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A factor analysis was performed on the
dataset, and 5 significant factors were extracted.
I was concerned that my overall result might
be skewed by an unrepresentative sampling of the player base,
but I reran the factor analysis for male players and female
players separately, and found no difference in the outcome.
I also reran the factor analysis for EQ and DAOC players separately,
and again found no difference in the outcome. Therefore, even
if I had the correct ratio of female players (closer to 14-16%)
or of DAOC players (about 1:3 EQ players), the factors would
still have been the same.
One thing to be clear about is that the 5
factors extracted are not 5 player types. It is not the case
that we have found evidence for an Achiever type or a Grief
type. The 5 factors are 5 different underlying motivations
for playing that are independent of each other. And in the
same way that a student can score high in both a Mathematical
and Verbal test, it is also the case that an EQ player can
score high on Achievement and Grief at the same time. The
appropriate way to think about these 5 factors is that each
gamer has a score for each factor, and that looking at all
5 scores allows us to understand a lot about why a particular
gamer plays the game. In a sense, they are facets of the same
core object - they each describe a different aspect of a person.
Again, don't think about these 5 factors as boxes to categorize
players in.
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