Factor Structure of Student Science-Learning Motivation: Evidence from TIMSS U.S. Data

Fa Zhang (), Christine L. Bae, Michael D. Broda and Alison C. Koenka
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Fa Zhang: Department of Education, Open University of China, Beijing 100039, China
Christine L. Bae: Department of Foundations of Education, Virginia Commonwealth University, Richmond, VA 23284, USA
Michael D. Broda: Department of Foundations of Education, Virginia Commonwealth University, Richmond, VA 23284, USA
Alison C. Koenka: Jeannine Rainbolt College of Education, The University of Oklahoma, Norman, OK 73019, USA

Sustainability, 2023, vol. 15, issue 17, 1-15

Abstract: We investigated the structure of science motivation among a nationally representative sample of grade 8 students in the United States from the TIMSS dataset. Drawing on an integrative conceptual model of motivation, the following constructs from situative expectancy-value theory (SEVT) and self-determination theory (SDT) were examined through confirmatory factor analyses, including self-concept, task value (intrinsic and utility value), and sense of relatedness, to test the underlying factor structure of motivation. Internal validity evidence was established, which showed that a four-factor model fits the data significantly better than a three-factor model and one-factor model. This study contributes to the current literature by providing evidence of the four dimensions of motivation in a manner that is theoretically aligned with SEVT and SDT, and validated using the TIMSS dataset. The student’s sense of relatedness as a key interpersonal facet of learning motivation is highlighted in this study.

Keywords: situative expectancy-value theory; integrative approach; model testing; self-determination theory; science-learning motivation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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