Skip to main content
SpringerLink
Log in

Experimental Research Methodology and Statistics Insights

  • Chapter
  • First Online:
Human-Robot Interaction

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 12))

Abstract

Methodological and statistical misunderstandings are common within empirical studies performed in the field of Human Robot Interaction (HRI). The current chapter is aimed to briefly introduce basic research methods concepts required for running robust HRI experimental studies. In addition, it is oriented to provide a conceptual perspective to the discussion regarding normality assumption violation, and describes a nonparametric alternative for complex experimental designs when such assumption cannot be fulfilled. It is concluded that HRI researchers should hold internal validity of studies as a priority and foster the use of within-subjects designs. Furthermore, the described statistical procedure is an alternative to analyze experimental data in multifactorial designs when normality assumptions are not fulfilled and may be held as a suggested practice within the field of HRI.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    A variable is held as independent when it is considered as a supposed cause in a relationship between variables. Conversely, the supposed effect elicited by this so-called independent variable is named dependent variable.

  2. 2.

    When responses within a set of data are ordinal, they provide sufficient information to pick any pair of them and determine in each comparison which is the lowest and which is the highest but they do not provide any information of their magnitudes.

  3. 3.

    Multifactorial designs are those that analyze the statistical effects of two or more independent variables on a dependent variable.

  4. 4.

    The long tail is the region of the distribution where data are less concentrated, whereas the steep tail is the region where data are more concentrated.

  5. 5.

    The complete code employed for the simulation ran in this subsection can be found in https://github.com/renatoparedes/ATS-vs-ANOVA.

References

  1. Eyssel, F.: An experimental psychological perspective on social robotics. Robot. Auton. Syst. 87, 363–371 (2017)

    Article  Google Scholar 

  2. Razali, N.M., Wah, Y.B., et al.: Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J. Stat. Model. Anal. 2(1), 21–33 (2011)

    Google Scholar 

  3. Siegal, S.: Nonparametric Statistics for the Behavioral Sciences. McGraw-hill (1956)

    Google Scholar 

  4. Boneau, C.A.: The effects of violations of assumptions underlying the t test. Psychol. Bull. 57(1), 49–64 (1960)

    Article  Google Scholar 

  5. Bradley, J.V.: Robustness? Br. J. Math. Stat. Psychol. 31(2), 144–152 (1978)

    Article  Google Scholar 

  6. Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. 140, 1–55 (1932)

    Google Scholar 

  7. Gombolay, M., Shah, A.: Appraisal of statistical practices in HRI vis-á-vis the t-test for likert items/scales. In: 2016 AAAI Fall Symposium Series (2016)

    Google Scholar 

  8. Jamieson, S., et al.: Likert scales: how to (ab) use them. Med. Educ. 38(12), 1217–1218 (2004)

    Article  Google Scholar 

  9. Carifio, J., Perla, R.J.: Ten common misunderstandings, misconceptions, persistent myths and urban legends about likert scales and likert response formats and their antidotes. J. Soc. Sci. 3(3), 106–116 (2007)

    Google Scholar 

  10. Baguley, T.: Serious stats: a guide to advanced statistics for the behavioral sciences. Macmillan International Higher Education (2012)

    Google Scholar 

  11. Hernández Sampieri, R., Fernández Collado, C., Baptista Lucio, P., et al.: Metodología de la investigación, vol. 3. McGraw-Hill, México (2010)

    Google Scholar 

  12. Bethel, C.L., Murphy, R.R.: Review of human studies methods in HRI and recommendations. Int. J. Soc. Robot. 2(4), 347–359 (2010)

    Article  Google Scholar 

  13. García, M.A., Seco, G.V.: Diseños experimentales en psicología. Pirámide (2007)

    Google Scholar 

  14. Kantowitz, B.H., Roediger III, H.L., Elmes, D.G.: Experimental Psychology. Nelson Education (2014)

    Google Scholar 

  15. Coolican, H.: Research Methods and Statistics in Psychology. Psychology Press (2017)

    Google Scholar 

  16. Smith, P.L., Little, D.R.: Small is beautiful: in defense of the small-n design. Psychon. Bull. Rev. 25(6), 2083–2101 (2018)

    Article  Google Scholar 

  17. Efron, B.: Student’s t-test under non-normal conditions, Technical report. Harvard Univ Cambridge Ma Dept of Statistics (1968)

    Google Scholar 

  18. Pearson, E.S., Adyanthāya, N.: The distribution of frequency constants in small samples from non-normal symmetrical and skew populations. Biometrika 21(1/4), 259–286 (1929)

    Article  Google Scholar 

  19. Kline, R.B.: Principles and Practice of Structural Equation Modeling. Guilford Publications (2015)

    Google Scholar 

  20. Bulmer, M.G.: Principles of Statistics. Courier Corporation (1979)

    Google Scholar 

  21. Carifio, J., Perla, R.: Resolving the 50-year debate around using and misusing likert scales. Med. Educ. 42(12), 1150–1152 (2008)

    Article  Google Scholar 

  22. Schunn, C.D., Wallach, D., et al.: Evaluating goodness-of-fit in comparison of models to data. In: Psychologie der Kognition: Reden and vorträge anlässlich der emeritierung von Werner Tack, pp. 115–154 (2005)

    Google Scholar 

  23. Kaptein, M.C., Nass, C., Markopoulos, P.: Powerful and consistent analysis of likert-type ratingscales. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2391–2394. ACM (2010)

    Google Scholar 

  24. Brunner, E., Domhof, S., Langer, F., Brunner, E.: Nonparametric Analysis of Longitudinal Data in Factorial Experiments. Wiley, New York (2002)

    Google Scholar 

  25. Noguchi, K., Gel, Y.R., Brunner, E., Konietschke, F.: nparld: an r software package for the nonparametric analysis of longitudinal data in factorial experiments. J. Stat. Softw. 50(12) (2012)

    Google Scholar 

  26. Brunner, E., Puri, M.L.: Nonparametric methods in factorial designs. Stat. Pap. 42(1), 1–52 (2001)

    Article  Google Scholar 

  27. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, Pontifical Catholic University of Peru, Lima, Peru

    Renato Paredes Venero & Alex Davila

Authors
  1. Renato Paredes Venero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Davila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renato Paredes Venero .

Editor information

Editors and Affiliations

  1. University Paris 8, Saint-Denis, France

    Céline Jost

  2. IUT de Vannes—Département STID, University of South Brittany, Vannes, France

    Brigitte Le Pévédic

  3. IDLab—imec—ELIS, Ghent, Belgium

    Tony Belpaeme

  4. Social, Therapeutic, and Robotic Systems Lab, Mississippi State University, Mississippi State, MS, USA

    Cindy Bethel

  5. Robotics and Automation Group, Aalborg University, Aalborg, Denmark

    Dimitrios Chrysostomou

  6. Research and Knowledge Exchange, Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford, UK

    Nigel Crook

  7. Laboratoire Ethologie Animale et Humaine, Université Rennes 1, Paimpont, France

    Marine Grandgeorge

  8. Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria

    Nicole Mirnig

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Paredes Venero, R., Davila, A. (2020). Experimental Research Methodology and Statistics Insights. In: Jost, C., et al. Human-Robot Interaction. Springer Series on Bio- and Neurosystems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42307-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-42307-0_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42306-3

  • Online ISBN: 978-3-030-42307-0

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation