Organizational and contextual support for the promotion of STEM vocations. Psychometrics of a measurement scale
DOI:
https://doi.org/10.33975/riuq.vol35n1.1229Keywords:
STEM, psychometric properties, organizational, contextual supportAbstract
Introduction: The objective of the study was the psychometric validation of a self-made scale to measure the perception of organizational and contextual support for the promotion of STEM disciplines (science, technology, engineering, and mathematics for its acronym) for high-level student’s upper middle.
Method: The questionnaire was administered to paper and pencil anonymously and each participant gave their informed consent at the beginning of the questionnaire. A deterministic sampling was carried out on 390 students who met the inclusion criteria.
Results: For content validity, experts were consulted on the subject, and construct validity was estimated by performing an exploratory factor analysis (EFA), using the maximum likelihood factor extraction method with direct oblimin rotation, and reporting loads. factorials greater than 0.5. To validate the theoretical model, a Confirmatory Factor Analysis (CFA) was carried out in the same way, which allowed demonstration of the validity of the previously obtained structure, but with adjustments. The fit indicators of the measurement model were estimated (χ2= 15.20, gl= 8, p > 0.055, SRMR=0.05, AGFI=0.96, RMSEA 0.04 IC90[0.00-0.08], TLI=0.98, and CFI=0.99). , whose values obtained, as well as those of reliability, are considered acceptable according to the standards reported in the literature.
Discussion or Conclusion: The measurement model is corroborated with adjustments to the theoretical structure according to what is reported in the adjustment indicators of both exploratory and confirmatory factor analysis. The results present an important contribution in the measurement of the elements that contribute to the promotion of vocations in STEM disciplines. From a methodological perspective, a consistent tool is proposed for the measurement of the defined constructs.
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References
Aschbacher, P. R., Ing, M., & Tsai, S. M. (2014). Is science me? exploring middle school students’ STEM career aspirations. Journal of Science Education and Technology, 23(6), 735–743. https://doi.org/10.1007/s10956-014-9504-x
Avendaño, K., Magaña, D., & Aguilar, N. (2017). Análisis Factorial Exploratorio del cuestionario interés por estudios universitarios en áreas STEM (I-STEM). Revista de Análisis Cuantitativo y Estadístico, 4(13), 54–68.
Avendaño-Rodríguez, K. C. (2018). Interés por estudios universitarios en Ciencia, Tecnología, Ingeniería y Matemáticas (STEM) en bachilleres de Tabasco [Tesis doctoral]. Universidad Juárez Autónoma de Tabasco. https://ri.ujat.mx/bitstream/20.500.12107/3106/3/1-5-Tesis Avendano Rodriguez %28Gen 2015-2018%29.pdf
Bahia, S., Janeiro, I., & Duarte, R. (2007). Personal and contextual factors in the construction of acting careers. Electronic Journal of Research in Educational Psychology, 5(11), 57–74.
Batista-Foguet, J. M., Coenders, G., & Alonso, J. (2004). Confirmatory factor analysis. Its role on the validation of health-related questionnaires. Medicina Cllínica, 122(Suppl 1), 21–27. https://doi.org/10.1157/13057542
Blanco, Á. (2009). El modelo cognitivo social del desarrollo de la carrera: Revisión de más de una década de investigación empírica. Revista de Educacion, 350, 423–445. https://sede.educacion.gob.es/publiventa/d/23280/19/1
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. En G. A. Marcoulides (Ed.), Methodology for business and management. Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
Cupani, M. (2012). Análisis de Educaciones Estructurales: conceptos, etapas de desarrollo y un ejemplo de aplicación. Revista Tesis, 1, 186–199. https://revistas.unc.edu.ar/index.php/tesis/article/download/2884/2750
Deemer, E. D., Marks, L. R., & Miller, K. A. (2017). Peer science self-efficacy: a proximal contextual support for college students’ science career intentions. Journal of Career Assessment, 25(3), 537–551. https://doi.org/10.1177/1069072716651620
Dong, Y., Xu, C., Song, X., Fu, Q., Chai, C. S., & Huang, Y. (2019). Exploring the Effects of Contextual Factors on In-Service Teachers’ Engagement in STEM Teaching. Asia-Pacific Education Researcher, 28(1), 25–34. https://doi.org/10.1007/s40299-018-0407-0
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Elosua, P. (2003). Sobre la validez de los test. Psichotema, 15(2), 315–321.
Gaskin, J., James, M., and Lim, J. (2019). Master Validity Tool, [AMOS Plugin]. http://statwiki.kolobkreations.com/
Goodwin, R., Costa, P., & Adonu, J. (2004). Social support and its consequences: “positive” and “deficiency” values and their implications for support and self-esteem. The British journal of social psychology 43(Pt 3), 465–474. https://doi.org/10.1348/0144666042038006
Gudiño-Paredes, S. (2018). Innovating science teaching with a transformative learning model. Journal of Education for Teaching, 44(1), 107–111. https://doi.org/10.1080/02607476.2018.1422619
Heaverlo, C. A., Cooper, R., & Lannan, F. S. (2013). STEM development: Predictors for 6th-12th grade girls’ interest and confidence in science and math. Journal of Women and Minorities in Science and Engineering, 19(2), 121–142. https://doi.org/10.1615/JWomenMinorScienEng.2013006464
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hernández-Mena, V. (2021). Apoyo social, autoeficacia y expectativas de resultado: factores asociados al interés por estudios universitarios en CTIM en alumnos de instituciones educativas rurales [Tesis doctoral]. Universidad Juárez Autónoma de Tabasco. https://ri.ujat.mx/bitstream/20.500.12107/3522/1/Tesis_Veronica_Hernandez_Mena.pdf
Holland, J. L. (1996). Exploring careers with typology. American Psychologist, 51(4), 397–406. https://doi.org/10.1037/0003-066X.51.4.397
IBM (2015). IBM – SPPS Amos (version 23) [software]. IBM.
IBM (2017). IBM – SPPS Statistics (version 25) [software]. IBM.
JASP Team (2023). JASP (Version 0.17.1) [software].Universidad of Amsterdam.
Jiménez Leon, R., Magaña Medina, D. E., & Aquino Zúñiga, S. P. (2021). Gestión de tendencias STEM en educación superior y su impacto en la industria 4.0. Journal of the Academy, 5, 99–121. https://doi.org/10.47058/joa5.7
Jiménez-León, R. (2021). Narrativas de elección por carreras en Ciencia, Tecnología, Ingeniería y Matemáticas. Prospectiva Estudiantil. [Tesis doctoral]. Universidad Juárez Autónoma de Tabasco. https://ri.ujat.mx/handle/20.500.12107/3607
Kantamneni, N., McCain, M. R. C., Shada, N., Hellwege, M. A., & Tate, J. (2018). Contextual Factors in the Career Development of Prospective First-Generation College Students: An Application of Social Cognitive Career Theory. Journal of Career Assessment, 26(1), 183–196. https://doi.org/10.1177/1069072716680048
Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The Development of the STEM Career Interest Survey ( STEM-CIS ). Research in Science Education, 44(3), 461-481. https://doi.org/10.1007/s11165-013-9389-3
King, M. F., & Bruner, G. C. (2000). Social Desirability Bias : A Neglected Aspect of Validity Testing Social Desirability Bias : A Neglected Aspect of Validity Testing. Psychology & Marketing, 17(2), 79–103. https://doi.org/10.1002/(SICI)1520-6793(200002)17
Ledesma, R. (2008). Introducción al Bootstrap . Tutorials in Quantitative Methods for Psychology, 4(2), 51–60. https://doi.org/10.20982/tqmp.04.2.p051
Lent, R. W. ., Brown, S. D. ., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/https://doi.org/10.1006/jvbe.1994.1027
Leung, S. A. (2008). The Big Five Career Theories. En J. A. Athanasou & R. Van Esbroeck (Eds.), International Handbook of Career Guidance (pp. 115–132). SpringerLink. https://doi.org/10.1007/978-1-4020-6230-8_6
Littlewood, H. F., & Bernal, E. R. (2014). Mi primer modelamiento de ecuaciones estructurales (2ª ed.). Autor.
Lund, T. J., & Stains, M. (2015). The importance of context: an exploration of factors influencing the adoption of student-centered teaching among chemistry, biology, and physics faculty. International Journal of STEM Education, 2(1). https://doi.org/10.1186/s40594-015-0026-8
Magaña, D., Aguilar, N., Vázquez, J. M., & Zetina, C. D. (2016). Propiedades psicométricas de un instrumento para medir las condiciones percibidas para la formación científica en estudiantes de pregrado. Revista del Congrés Internacional de Docència Universitària i Innovació (CIDUI), 3, 861–877. http://www.cidui.org/revistacidui/index.php/cidui/article/view/900/861
Manzano, A., & Zamora, S. (2010). Sistema de ecuaciones estructurales: 4. Centro Nacional de Evaluación para la Educación Superior, A.C.
Mitchell, P. T. (2016). Undergraduate motivations for choosing a science , technology , engineering , or mathematics ( STEM ) major [University of Tennessee]. http://trace.tennessee.edu/utk_chanhonoproj/1907
Mohtar, L. E., Halim, L., & Rahman, N. A. (2019). A Model of Interest in Stem Careers Among Secondary. Journal of Baltic Science Education, 18(3), 404–416.
Morales, F. ( 2022, 4 de mayo). Las 10 carreras profesionales mejor pagadas en México en 2022 ( y las de menor salario). El economista. https://www.eleconomista.com.mx/capitalhumano/Las-10-carreras-profesionales-mejor-pagadas-en-Mexico-en-2022-y-las-de-menor-salario-20220503-0122.html
Nadelson, L. S., & Seifert, A. L. (2017). Integrated STEM defined: contexts, challenges, and the future. Journal of Educational Research, 110(3), 221–223. https://doi.org/10.1080/00220671.2017.1289775
Osipow, S. H. (1990). Convergence in theories of career choice and development: Review and prospect. Journal of Vocational Behavior, 36(2), 122–131. https://doi.org/10.1016/0001-8791(90)90020-3
Otzen, T., & Manterola, C. (2017). Técnicas de Muestreo sobre una Población a Estudio. International Journal of Morphology, 35(1), 227–232. https://doi.org/10.4067/S0717-95022017000100037
Pantoja, L. F., Peña, J. M., & Mendoza, C. P. (2020). Desarrollo de habilidades STEM en media superior como mecanismo para impulsar la continuidad en educación superior: Caso programa Bases de Ingeniería. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 10(20). https://doi.org/10.23913/ride.v10i20.614
Perna, L., Lundy-Wagner, V., Drezner, N. D., Gasman, M., Yoon, S., Bose, E., & Gary, S. (2009). The contribution of HBCUS to the preparation of african american women for stem careers: a case study. Research in Higher Education, 50(1), 1–23. https://doi.org/10.1007/s11162-008-9110-y
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 6(99), 323–338. https://doi.org/10.3200/JOER.99.6.323-338
Thibaut, L., Knipprath, H., Dehaene, W., & Depaepe, F. (2018). The influence of teachers’ attitudes and school context on instructional practices in integrated STEM education. Teaching and Teacher Education, 71, 190–205. https://doi.org/10.1016/j.tate.2017.12.014
Urrutia Egaña, M., Barrios Araya, S., Gutiérrez Núñez, M., & Mayorga Camus, M. (2015). Métodos óptimos para determinar validez de contenido. Revista Cubana de Educacion Medica Superior, 28(3), 547–558.
Valdés-Cuervo, A. A., García-Vázquez, F., Torres-Acuña, G. M., Urías-Murrieta, M., & Grijalva-Quiñonez, C. S. (2019). Medición en Investigación Educativa con Apoyo del SPSS y el AMOS. Instituto Tecnológico de Sonora.
Vázquez-Alonso, Á., & Manassero-Mas, M. A. (2015). La elección de estudios superiores científico-técnicos: Análisis de algunos factores determinantes en seis países. Revista Eureka, 12(2), 264–277. https://doi.org/10.498/17251
Ventura-León, J. L., & Caycho-Rodríguez, T. (2017). El coeficiente Omega: un método alternativo para la estimación de la confiabilidad. Revista Latinoamericana en Ciencias Sociales, Niñez y Juventud, 15(1), 625–627. https://doi.org/10.11600/1692715x.13110020813.Campo-Arias
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices Mr. Journal of emergency primary health care, 8(3), 1–13.
World Economic Forum. (2020). The future of jobs report 2020 | world economic forum. The Future of Jobs Report, October 1163. https://www.weforum.org/reports/the-future-of-jobs-report-2020/digest
Zhao, J., Wijaya, T. T., Mailizar, M., & Habibi, A. (2022). Factors influencing student satisfaction toward STEM education: Exploratory study using structural equation modeling. Applied Sciences (Switzerland), 12(19), 1–20. https://doi.org/10.3390/app12199717
ZOOM (2022). Plataforma de video conferencia (versión 5.13). Zoom video communications Inc.
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