Factor Analysis Results

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.

Copyright, March 2002, by Nicholas Yee.

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