Investigating Bifactor Modeling of Biology Undergraduates' Task Values and Achievement Goals across Semesters

Autor/inn/en	Greene, Jeffrey A.; Bernacki, Matthew L.; Plumley, Robert D.; Kuhlmann, Shelbi L.; Hogan, Kelly A.; Evans, Mara; Gates, Kathleen M.; Panter, Abigail T.
Titel	Investigating Bifactor Modeling of Biology Undergraduates' Task Values and Achievement Goals across Semesters
Quelle	In: Journal of Educational Psychology, 115 (2023) 6, S.836-858 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Greene, Jeffrey A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0663
DOI	10.1037/edu0000803
Schlagwörter	Biology; Science Instruction; Student Attitudes; Learning Motivation; Measurement; Undergraduate Students; Academic Persistence; Goal Orientation; Factor Analysis; Structural Equation Models; Scores; Goodness of Fit; Outcomes of Education; STEM Careers; Academic Achievement + Suchen Sie Ihr Suchwort? Biologie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Schülerverhalten; Motivation for studies; Lernmotivation; Messverfahren; Zielorientierung; Zielvorstellung; Faktorenanalyse; Lernleistung; Schulerfolg; Schulleistung
Abstract	Undergraduate science, technology, engineering, and mathematics (STEM) students' motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students' expectancies and perceptions of value and cost (i.e., expectancy value theory [EVT]) and their goals (i.e., achievement goal theory [AGT]), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional confirmatory factor analysis, exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we measured undergraduate biology students' EVT and AGT motivations and investigated which measurement model best fit the data, and whether measurement invariance held, across three semesters. Having determined the best-fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results indicated a bifactor-ESEM model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT, with evidence of measurement invariance across semesters. Motivation factors, in particular attainment value and subjective task value, predicted small yet statistically significant amounts of variance in biology course outcomes each semester. Our findings provide support for using modern measurement models to capture students' STEM motivations and potentially refine conceptualizations of them. Such future research will enhance educators' ability to benevolently monitor and support students' motivation, and enhance STEM performance and career success. (As Provided).
Anmerkungen	American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01