Advertisement

AE Workbench Function: A Springboard for Exploratory Analysis in Affective Engineering

  • Fabio R Camargo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 739)

Abstract

Affective engineering (AE) workbench function is a computer simulation tool based on latent trait models for applications in Kansei and affective engineering. The tool integrates observed data obtained from responses of small number of people in exploratory trials and artificial data sets within a projected probabilistic distribution. Via different simulations a number of potential issues in data analysis can be tested before carrying out larger trials, such as dependence between affective statements or Kansei words, and high correlation between stimuli. The general principles of the AE workbench function is shown in a case study based on a typical data collection in the domain. Although the tool is not designed to overcome completely the difficulties associated with the availability of data as eliciting people’s responses in controlled environment, it can offer insights about an experimental design, providing the opportunity for fine-tuning and strengthening one’s a priori assumptions.

Keywords

Simulation Probabilistic Approach Affective Engineering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hair, J.F., Black, W.C., Babin, B.J. Anderson, R.E., Tatham, R.L.: Multivariate data analysis. 6th edn. Pearson-Prentice Hall, Upper Saddle River, NJ (2006).Google Scholar
  2. 2.
    Nunnally, J.O. Psychometric theory. McGraw-Hill, New York (1978).Google Scholar
  3. 3.
    Orme, B. Getting started with conjoint analysis: strategies for product design and pricing research. 2nd edn. Research Publishers LLC, Madison, Wis (2010).Google Scholar
  4. 4.
    Kraemer, H.C., Thiemann, S. How many subjects: Statistical power analysis in research. Sage Publications, Newbury Park (1987).Google Scholar
  5. 5.
    Lord, F.M. A theory of tests scores. Psychometric monograph No.7. Psychometric, Iowa City (1952).Google Scholar
  6. 6.
    Brown, T.A. Confirmatory factor analysis for applied research. The Guilford Press, New York (2006).Google Scholar
  7. 7.
    Hoyle, R.H. Structural equation modeling: Concepts, issues and applications. Sage Publications, London (1995).Google Scholar
  8. 8.
    Embretson, S.E., Reise, S.P. Item response theory for psychologists. Lawrence Erlbaum, Mahwah, NJ (2000).Google Scholar
  9. 9.
    Rasch, G. Probabilistic models for some intelligence and attainment tests, (Copenhagen: Danish Institute for Educational Research), expanded edition (1980) with foreword and afterword by B.D. Wright. The University of Chicago Press, Chicago (1960, 1980).Google Scholar
  10. 10.
    Andrich, D. Rasch models for measurement. Sage university papers series on quantitative applications in the social sciences, No. 68. Sage, London (1988).Google Scholar
  11. 11.
    Camargo, F.R., Henson, B. Beyond usability: Designing for consumers’ product experience using the Rasch model, Journal of Engineering Design 26(4-6), 121–139 (2015).Google Scholar
  12. 12.
    Linacre, J. M. Many-Facet Rasch Measurement. Mesa Press, Chicago (1989).Google Scholar
  13. 13.
    Camargo, F.R., Henson, B. Measuring affective responses for human-oriented product design using the Rasch model. Journal of Design Research 9(4), 360 – 375 (2011).Google Scholar
  14. 14.
    Andrich, D., Sheridan, B. E., Luo, G. Rumm2030: Rasch unidimensional models for measurement (computer software). RUMM Laboratory, Perth, Australia (2012).Google Scholar
  15. 15.
    Efron, B. Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7(1), 1-26 (1979).Google Scholar
  16. 16.
    Marais, I., Andrich, D. RUMMss – Rasch unidimensional measurement model simulation studies program (computer software). The University of Western Australia, Perth (2012).Google Scholar
  17. 17.
    Marais, I., Andrich, D. Formalizing dimension and response violations of local independence in the unidimensional Rasch model. Journal of Applied Measurement 9(3), 200 – 215 (2008).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Softmetrika Consulting ServicesCuritibaBrazil

Personalised recommendations