
6 A small increase in the cost of the ticket would be acceptable as long as free Wi-Fi is available on-board.
0.641 0.725
Factor 4 8 This train is comfortable. 0.667 0.814 14 I would encourage people to use this
train service.
0.590 0.718 Factor 5 1 I have the opportunity to work during
my journey today.
0.552 0.645 2 Working on the train is more
productive than in the office.
0.712 0.816 Factor 6 7 The duration of this journey is too
long.
0.649 0.735
a A cross factor loading revealed among the 3rd and the 6th factor (0.525 and 0.504
Using the highest absolute factor loadings, each variable was assigned to a certain factor and then a label was assigned to describe the collective characteristics of all variables associated with each. These were as follows:
1. Factor 1: Personal feeling 2. Factor 2: Multitasking ability 3. Factor 3: Technology effect 4. Factor 4: Train comfort potential 5. Factor 5: Productivity
6. Factor 6: Journey duration
There is a degree of consistency in the results of the factor analysis and the descriptive analysis in the sense that factor 1 confirmed the observation in Section 9.2 that numbers 9, 10, 11, 12, and 13 were similar. Subsequently, in terms of multitasking ability, consistent with the results in the Section 9.2, the items number 15, 16, 17 and 18, were also similar and grouped as factor 2.