Cross-cultural adaptation of the Science Motivation Questionnaire II (SMQ-II) for Portuguese-speaking Brazilian secondary school students
Adaptação transcultural do Science Motivation Questionnaire II (SMQ-II) para alunos do ensino médio de língua portuguesa do Brasil
Radu Bogdan Toma
,
Ayla Márcia Cordeiro Bizerra
,
Iraya Yánez
,
Jesús Ángel Meneses Villagrá
Revista Latinoamericana de Psicología, (2023), 55, pp. 109-119.
Received 9 October 2022
Accepted 11 April 2023
Introdução: A motivação científica é importante para a alfabetização científica dos estudantes. No entanto, há uma falta de ferramentas de medição válidas e confiáveis para o contexto brasileiro. Este estudo apresenta a versão em português brasileiro do Questionário de Motivação Científica (SMQ-II) e dados de base motivacionais. Método: O instrumento foi traduzido para o português brasileiro utilizando procedimentos de validação transcultural. Para construir provas de validade, as respostas de 646 alunos do ensino médio foram submetidas à análise exploratória e confirmatória de fatores, bem como invariância de medidas. Para a evidência de confiabilidade, foram calculados o alfa de Cronbach (α) e o ômega de McDonald’s (ω). A motivação dos estudantes foi analisada usando 2 (gênero) x 4 (notas) x 3 (modalidade de estudo) MANOVA. Resultados: 24 itens medindo a motivação intrínseca, motivação de carreira, motivação de grau e auto-eficácia suportaram uma estrutura de quatro fatores com confiabilidade adequada contra a estrutura original de cinco fatores (a autodeterminação não foi saliente). A invariância da medição foi estabelecida através de gênero e modalidade de estudo, mas não para o nível de nota. Os estudantes brasileiros de grau superior estavam menos motivados, e as meninas relataram maior motivação intrínseca e de carreira, mas menor auto-eficácia do que os meninos. Conclusão: Estas descobertas abrem o caminho para a avaliação da motivação científica dos estudantes brasileiros, mas também revelam problemas na estrutura latente do SMQ-II e exigem o desenvolvimento de instrumentos enraizados em teorias motivacionais contemporâneas.
Palabras chave:
Escola secundária, motivação intrínseca, motivação de carreira, motivação de série, auto-eficácia
Introduction: Science motivation is important for students’ scientific literacy. Yet, there is a lack of valid and reliable measurement tools for the Brazilian context. This study presents the Brazilian Portuguese version of the Science Motivation Questionnaire (SMQ-II) and motivational baseline data. Method: The instrument was translated into Brazilian Portuguese using cross-cultural validation procedures. For structural validity evidence, the responses of 646 secondary school students were subjected to exploratory and confirmatory factor analysis, as well as measurement invariance. For reliability evidence, Cronbach’s alpha (α) and McDonald’s omega (ω) were calculated. Students’ motivation was analysed using 2 (gender) x 4 (grade levels) x 3 (study modality) MANOVA. Results: 24 items measuring intrinsic motivation, career motivation, grade motivation, and self-efficacy supported a four-factor structure with adequate reliability against the original five-factor structure (self-determination was not salient). Measurement invariance was established across the gender and study modalities, but not for grade levels. Higher-grade level Brazilian students were less motivated, and girls reported higher intrinsic and career motivation, but lower self-efficacy than boys. Conclusion: These findings lay the foundation for the assessment of Brazilian students’ science motivation, although they also reveal problems in the latent structure of the SMQ-II and call for the development of instruments rooted in contemporary motivational theories.
Keywords:
Secondary school, intrinsic motivation, career motivation, grade motivation, self-efficacy
AERA, NCME, & APA. (2014). Standards for educational and psychological testing. American Psychological Association.
Aeschlimann, B., Herzog, W., & Makarova, E. (2016). How to foster students’ motivation in mathematics and science classes and promote students’ STEM career choice. A study in Swiss high schools. International Journal of Educational Research, 79, 31-41. https://doi.org/10.1016/j.ijer.2016.06.004
Anderman, E. M. (2020). Achievement motivation theory: Balancing precision and utility. Contemporary Educational Psychology, 61(April), 101864. https://doi.org/10.1016/j.cedpsych.2020.101864
Appianing, J., & Van Eck, R. N. (2018). Development and validation of the Value-Expectancy STEM assessment scale for students in higher education. International Journal of Stem Education, 5(24), 1-16. https://doi.org/10.1186/s40594-018-0121-8
Arbuckle, J. L. (2021). Amos (Version 28.0) [Computer Program]. IBM SPSS.
Ardura, D., & Pérez-Bitrián, A. (2018). The effect of motivation on the choice of chemistry in secondary schools: Adaptation and validation of the Science Motivation Questionnaire II to Spanish students. Chemistry Education Research and Practice, 19(3), 905-918. https://doi.org/10.1039/c8rp00098k
Ato, M., López, J. J., & Benavente, A. (2013). A classification system for research designs in psychology. Anales de Psicologia, 29(3), 1038-1059. https://doi.org/10.6018/analesps.29.3.178511
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1-26. https://doi.org/10.1146/annurev.psych.52.1.1
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191. https://doi.org/10.1097/00007632-200012150-00014
Bidegain, G., & Lukas Mujika, J. F. (2020). Exploring the relationship between attitudes toward science and PISA scientific performance. Revista de Psicodidáctica, 25(1), 1-12. https://doi.org/10.1016/j.psicoe.2019.08.002
Blalock, C. L., Lichtenstein, M. J., Owen, S., Pruski, L., Marshall, C., & Toepperwein, M. A. (2008). In pursuit of validity: A comprehensive review of science attitude instruments 1935-2005. International Journal of Science Education, 30(7), 961-977. https://doi.org/10.1080/09500690701344578
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge Taylor & Francis Group.
Carrasquilla, O. M., Pascual, E. S., & Roque, I. M. S. (2022). The gender gap in STEM Education. Revista de Educacion, (396), 149-172. https://doi.org/10.4438/1988-592X-RE-2022-396-533
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th edition). Routledge.
Ferrando, P. J., & Lorenzo-Seva, U. (2018). Assessing the quality and appropriateness of factor solutions and factor score estimates in exploratory item factor analysis. Educational and Psychological Measurement, 78(5), 762-780. https://doi.org/10.1177/0013164417719308
Ferrando, P. J., Lorenzo-Seva, U., Hernández-Dorado, A., & Muñiz, J. (2022). Decálogo para el Análisis Factorial de los Ítems de un Test [Decalogue for the factor analysis of test items]. Psicothema, 34(1), 7-17. https://doi.org/10.7334/psicothema2021.456
Fortus, D., & Vedder-Weiss, D. (2014). Measuring students’ continuing motivation for science learning. Journal of Research in Science Teaching, 51(4), 497-522. https://doi.org/10.1002/tea.21136
Gaskin, C. J., & Happell, B. (2014). On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511-521. https://doi.org/10.1016/j.ijnurstu.2013.10.005
Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159-1176. https://doi.org/10.1002/tea.20442
Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2007). Nonscience majors learning science: A theoretical model of motivaton. Journal of Research in Science Teaching, 44(8), 1088-1107. https://doi.org/10.1002/tea.20181
Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: Construct validation with nonscience majors. Journal of Research in Science Teaching, 46(2), 127-146. https://doi.org/10.1002/tea.20267
Guo, J., Parker, P. D., Marsh, H. W., & Morin, A. J. S. (2015). Achievement, motivation, and educational choices: A longitudinal study of expectancy and value using a multiplicative perspective. Developmental Psychology, 51(8), 1163–1176. https://doi.org/10.1037/a0039440
Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
Ha, M., Shin, S., & Lee, J.-K. (2016). Exploring the motivation for science learning of 3rd year high school students who chose different college majors from their track. Journal of the Korean Association for Science Education, 36(2), 317-324. https://doi.org/10.14697/jkase.2016.36.2.0317
Hayes, A. F., & Coutts, J. J. (2020). Use Omega rather than Cronbach’s Alpha for estimating reliability. But…. Communication Methods and Measures, 14(1), 1-24. https://doi.org/10.1080/19312458.2020.1718629
Komperda, R., Hosbein, K. N., Phillips, M. M., & Barbera, J. (2020). Investigation of evidence for the internal structure of a modified science motivation questionnaire II (mSMQ II): A failed attempt to improve instrument functioning across course, subject, and wording variants. Chemistry Education Research and Practice, 21(3), 893-907. https://doi.org/10.1039/d0rp00029a
Kosovich, J. J., Hulleman, C. S., Barron, K. E., & Getty, S. (2015). A practical measure of student motivation: Establishing validity evidence for the expectancy-value-cost scale in middle school. Journal of Early Adolescence, 35(5-6), 790-816. https://doi.org/10.1177/0272431614556890
Lloret-Segura, S., Ferreres-Traves, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). El análisis factorial exploratorio de los ítem: una guía práctica, revisada y actualizada. Anales de Psicología, 30(3), 1151-1169. https://doi.org/10.6018/analesps.30.3.199361
Lorenzo-Seva, U., & Ferrando, P. J. (2006). FACTOR: A computer program to fit the exploratory factor analysis model. Behavior Research Methods, 38(1), 88-91. https://doi.org/10.3758/BF03192753
Lupión-Cobos, T., Franco Mariscal, A. J., & Girón Gambero, J. R. (2019). Predictores de vocación en Ciencia y Tecnología en jóvenes: Estudio de caso sobre percepciones de alumnado de secundaria y la influencia de participar en experiencias educativas innovadoras. Revista Eureka Sobre Enseñanza y Divulgación de las Ciencias, 16(3), 3102. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2019.v16.i3.3102
Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877-907. https://doi.org/10.1002/sce.20441
Mundform, D. J., Shaw, D. G., & Tian, L. K. (2005). Minumum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159-168. https://doi.org/10.1207/s15327574ijt0502_4
O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, and Computers, 32(3), 396-402. https://doi.org/10.3758/bf03200807
OECD. (2019). PISA 2018 Results (Volume I): What Students Know and Can Do. https://doi.org/10.1787/5f07c754-en
Patil, V. H., Singh, S. N., Mishra, S., & Todd Donavan, D. (2008). Efficient theory development and factor retention criteria: Abandon the “eigenvalue greater than one” criterion. Journal of Business Research, 61(2), 162-170. https://doi.org/10.1016/j.jbusres.2007.05.008
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667-686. https://doi.org/10.1037/0022-0663.95.4.667
Potvin, P., & Hasni, A. (2014). Interest, motivation and attitude towards science and technology at K-12 levels: A systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85-129. https://doi.org/10.1080/03057267.2014.881626
Prasetya, A. T., & Ridlo, S. (2018). Factor analysis for instruments of science learning motivation and its implementation for the chemistry and biology teacher candidates. Journal of Physics: Conference Series, 983, 012168. https://doi.org/10.1088/1742-6596/983/1/012168
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
Salta, K., & Koulougliotis, D. (2015). Assessing motivation to learn chemistry: Adaptation and validation of Science Motivation Questionnaire II with Greek secondary school students. Chemistry Education Research and Practice, 16(2), 237-250. https://doi.org/10.1039/c4rp00196f
Salta, K., & Koulougliotis, D. (2020). Domain specificity of motivation: Chemistry and physics learning among undergraduate students of three academic majors. International Journal of Science Education, 42(2), 253-270. https://doi.org/10.1080/09500693.2019.1708511
Schmid, S., & Bogner, F. X. (2017). How an inquiry-based classroom lesson intervenes in science efficacy, career-orientation and self-determination. International Journal of Science Education, 39(17), 2342-2360. https://doi.org/10.1080/09500693.2017.1380332
Schumm, M. F., & Bogner, F. X. (2016). Measuring adolescent science motivation. International Journal of Science Education, 38(3), 434-449. https://doi.org/10.1080/09500693.2016.1147659
Taasoobshirazi, G., Heddy, B., Bailey, M. L., & Farley, J. (2016). A multivariate model of conceptual change. Instructional Science, 44(2), 125-145. https://doi.org/10.1007/s11251-016-9372-2
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed). Pearson Education.
Toma, R. B. (2020). Revisión sistemática de instrumentos de actitudes hacia la ciencia (2004-2016). Enseñanza de Las Ciencias, 38(3), 143–159. https://doi.org/10.5565/rev/ensciencias.2854
Toma, R. B., & Lederman, N. G. (2022). A comprehensive review of instruments measuring attitudes toward science. Research in Science Education, 52, 567-582. https://doi.org/10.1007/s11165-020-09967-1
Toma, R. B., & Meneses-Villagrá, J. Á. (2020). Development and validation of the SUCCESS instrument: Towards a valid and reliable measure of expectancies of success in school science. Current Psychology, 1-15. https://doi.org/10.1007/s12144-020-00958-z
Tosun, C. (2013). Adaptation of chemistry motivation questionnaire-II to Turkish: A validity and reliability study. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 15(1), 173-202.
Tuan, H. L., Chin, C. C., & Shieh, S. H. (2005). The development of a questionnaire to measure students’ motivation towards science learning. International Journal of Science Education, 27(6), 639-654. https://doi.org/10.1080/0950069042000323737
Velayutham, S., Aldridge, J., & Fraser, B. (2011). Development and validation of an instrument to measure students’ motivation and self-regulation in science learning. International Journal of Science Education, 33(15), 2159-2179. https://doi.org/10.1080/09500693.2010.541529
Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081-1121. https://doi.org/10.3102/0002831213488622
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219-246. https://doi.org/10.1177/0095798418771807
Widaman, K. F., & Revelle, W. (2023). Thinking thrice about sum scores, and then some more about measurement and analysis. Behavior Research Methods, 55, 788–806. https://doi.org/10.3758/s13428-022-01849-w
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of motivation. Contemporary Educational Psychology, 25, 68-81.
Wigfield, A., & Eccles, J. S. (2020). 35 years of research on students’ subjective task values and motivation: A look back and a look forward. In Andrew J. Elliot (Ed.), Advances in Motivation Science (pp. 161-198). Elsevier Inc. https://doi.org/10.1016/bs.adms.2019.05.002
Yamamura, S., & Takehira, R. (2017). Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling. Journal of Educational Evaluation for Health Professions, 14(2). https://doi.org/10.3352/jeehp.2017.14.2