Multidimensional scaling: A technique for visualization and perception análisis
DOI:
https://doi.org/10.22458/rc.v27i1.4616Keywords:
multidimensional scaling, perceptual mapping, multivariate analysis, multiple regression, secondary school curriculumAbstract
In education and in other areas of the social sciences, studies are carried out of the perception of a group of people about a certain phenomenon under study and it is necessary to use specialized techniques for the analysis of this perception. The purpose of this article is to present the technique of multidimensional scaling as an option for analyzing perceptions and exemplify the use of perceptual maps, product of this technique, in studies where you want to capture the perception of a group of teachers and people in general. To do this, it explains what this statistical technique consists of, and as an example, it is applied to a set of data that include two scales that measure the perception of 288 mathematics teachers, in terms of the degree of implementation of a study program. The main conclusion reached is that the statistical tool of multidimensional scaling is appropriate to analyze the perceptions and opinions of teachers, this due to the ease of expressing these perceptions in graphic representations that show different dimensions (Bord et al., 2018). Multiple linear regression on the two axes of the perceptual maps is recommended to improve its interpretation.
References
Bord, I., Groenen, P. & Mair, P. (2018). Applied Multidimensional Scaling and Unfolding. Second Ed, Springer. https://doi.org/10.1007/978-3-319-73471-2
DeVellis, R. & Thorpe, C. (2021). Scale development: Theory and applications. Fifth Ed, Sage.
Estado de la Nación (2020). Estado de la Nación. Bases de datos del Estado de la Nación. Recuperado el 02 julio de 2020 de https://estadonacion.or.cr/base-datos/.
George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference. 16th ed. Routledge. https://doi.org/10.4324/9780429056765
Gil, J. (1993). La posición del profesorado ante el cambio educativo. Un escalamiento multidimensional no métrico de los discursos sobre la reforma. Revista de Investigación Educativa, 21(1), 67-82. Recuperado de https://revistas.um.es/rie/article/view/136531/124141
Guisande, C., Vaamonde, A. & Barreiro, A. (2013). Temas de Análisis Estadístico Multivariante. San José: EUCR.
Härdle, W.K., Simar, L. (2019). Applied Multivariate Statistical Analysis. Springer Cham. https://doi.org/10.1007/978-3-030-26006-4
Hernández, O. (2013). Temas de Análisis Estadístico Multivariante. San José: EUCR.
Hidalgo, P., Manzur, E., Olavarrieta, S. y Farias, P. (2007). Cuantificación de las distancias culturales entre países: un análisis de Latinoamérica. Cuadernos De Administración, 20(33). Recuperado de
https://revistas.javeriana.edu.co/index.php/cuadernos_admon/article/view/4096
IBM Corp. (2020). IBM SPSS Categories 28. SPSS Inc. Recuperado de https://www.ibm.com/docs/en/SSLVMB_28.0.0/pdf/es/IBM_SPSS_Categories.pdf
Kruskal, J. B. (1964). Nonmetric Multidimensional Scaling: A Numerical Method. Psychometrika, 2,123-162.
Lentini, V. & Villalobos, G. (2015). Condiciones en que se aplica la reforma curricular de Matemáticas en colegios públicos diurnos, según los docentes. Quinto informe del Estado de la Educación, 357-365.
Mair, P., Borg, I., & Rusch, T. (2016). Goodness-of-Fit Assessment in Multidimensional Scaling and Unfolding. Multivariate Behavioral Research, 51, 772-789.
Ruiz, A. (2014). La implementación de los programas oficiales de matemáticas. 5to Informe del Estado de la Educación, CONARE. Recuperado de:
Trejos, J., Castillo, W. & González, J (2014). Análisis Multivariado de Datos: Métodos y Aplicaciones. San José: EUCR.
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