Open Access
Issue
2015
17th International Congress of Metrology
Article Number 17001
Number of page(s) 6
Section Posters Métrologie sensorielle – Soft Metrology
DOI https://doi.org/10.1051/metrology/20150017001
Published online 21 September 2015
  • JCGM, Guide to the expression of uncertainty in measurement (GUM). 2008. [Google Scholar]
  • JCGM, Evaluation of measurement data – The role of measurement uncertainty in Conformity Assessment, in Joint Committee on Guides in Metrology (JCGM). 2012. [Google Scholar]
  • T Gadrich, E Bashkansky, and R Zitikis, Assessing variation: a unifying approach for all scales of measurement. Quality & Quantity, 2014. 49(3): p. 1145–1167. [CrossRef] [Google Scholar]
  • J C Helton, et al., Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering and System Safety, 2006. 91: p. 1175–209. [CrossRef] [Google Scholar]
  • L R Pendrill, Using measurement uncertainty in decision-making & conformity assessment. Metrologia, 2014. 51: p. S206. [CrossRef] [Google Scholar]
  • L R Pendrill, et al., Measurement with Persons: A European Network. NCSLI Measure J. Meas. Sci., 2010. 5(2): p. 42–54. [Google Scholar]
  • L R Pendrill and W P Fisher Jr, Counting and Quantification: Comparing Psychometric and Metrological Perspectives on Visual Perceptions of Number. Measurement, 2015. 71: p. 46–55. [CrossRef] [Google Scholar]
  • L R Pendrill, El ser humano como instrument de medida. e-medida, 2014. [Google Scholar]
  • AIAG, Measurement Systems Analysis Reference Manual, in Chrysler, Ford, General Motors Supplier Quality Requirements Task Force. 2002, Automotive Industry Action Group. [Google Scholar]
  • L R Pendrill, Man as a Measurement Instrument. NCSLI Measure J. Meas. Sci., 2014. 9: p. 24–35. [Google Scholar]
  • J P Bentley, Principles of Measurement Systems. 4th ed. 2005, London: Pearson Education Limited. [Google Scholar]
  • J Sun, et al., A framework for Bayesian optimality of psychophysical laws. J. Math. Psychol., 2012. 56(6): p. 495–501. [CrossRef] [Google Scholar]
  • K Sijtsma, Introduction to the measurement of psychological attributes. Measurement, 2011. 44(7): p. 1209–1219. [CrossRef] [Google Scholar]
  • L Mari and M Wilson, An introduction to the Rasch measurement approach for metrologists. Measurement, 2014. 51: p. 315–327. [CrossRef] [Google Scholar]
  • E Svensson, Guidelines to statistical evaluation of data from rating scales and questionnaires. J. Rehabil. Med., 2001. 33: p. 47–48. [CrossRef] [PubMed] [Google Scholar]
  • W Fisher Jr, Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 2009. 42(9): p. 1278–1287. [CrossRef] [Google Scholar]
  • EN 15224, Health care services – Quality management systems – Requirements based on EN ISO 9001:2008. 2012. [Google Scholar]
  • C D Ehrlich. Traceability Considerations for the Characterization and Use of Measuring Systems. in NCSLi Workshop and Symposium. 2015. Dallas, TX, USA. [Google Scholar]
  • W Hardcastle, Qualitative Analysis: A Guide to Best Practice. 1998, Cambridge, UK: Royal Society of Chemistry. [Google Scholar]
  • S Ellison and T Fearn, Characterising the performance of qualitative analytical methods: Statistics and terminology. TRAC-Trend Anal. Chem., 2005. 24(6): p. 468–476. [CrossRef] [Google Scholar]
  • L R Pendrill, Uncertainty & risks in decision-making in qualitative measurement: An information-theoretical approach, in Advanced Mathematical and Computational Tools in Metrology and Testing, S.o.A.i.M.f.A. Sciences, Editor. 2012, World Scientific. [Google Scholar]
  • L R Pendrill, Discrete ordinal & interval scaling and psychometrics, in Métrologie 2013 Congress, CFM, Editor. 2013: Paris. [Google Scholar]
  • P McCullagh, Regression models for ordinal data. J. Roy. Stat. Soc., 1980. 42: p. 109–42. [Google Scholar]
  • F De Battisti, G Nicolini, and S Salini, The Rasch model to measure service quality. The ICFAI Journal of Services Marketing, 2005. vol. III(3): p. 58–80. [Google Scholar]
  • G Rasch, On general laws and the meaning of measurement in psychology, in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability. 1961, University of California Press: Berkeley. p. 321–334. [Google Scholar]
  • J Weller, et al., Development and Testing of an Abbreviated Numeracy Scale: A Rasch Analysis Approach. J. Behav. Decis. Making, 2013. 26(2): p. 198–212. [CrossRef] [Google Scholar]
  • G Iverson and R Luce, The representational measurement approach to psychophysical and judgmental problems, in Measurement, Judgment, and Decision Making. 1998, Academic Press. [Google Scholar]
  • OECD, The Rasch Model, in PISA Data Analysis Manual, SAS, Editor. 2009, OECD. p. 79–94. [Google Scholar]
  • J P Guilford, Psychometric Methods. 1936, McGraw-Hill, Inc. p. 1–19. [Google Scholar]
  • B Wright, Comparing factor analysis and Rasch measurement. Rasch Measurement Transactions, 1994. 8(1). [Google Scholar]
  • M Wilson, et al., A comparison of measurement concepts across physical science and social science domains: instrument design, calibration, and measurement. Journal of Physics: Conference Series, 2015. 588. [CrossRef] [Google Scholar]
  • J Linacre and B Wright, The ‘Length’ of a Logit. Rasch Measurement Transactions, 1989. 3(2): p. 54–55. [Google Scholar]
  • S Humphry, The Role of the Unit in Physics and Psychometrics. Measurement: Interdisciplinary Research and Perspectives, 2011. 9(1): p. 1–24. [CrossRef] [Google Scholar]
  • J de Boer, On the History of Quantity Calculus and the International System. Metrologia, 1994/95. 32: p. 405–429. [Google Scholar]
  • G B Rossi, Measurability. Measurement, 2007. 40: p. 545–562. [CrossRef] [Google Scholar]