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A computer adaptive measure of reading motivation
Author(s) -
Davis Marcia H.,
Wang Wenhao,
Kingston Neal M.,
Hock Michael,
Tonks Stephen M.,
Tiemann Gail
Publication year - 2020
Publication title -
journal of research in reading
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.077
H-Index - 51
eISSN - 1467-9817
pISSN - 0141-0423
DOI - 10.1111/1467-9817.12318
Subject(s) - psychology , reading (process) , curiosity , reading motivation , prosocial behavior , preference , developmental psychology , goal theory , multilevel model , social psychology , statistics , mathematics , political science , law
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.