Analyzing data from a fuzzy rating scale-based questionnairea case study

  1. Gil Alvarez, María Angeles 1
  2. Lubiano Gómez, María Asunción 1
  3. Rosa de Sáa, Sara de la 1
  4. Sinova Fernández, Beatriz 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Psicothema

ISSN: 0214-9915

Año de publicación: 2015

Volumen: 27

Número: 2

Páginas: 182-191

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Psicothema

Resumen

Antecedentes: la escala de valoración difusa se introdujo para abordar la imprecisión inherente al pensamiento humano y la experiencia al medir actitudes en muchos campos de la Psicología. La flexibilidad y expresividad de esta escala permiten describir apropiadamente las respuestas a la mayoría de las cuestiones que involucran mediciones psicológicas. Método: analizar las respuestas a cuestionarios basados en dicha escala supone un problema crítico. No obstante, en los últimos años se está desarrollando una metodología específica para el análisis estadístico de datos difusos que explota toda la información disponible. En este trabajo se recoge un resumen de los procedimientos más relevantes. Resultados: los métodos se ilustrarán mediante su aplicación a los datos de un estudio realizado con niños de nueve años. En él, los niños han respondido a algunas cuestiones del conocido cuestionario TIMSS/PIRLS recurriendo a un formulario basado en la escala de valoración difusa y en formato impreso o digital. Conclusiones: en primer lugar, el estudio muestra que los requisitos previos de formación y entrenamiento para cumplimentar tal formulario son poco exigentes. En segundo lugar, se verifica que a menudo las conclusiones estadísticas difieren sustancialmente dependiendo de que las respuestas se den según escala Likert o de valoración difusa.

Referencias bibliográficas

  • Agarwal, S., & Agarwal, P. (2005). A Fuzzy Logic approach to search results’ personalization by tracking user’s web navigation pattern and Psychology. In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence-ICTAI’05 (pp. 318-325).
  • Ávila-Muñoz, A.M., & Sánchez-Sáez, J.M. (2014). Fuzzy sets and prototype theory representational model of cognitive community structures based on lexical availability trials. Review of Cognitive Linguistics, 12, 133-159.
  • Bělohlávek, R., & Klir, G.J. (Eds.) (2011). Concepts and Fuzzy Logic. Cambridge, USA: The MIT Press.
  • Bělohlávek, R., Klir, G.J., Lewis III, H.W., & Way, E. (2002). On the capability of Fuzzy Set Theory to represent concepts. International Journal of General Systems, 31, 569-585.
  • Bělohlávek, R., Klir, G.J., Lewis III, H.W., & Way, E. (2009). Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights. International Journal of Approximate Reasoning, 51, 23-34.
  • Bertoluzza, C., Corral, N., & Salas, A. (1995). On a new class of distances between fuzzy numbers. Mathware & Soft Computing, 2, 71-84.
  • Blanco-Fernández, A., Casals, M.R., Colubi, A., Corral, N., García- Bárzana, M., Gil, M.A., González-Rodríguez, G., López, M.T., Lubiano, M.A., Montenegro, M., Ramos-Guajardo, A.B., De la Rosade Sáa, S., & Sinova, B. (2014). A distance-based statistical analysis of fuzzy number-valued data. International Journal of Approximate Reasoning, 55, 1487-1501. Rejoinder. International Journal of Approximate Reasoning, 55, 1601-1605.
  • Castillo, I., Tomás, I., Ntoumanis, N., Bartholomew, K., Duda, J.L., & Balaguer, I. (2014). Psychometric properties of the Spanish version of the Controlling Coach Behaviors Scale in the sport context. Psicothema, 26, 409-414.
  • Coppi, R., Giordani, P., & D’Urso, P. (2006). Component models for fuzzy data. Psychometrika, 71, 733-761.
  • Corral-Blanco, N., Zurbano-Fernández, E., Blanco-Fernández, A., García- Honrado, I., & Ramos-Guajardo, A.B. (2013). Structure of the family educational environment: Its influence on performance and differential performance. In PIRLS-TIMSS 2011 International Study on Progress in Reading Comprehension, Mathematics and Sciences IEA. Volume II. Spanish Report. Secondary Analysis. Ministerio de Educación, Cultura y Deporte, Instituto Nacional de Evaluación Educativa (pp. 9-31).
  • De la Rosade Sáa, S., Gil, M.A., González-Rodríguez, G., López, M.T., & Lubiano, M.A. (2015). Fuzzy rating scale-based questionnaires and their statistical analysis. IEEE Transactions on Fuzzy Systems, 23, 111-126.
  • Diamond, P., & Kloeden, P. (1990). Metric spaces of fuzzy sets. Fuzzy Sets and Systems, 35, 241-249.
  • Dubois, D., & Prade, H. (1979). Fuzzy real algebra: Some results. Fuzzy Sets and Systems, 2, 327-348.
  • Garriga Trillo, A.J., & Dorn, T. (1991). Medición de la borrosidad: modalidades cruzadas. Psicothema, 3, 423-432.
  • Giné, E., & Zinn, J. (1990). Bootstrapping general empirical measures. Annals of Probability, 18, 851-869.
  • González-Rodríguez, G., Colubi, A., & Gil, M.A. (2012). Fuzzy data treated as functional data. A one-way ANOVA test approach. Computational Statistics & Data Analysis, 56, 943-955.
  • González-Rodríguez, G., Montenegro, M., Colubi, A., & Gil, M.A. (2006). Bootstrap techniques and fuzzy random variables: Synergy in hypothesis testing with fuzzy data. Fuzzy Sets and Systems, 157, 2608-2613.
  • Ghneim, J. (Ed.) (2013). Fuzzy concept. In Encyclopedia of Psychometrics, pp. 61-68 - Wikipedia. October 27, 2014, URL: http://en.wikipedia.org/ wiki/Fuzzy_concept.
  • Hesketh, B., Griffi n, B., & Loh, V. (2011). A future-oriented retirement transition adjustment framework. Journal of Vocational Behavior, 79, 303-314.
  • Hesketh, B., Hesketh, T., Hansen, J.-I., & Goranson, D. (1995). Use of fuzzy variables in developing new scales from the strong interest inventory. Journal of Counseling Psychology, 42, 85-99.
  • Hesketh, T., & Hesketh, B. (1994). Computerized fuzzy ratings: The concept of a fuzzy class. Behavior Research Methods, Instruments & Computers, 26, 272-277.
  • Hesketh, T., Pryor, R., & Hesketh, B. (1988). An application of a computerized fuzzy graphic rating scale to the psychological measurement of individual differences. International Journal of Man- Machine Studies, 29, 21-35.
  • Horowitz, L.M., & Malle, B.M. (1993). Fuzzy concepts in psychotherapy research. Psychotherapy Research, 3, 131-148.
  • Horvath, J.M. (1988). A fuzzy set model of learning disability: identifi cation from clinical data. In T. Zétényi (Ed.), Fuzzy Sets in Psychology. Series: Advances in Psychology, vol. 56 (pp. 345-382). Amsterdam: North- Holland, Elsevier.
  • Leding, J.K. (2013). Need for cognition is related to the rejection (but not the acceptance) of false memories. American Journal of Psychology, 126, 1-10.
  • Lozano, L.M., García-Cueto, E., & Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology, 4, 73-79.
  • Lubiano, M.A., & Gil, M.A. (1999). Estimating the expected value of fuzzy random variables in random samplings from finite populations. Statistical Papers, 40, 277-295.
  • Lubiano, M.A., Gil, M.A., López-Díaz, M., & López, M.T. (2000). The lambda-mean squared dispersion associated with a fuzzy random variable. Fuzzy Sets and Systems, 3, 307-317.
  • Montenegro, M., Casals, M.R., Lubiano, M.A., & Gil, M.A. (2001). Two-sample hypothesis tests of means of a fuzzy random variable. Information Sciences, 133, 89-100.
  • Montenegro, M., Colubi, A., Casals, M.R., & Gil, M.A. (2004). Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable. Metrika, 59, 31-49.
  • Montenegro, M., López-García, M.T., Lubiano, M.A., & González- Rodríguez, G. (2009). A dependent multi-sample test for fuzzy means. In Abstracts 2nd Workshop ERCIM WG Computing & Statistics (p. 102).
  • Nakama, T., Colubi, A., & Lubiano, M.A. (2010). Factorial analysis of variance for fuzzy data. In Abstracts 3rd Workshop ERCIM WG Computing & Statistics (p. 88).
  • Osherson, D.N., & Smith, E.E. (1981). On the adequacy of prototype theory as a theory of concepts. Cognition, 9, 35-58.
  • Osherson, D.N., & Smith, E.E. (1982). Gradedness and conceptual combination. Cognition, 12, 299-318.
  • Peña-Suárez, E., Muñiz, J., Campillo-Álvarez, Á., Fonseca-Pedrero, E., & García-Cueto, E. (2013). Assessing organizational climate: Psychometric properties of the CLIOR Scale. Psicothema, 25, 137-144.
  • Puri, M.L., & Ralescu, D.A. (1986). Fuzzy random variables. Journal of Mathematical Analysis and Applications, 114, 409-422.
  • Ramos-Guajardo, A.B., & Lubiano, M.A. (2012). K-sample tests for equality of variances of random fuzzy sets. Computational Statistics and Data Analysis, 56, 956-966.
  • Sinova, B., Gil, M.A., Colubi, A., & Van Aelst, S. (2012). The median of a random fuzzy number. The 1-norm distance approach. Fuzzy Sets and Systems, 200, 99-115.
  • Smithson, M., & Oden, G.C. (1999). Fuzzy set theory and applications in Psychology. In H.-J. Zimmermann (Ed.), Practical Applications of Fuzzy Technologies. Series: The Handbooks of Fuzzy Sets Series Vol. 6 (pp. 557-585). Heidelberg: Springer.
  • Sowa, J.F. (2013). What is the source of fuzziness? In R. Seising, E. Trillas, C. Moraga & S. Termini (Eds.), On Fuzziness. A Homage to Lotfi A. Zadeh-Volume 2 (pp. 645-652). Heidelberg: Springer.
  • Stoklasa, J., Talášek, T., & Musilová, J. (2014). Fuzzy approach - a new chapter in the methodology of Psychology? Human Affairs, 24, 189-203.
  • Szalma, J.L., & Hancock, P.A. (2013). A signal improvement to signal detection analysis: Fuzzy sdt on the ROCs. Journal of Experimental Psychology: Human Perception and Performance, 39, 1741-1762.
  • Takemura, K. (1999). A fuzzy linear regression analysis for fuzzy inputoutput data using the least squares method under linear constraints and its application to fuzzy rating data. Journal of Advanced Computational Intelligence and Intelligent Informatics, 3, 36-41.
  • Takemura, K. (2007). Ambiguous comparative judgment: fuzzy set model and data analysis. Japanese Psychological Research, 49, 148-156.
  • Tomás, J.M., & Oliver, A. (1988). Efectos de formato de respuesta y método de estimación en análisis factorial confi rmatorio. Psicothema, 10, 197-208.
  • Van Dijk, M., & Van Geert, P. (2009). The application of Fuzzy Logic principles to Developmental Psychology. In R.E. Vargas (Ed.), Fuzzy Logic: Theory, Programming and Applications (pp. 329-336). New York: Nova Sciences Publishers.
  • Walsh, R.T.G., Teo, T., & Baydala, A. (2014). A Critical History and Philosophy of Psychology. Cambridge UK: Cambridge University Press.
  • Wierman, M.J. (2013). Psychologists: Are they logically fuzzy? In Joint IFSA World Conference and NAFIPS Annual Meeting (pp. 854-859).
  • Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
  • Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Part 1. Information Sciences, 8, 199-249; Part 2. Information Sciences, 8, 301-353; Part 3. Information Sciences, 9, 43-80.
  • Zadeh, L.A. (2008). Is there a need for fuzzy logic? Information Sciences, 178, 2751-2779.
  • Zétényi, T. (Ed.) (1988). Fuzzy Sets in Psychology. Series: Advances in Psychology, Vol. 56. Amsterdam: North-Holland, Elsevier.
Fuente de los datos: Dialnet