A Computer Adaptive Measure of Reading Motivation

Autor/inn/en	Davis, Marcia H.; Wang, Wenhao; Kingston, Neal M.; Hock, Michael; Tonks, Stephen M.; Tiemann, Gail
Titel	A Computer Adaptive Measure of Reading Motivation
Quelle	(2020), (38 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Davis, Marcia H.) ORCID (Wang, Wenhao) ORCID (Kingston, Neal M.) ORCID (Hock, Michael) ORCID (Tonks, Stephen M.) ORCID (Tiemann, Gail) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Reading Motivation; Computer Assisted Testing; Adaptive Testing; Measures (Individuals); Adolescents; Test Construction; Test Validity; Gender Differences; Age Differences; Test Reliability; Public Schools; Elementary School Students; Secondary School Students; Achievement Tests; Reading Achievement; Reading Habits; Mathematics Achievement; Measures of Academic Progress + Suchen Sie Ihr Suchwort? Lesemotivation; Messdaten; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Testaufbau; Testvalidität; Geschlechterkonflikt; Age; Difference; Age difference; Altersunterschied; Testreliabilität; Public school; Öffentliche Schule; Sekundarschüler; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Leseleistung; Reading habit; Lesegewohnheit; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz
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 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. [The paper will be published in "Journal of Research in Reading."] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11