Measuring a Cross-Sectional Sample of Students' Intentions to Engage with Science and Modelling Associations According to Two Theoretical Perspectives

Autor/inn/en	Summers, Ryan; Wang, Shuai
Titel	Measuring a Cross-Sectional Sample of Students' Intentions to Engage with Science and Modelling Associations According to Two Theoretical Perspectives
Quelle	In: International Journal of Science Education, 45 (2023) 7, S.521-540 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Summers, Ryan) ORCID (Wang, Shuai)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0950-0693
DOI	10.1080/09500693.2023.2167243
Schlagwörter	Social Cognition; Theories; Student Attitudes; Intention; Rural Schools; Grade 5; Grade 6; Grade 7; Grade 8; Grade 9; Grade 10; Student Behavior; Student Participation; Science Education; North Dakota + Suchen Sie Ihr Suchwort? Soziale Kognition; Theory; Theorie; Schülerverhalten; Rural area; Rural areas; School; Schools; Ländlicher Raum; Schule; Schulen; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; School year 09; 9. Schuljahr; Schuljahr 09; Student behaviour; Schülermitarbeit; Schülermitwirkung; Studentische Mitbestimmung; Naturwissenschaftliche Bildung
Abstract	Social cognitive career theory (SCCT) and the theories of reasoned action and planned behaviour (TRAPB) have both guided studies seeking to explain students' intentions and decisions related to future science engagement. The purpose of this study was to simultaneously validate measures for these two social psychological models and closely examine these constructs and their associations. Students were sampled in two groups, across grades 5 through 10, from rural schools in the United States (N = 538). Each group was administered two sets of measures, and each set of measures was associated with a theoretical model, and included corresponding sub-scales suitable for self-report. Respondents also supplied information about a common set of predictor variables based on a review of the literature. Structural equation modelling was used to examine the constructs more closely. Results from confirmatory factor analysis were generally consistent with the theoretical structures. Multiple indicators multiple causes (MIMIC) models were generated to compare SCCT and TRAPB, including associations between factors and variables. Associations between common predictor variables like race and gender differed between the MIMIC models. Frequency of talk with family members, perceived science ability, gender, and race were all significantly associated with multiple factors in both the SCCT- and TRAPB-aligned models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01