Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018 (KEER 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 739))

Included in the following conference series:

  • 4386 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. Nunnally, J.O. Psychometric theory. McGraw-Hill, New York (1978).

    Google Scholar 

  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. Kraemer, H.C., Thiemann, S. How many subjects: Statistical power analysis in research. Sage Publications, Newbury Park (1987).

    Google Scholar 

  5. Lord, F.M. A theory of tests scores. Psychometric monograph No.7. Psychometric, Iowa City (1952).

    Google Scholar 

  6. Brown, T.A. Confirmatory factor analysis for applied research. The Guilford Press, New York (2006).

    Google Scholar 

  7. Hoyle, R.H. Structural equation modeling: Concepts, issues and applications. Sage Publications, London (1995).

    Google Scholar 

  8. Embretson, S.E., Reise, S.P. Item response theory for psychologists. Lawrence Erlbaum, Mahwah, NJ (2000).

    Google Scholar 

  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. 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. 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. Linacre, J. M. Many-Facet Rasch Measurement. Mesa Press, Chicago (1989).

    Google Scholar 

  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. Andrich, D., Sheridan, B. E., Luo, G. Rumm2030: Rasch unidimensional models for measurement (computer software). RUMM Laboratory, Perth, Australia (2012).

    Google Scholar 

  15. Efron, B. Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7(1), 1-26 (1979).

    Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Softmetrika Consulting Services, Curitiba, 82520460, Brazil

    Fabio R Camargo

Authors
  1. Fabio R Camargo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabio R Camargo .

Editor information

Editors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Anitawati Mohd Lokman

  2. Faculty of Art and Design, University of Tsukuba, Tsukuba, Ibaraki, Japan

    Toshimasa Yamanaka

  3. Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands

    Pierre Lévy

  4. College of Communication and Design, I-Shou University, Kaohsiung, Kao-hsiung, Taiwan

    Kuohsiang Chen

  5. School of Art and Design, University of Tsukuba, Tsukuba, Ibaraki, Japan

    Shinichi Koyama

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Camargo, F.R. (2018). AE Workbench Function: A Springboard for Exploratory Analysis in Affective Engineering. In: Lokman, A., Yamanaka, T., Lévy, P., Chen, K., Koyama, S. (eds) Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018. KEER 2018. Advances in Intelligent Systems and Computing, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-10-8612-0_35

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8612-0_35

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8611-3

  • Online ISBN: 978-981-10-8612-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation