Last week, we presented a handy reference chart of our Gamer Motivation Model based on our data from over 140,000 gamers. Here it is again for ease of reference.

Gamer Motivation Model (Overview)

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As we mentioned, motivations in the same column tend to be correlated, while motivations in different columns tend to be less correlated. But that doesn’t mean that each column is completely unrelated to each other. And indeed, some columns are more related than others. So wouldn’t it be nice to get a visual map of these motivations that could capture these relationships better? Luckily for us, there’s a statistical method for that.

Wouldn’t it be nice to get a visual map of these motivations?

Multidimensional Scaling (MDS) is a technique that compresses the distances between a set of variables into a 2D map while preserving the original distances as much as possible. Variables that are more correlated are put closer together, while variables that are less correlated are put further apart. When we applied this technique to the motivation data, we found a higher-level structure to the motivations.

Map of Gaming Motivations

At a high-level, 3 clusters of motivations emerged.

  • In the bottom-right orange cluster, there’s an Action-Social cluster that combines the interest in fast-paced gameplay with player interaction.
  • In the left yellow cluster, there’s an Immersion-Creativity cluster that combines the interest in narrative, expression, and world exploration.
  • In the top blue clusters, there’s a Mastery-Achievement cluster that combines the appeal of strategic gameplay, taking on challenges, and becoming powerful.

We found motivations that act as bridges between these major clusters.

  • Discovery is a bridge between the Immersion-Creativity cluster and the Mastery-Achievement cluster.
  • Power is a bridge between Action-Social and Mastery-Achievement.

We didn’t find a bridge between Immersion-Creativity and Action-Social. This map might be hinting to us that there should be something here. On the other hand, there’s no reason why motivations need to fall neatly into balanced models where everything is bridged.

Think of it as a proximity map.

What the map is showing is the relatedness between all these motivations. So if someone scores high on a particular motivation, they are more likely to score high on the nearby motivations.

The opposite isn’t true though. Motivations that are farther apart are independent of each other; they don’t suppress each other. For example, gamers can score high on Action-Social and high on Mastery-Achievement.

The axes point to 2 primary dimensions on which motivations vary.

The axes produced by Multidimensional Scaling are not always easy to interpret. In this case though, we think they point to two interesting dimensions.