Chapter IV- Data Analysis
4.1 Factor Analysis
4.1.1 Factor analysis on attractiveness of game design element 1st run
The factor analysis conducted on the research variables uses principal factor analysis (PFA) and varimax to perform orthogonal rotation. The rotated factor loading matrix is obtained when Eigenvalue is bigger than 1.
KMO and Bartlett’s test of sphericity is used when conducting the factor analysis. KMO, also known as Kaiser-Meyer-Olkin, is the test of the adequacy of the samples as the tools of measurement for a study. According to Kaiser, when KMO >0.9 (excellent), KMO >0.8 (great), KMO >0.7 (Good), KMO >0.6 (Average), KMO >0.5 (Poor), and KMO <0.5 (Reject).
From Barlett’s test of shpericity, we can determine whether the data fits as a multivariate normal distribution, and that it can also be used to test whether the correlation coefficient matrix is fitted in order to proceed to the factor analysis. This study’s approximate chi-square is 9069.427, with a degree of freedom of 300 when the significance level is set at α = 0.05. In other words, the test is significant and that the correlation coefficient matrix representing the parent group does have an existing common factor. Furthermore, the KMO value is 0.929, meaning the study’s attractiveness of game design element is a fitted research variable to run the factor analysis.
According to Wu & Lin (2001), when factor loading is bigger than 0.6, it means the factor is significant, and we should reject the factor when it is less than 0.6.
Research conducted by Zaltman & Burger (1975) pointed out that when cumulative explained variance is higher than 40%, the result is within a reasonable range. After conducting the factor analysis on the attractiveness of game design element , the cumulative explained variance is 76.052%, as shown in table 4-1.
Table 4-1 Attractiveness of Game Design Element Eigenvalue and Explained Variance (1st Run)
Factor Eigenvalue Explained Variance Cumulative Explained Variance
Factor 1 12.787 51.147% 51.147%
Factor 2 2.156 8.25% 59.772%
Factor 3 1.730 8.920% 66.692%
Factor 4 1.281 5.122% 71.815%
Factor 5 1.059 4.238% 76.052%
Here is the result for the first run on the factor analysis (table 4-2):
Table 4-2 Rotated Factor Loading for Attractiveness of Game Design Elements 1st Run
Rotated Component Matrixa
Component
1 2 3 4 5
Story #02 .859 .145 .141 .084 .194
Story #01 .846 .203 .029 .014 .111
Story #04 .737 .255 .193 .158 .115
Story #03 .728 .189 .194 .265 .187
Story #05 .641 .237 .292 .282 .153
Sound & Music #04 .566 -.023 .318 .376 .262
Sound & Music #03 .502 .019 .481 .279 .345
Visual Presentation #04 .214 .749 .297 .214 .292
Visual Presentation #03 .156 .738 .317 .334 .177
Visual Presentation #02 .125 .728 .208 .298 .221
Visual Presentation #05 .295 .722 .233 .130 .244
Visual Presentation #01 .245 .714 .259 .130 .335
Interaction #01 .288 .309 .766 .145 .090
Interaction #04 .228 .325 .758 .067 .163
Interaction #02 .186 .444 .748 .186 .167
Interaction #03 .081 .391 .652 .302 .208
Control #01 .204 .238 .096 .842 .065
Control #03 .113 .234 .179 .819 .139
Control #02 .166 .363 .104 .814 .111
Sound & Music #02 .372 .006 .437 .555 .346
Sound & Music #01 .414 .047 .432 .537 .380
Character Setting #03 .047 .290 .219 .258 .736
Character Setting #02 .287 .364 .247 .142 .735
Character Setting #04 .361 .227 .039 .028 .702
Character Setting #01 .258 .441 .202 .178 .682
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
As shown in table 4-2, the number of factors is reduced from a total of six to five.
The “sound & music” factor is rejected and ruled out since all of the dimensions within the factor received a factor loading of less than 0.6, making them not significant for the test.
4.1.2 Factor analysis on attractiveness of game design element final run
After taking out the factor “sound & music” which received a factor loading of less than 0.6 for all its dimensions, a second and final run of factor analysis is performed, and the adjusted result shows an increase in the cumulative explained variance from 76.052% to 79.474%, as shown in table 4-3:
Table 4-3 Attractiveness of Game Design Element Eigenvalue and Explained Variance (Final Run)
Factor Eigenvalue Explained Variance Cumulative Explained Variance
Factor 1 10.830 51.571% 51.571%
Factor 2 2.003 9.540% 61.111%
Factor 3 1.575 7.501% 68.611%
Factor 4 1.270 6.046% 74.657%
Factor 5 1.012 4.817% 79.474%
And here is the result for the final (adjusted) run of the factor analysis (table 4-4):
Table 4-4 Rotated Factor Loading for Attractiveness of Game Design Element Final Run
Rotated Component Matrixa
Component
1 2 3 4 5
Visual Presentation #02 .787 .108 .194 .276 .196
Visual Presentation #01 .780 .226 .237 .104 .307
Visual Presentation #03 .756 .149 .325 .324 .172
Visual Presentation #05 .744 .286 .240 .115 .236
Visual Presentation #04 .739 .208 .321 .213 .300
Story #02 .152 .856 .133 .075 .198
Story #01 .152 .853 .058 .031 .141
Story #03 .102 .749 .238 .287 .234
Story #04 .248 .739 .204 .148 .126
Story #05 .168 .665 .324 .298 .188
Interaction #04 .232 .245 .806 .079 .205
Interaction #01 .277 .293 .773 .147 .104
Interaction #02 .406 .192 .767 .184 .184
Interaction #03 .246 .100 .730 .346 .278
Control #01 .154 .217 .151 .867 .116
Control #03 .180 .127 .208 .836 .171
Control #02 .328 .171 .133 .826 .137
Character Setting #03 .234 .060 .250 .258 .761
Character Setting #02 .353 .288 .250 .132 .741
Character Setting #04 .166 .359 .068 .050 .737
Character Setting #01 .402 .259 .229 .178 .703
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
An organized table with the factors, associated dimensions with their respective question contents and factor loadings is provided below (see table 4-5):
Table 4-5 Organized Table for Attractiveness of Game Design Element Constructs & Rotated Factor Loading
Factor Dimension Question Content Factor
Loading The character module design in the game is unique
and consistent. .787
The game delivers amazing style of visual arts. .780 The background design in the game is consistent. .756 The actions and expressions of the characters are
designed with great detail. .744
Factor 1
Visual Presentation
The equipments (armors & weapons) in the game have unique artistic designs that are visually
appealing. .739
The game offers a rich and intriguing story content. .856 The game delivers a storyline that clearly explains
the plot of the game. .853
The events in the game are consistent with one
another. .749
The game is expandable with lots of side stories &
quests to accomplish. .739
Factor 2 Story
The characters show development throughout the
progress of the game. .665
I am meet new friends easily in the game. .806 The players can conveniently team up with other
players for questing in the game. .773 The players can conveniently communicate with
other players in the game. .767
Factor 3 Interaction
The players can conveniently make in-game
transactions with other players in the game. .730 The control of the game is easy to handle. .867 The control of the game is easy to memorize. .836 Factor 4 Control
The control of the game is easy to learn. .826 The sets of equipments (weapons and armors) each
job class/race can wear are clearly different from
one another. .761
There are many techniques/ maigcs available to use
in the game. .741
The job classes/races are balanced in a way that no
one class/race is superior to another. .737 Factor 5 Character
Setting
There are many job classes/races to choose from in
the game. .703
4.2 Reliability Analysis on “Attractiveness of Game Design Element”, “Gamer