A computer adaptive measure of reading motivation. (23rd July 2020)

• Record Type: Journal Article
• Authors: Davis, Marcia H.; Wang, Wenhao; Kingston, Neal M.; Hock, Michael; Tonks, Stephen M.; Tiemann, Gail
• Is Part Of: Journal of research in reading. Volume 43:Number 4(2020)
• Page Start: 434
• Page End: 453
• Publication Date: 2020-07-23
• Subjects: reading motivation -- measurement -- adolescents -- validity -- computer adaptive; Reading -- Research -- Periodicals; Reading -- Periodicals; 418.4
• DOI: 10.1111/1467-9817.12318
• Languages: English
• ISSNs: 0141-0423

Abstract

Abstract : 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 men 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 behaviour, 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.