A computer adaptive measure of reading motivation

Background: The importance of reading motivation has led to the development of a large number of self-report reading motivation measures; however, there is still a need for a usable measure of adolescent reading motivation that captures a large number of theoretically and empirically distinct constructs. Methods: The current paper details the development and validation of a computer adapted measure of reading motivation, the Adaptive Reading Motivation Measure (ARMM), which assesses constructs of curiosity, involvement, interest, value, challenge, grades, recognition, competition, avoidance, self-efficacy, perceived difficulty, preference for autonomy, social motivation, prosocial goals, and antisocial goals for reading. Results: Model fit indicated that hierarchical multidimensional models fit better than models without a hierarchical structure . The validation results indicate that females scored higher than males and younger students scored higher than older students on most ARMM scores when scores were derived using a higher-order model. In addition, these scores correlated significantly to reading behavior, engagement, and achievement and indicated high reliability. Conclusions: The findings suggest that the ARMM would be a valid measure to assess a large number of reading motivation constructs in a short period of time within a classroom setting.