A derived dimension is similar to a scale on a personality test. However, a scale is a direct measure of a particular construct, such as openness to experience or extroversion. In contrast, a derived dimension is “derived” statistically using a formula that has been shown by previous research to predict scores in this dimension.
More simply put: A group of candidates completes the Identity Questionnaire and the Belbin Team Roles Inventory. The publisher then statistically examines the relationship between scores on various Identity scales and team role preferences. An equation is established that enables prediction of team role preferences from Identity scale scores. In the future, respondents do not actually complete the Team Roles Inventory, instead, their Identity scales are used to predict how they are likely to score on the inventory if they were to complete it.
The benefits of this are related to time and cost savings. However, the con is that test users must be cautious in the interpretation of derived dimensions. As we know, there is error associated with any selection method, including psychometric tests. When using derived dimensions, we are actually correlating potential error with potential error and thus our results (and interpretations) may be less accurate than if we were to take a direct measure of the construct or derived dimension.