Fuzzy approach - a new chapter in the methodology of psychology?

• Authors: Jan Stoklasa , Tomáš Talášek , Jana Musilová
• Languages of publication: EN

Abstracts

EN

This paper aims to briefly introduce the main idea behind the fuzzy approach and to identify the areas and problems encountered in the humanities that might profit from using this approach. Based on a short overview of selected applications of fuzzy in psychology we identify key areas in which the fuzzy approach has already been applied, and propose a list of general types of problems that the fuzzy approach may provide solutions for in psychology and the humanities in general. These types of problems are illustrated using practical examples. The benefits and possible shortcomings of using the fuzzy approach compared to classical approaches in use today are discussed. The goal of this paper is to indicate areas in research and practice in the humanities, where modern mathematical tools-in this case linguistic fuzzy modeling-have already been used or might prove promising.

Keywords

EN

methodology; fuzzy; linguistic modelling; decision support; diagnostics

• Publisher: Sciendo
• Journal: Human Affairs
• Year: 2014
• Volume: 24
• Issue: 2
• Pages: 189-203

Dates

published

2014-04-01

online

2014-03-29

Contributors

author

Jan Stoklasa

• Palacky University in Olomouc

author

Tomáš Talášek

author

Jana Musilová

• Family counselling

References

• [1] Arfi, B. (2010). Linguistic fuzzy logic methods in social sciences. Berlin Heidelberg: Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-13343-5[Crossref]
• [2] Bebčáková, I., Talašová, J., & Škobrtal, P. (2010). Interpretation of the MMPI-2 Test based on fuzzy set techniques. Acta Universitatis Matthiae Belii ser. Mathematics 16, 5–16.
• [3] Burisch, M. (1993). In search of theory: Some ruminations on the nature and etiology of burnout. In W. B. Schaufeli, C. Maslach, & T. Marek (Eds.), Professional burnout: recent developments in theory and research (pp. 75–93). Washington: Taylor & Francis.
• [4] Dubois, D., & Prade, H. (Eds.). (2000). Fundamentals of fuzzy sets. The handbook of fuzzy sets series. Boston, London, Dordrecht: Kluwer Academic Publishers.
• [5] Horowitz, L. M., & Malle, B. F. (1993). Fuzzy concepts in psychotherapy research. Psychotherapy research, 3, 131–148. http://dx.doi.org/10.1080/10503309312331333739[Crossref]
• [6] Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. New Jersey: Prentice Hall.
• [7] Massaro, D. W., Weldon, M. S., & Kitzis, S. N. (1991). Integration of orthographic and semantic information in memory retrieval. Journal of Experimental Psychology Learning, Memory and Cognition, 17, 277–287. http://dx.doi.org/10.1037/0278-7393.17.2.277[WoS][Crossref]
• [8] Oden, G. C., & Massaro, D. W. (1978). Integration of featural information in speech perception. Psychological review, 85, 172–191. http://dx.doi.org/10.1037/0033-295X.85.3.172[Crossref]
• [9] Ragin, C. C. (2000). Fuzzy-set social sciences. Chicago: University of Chicago Press.
• [10] Smithson, M., & Oden, C. G. (1999). Fuzzy set theory and applications in psychology. In H. J. Zimmermann (Ed.), Practical Applications of fuzzy technologies (pp. 557–585). Norwell: Kluwer Academic Publishers. http://dx.doi.org/10.1007/978-1-4615-4601-6_17[Crossref]
• [11] Smithson, M., & Verkuilen, J. (2006). Fuzzy set theory: Applications in the social sciences. Thousand Oaks, London, New Delhi: Sage Publications.
• [12] Smithson, M. (1986). Fuzzy set analysis for behavioral and social science. New York, Berlin, Heidelberg: Springer-Verlag.
• [13] Stoklasa, J., Holeček, P., & Talašová, J. (2012). A holistic approach to academic staff performance evaluation - a way to the fuzzy logic based evaluation, Peer reviewed full papers of the 8th international conference on evaluation for practice “Evaluation as a tool for research, learning and making things better”. A Conference for Experts of Education, Human Services and Policy, 18–20 June 2012, 2012, Pori, Finland, 121–131.
• [14] Stoklasa, J., Jandová, V., & Talašová, J. (2013). Weak consistency in Saaty’s AHP - evaluating creative work outcomes of Czech Art Colleges. Neural network world 23, 61–77.
• [15] Stoklasa, J., & Talašová, J. (2013). AHP based decision support tool for the evaluation of works of art - Registry of Artistic Performances., Proceedings of the Finnish operations research society 40th anniversary workshop - FORS40, Lappeenranta 20. - 21.8.2013, LUT Scientific and Expertise Publications No. 13, 44–47.
• [16] Stoklasa, J., & Talašová, J. (2011). Using linguistic fuzzy modeling for MMPI-2 data interpretation. Proceedings of the 29th International Conference on Mathematical Methods in Economics 2011 - part II, Praha, Czech Republic, 653–658.
• [17] Stoklasa, J., Talašová, J., & Holeček, P. (2011). Academic staff performance evaluation - variants of models, Acta Polytechnica Hungarica 8(3), 91–111.
• [18] Zadeh, L. A. (1975). The concept of linguistic variable and its application to approximate reasoning. Information Sciences, Part 1: 8, 199–249; Part 2: 8, 301–357; Part 3: 9, 43–80.
• [19] Zadeh, L. A. (1965). Fuzzy sets. Inform. Control, 8, 338–353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X[Crossref]
• [20] Zemková, B., & Talašová, J. (2011). Fuzzy sets in HR Management, Acta Polytechnica Hungarica, 8(3), 113–124.
• [21] Zétényi, T. (Ed.). (1988). Fuzzy sets in psychology. Amsterdam, New York, Oxford, Tokyo: North-Holland.

Identifiers

DOI

10.2478/s13374-014-0219-8