Cross-cultural adaptation of the Science Motivation Questionnaire II (SMQ-II) for Portuguese-speaking Brazilian secondary school students

  1. Radu Bogdan Toma 1
  2. Ayla Márcia Cordeiro Bizerra 1
  3. Iraya Yánez 2
  4. Jesús Ángel Meneses Villagrá 1
  1. 1 Universidad de Burgos, Burgos
  2. 2 Instituto Federal de Educação
Revista:
Revista Latinoamericana de Psicología

ISSN: 0120-0534

Año de publicación: 2023

Volumen: 55

Número: 0

Páginas: 109-119

Tipo: Artículo

DOI: 10.14349/RLP.2023.V55.13 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Revista Latinoamericana de Psicología

Resumen

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 (a) e o ômega de McDonald’s (w). 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.

Referencias bibliográficas

  • 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
  • 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
  • Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press 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/10.1080/19 312458.2020.1718629
  • International Journal of Testing, 5(2), 159-168. https://doi.org/10.1207/s15327574ijt0502_4
  • 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 de grees 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.
  • 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/10.1080/03057267.20 14.881626
  • 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/10.1080/09 500693.2019.1708511
  • 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/10.1080/09500693.20 17.1380332
  • 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/10.108 0/09500693.2010.541529
  • 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