238
Maul, A., Mari, L., Torres Irribarra, D., & Wilson, M. (2018). The quality of measurement results
in terms of the structural features of the measurement process. Measurement, 116, 611–620.
Maul, A., Mari, L., & Wilson, M. (2019). Intersubjectivity of measurement across the sciences.
Measurement, 131, 764–770.
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments (CSE technical report). Washington, DC: University of California at Los Angeles,
Center for the Study of Evaluation, National Center for Research on Evaluation, Standards,
and Student Testing.
Morawski, R. Z. (2013). An application-oriented mathematical meta-model of measurement.
Measurement, 46, 3753–3765.
Muller, J. Z. (2018). The tyranny of metrics. Princeton, NJ: Princeton University Press.
Nitko, A. J. (1983). Educational tests and measurement: An introduction. New York: Harcourt
Brace Jovanovich.
Nunally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Petri, D., Mari, L., & Carbone, P. (2015). A structured methodology for measurement development. IEEE Transactions on Instrumentation and Measurement, 64, 2367–2379.
Piotrowski, J. (1992). Theory of physical and technical measurement. Amsterdam: Elsevier.
Porter, T. M. (1995). Trust in numbers—The pursuit of objectivity in science and public life.
Princeton, NJ: Princeton University Press.
Possolo, A. (2015). Simple guide for evaluating and expressing the uncertainty of NIST measurement results. NIST Technical Note 1900. Retrieved from www.nist.gov/publications/
simple-guide-evaluating-and-expressing-uncertainty-nist-measurement-results
Roberts, F. S. (1979). Measurement theory with applications to decision-making, utility and the
social sciences. Reading, MA: Addison-Wesley.
Rossi, G. B., & Crenna, F. (2018). A formal theory of the measurement system. Measurement,
116, 644–651.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.
Taylor, J. R. (1997). An introduction to error analysis—The study of uncertainties in physical
measurements. Sausalito: University Science Book.
Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ:
Erlbaum. (Now published by Taylor and Francis, New York).
Wilson, M., & Sloane, K. (2000). From principles to practice: An embedded assessment system.
Applied Measurement in Education, 13(2), 181–208.
Wright, B. D., & Masters, G. N. (1981). Rating scale analysis. Chicago, IL: MESA Press.
7 Modeling measurement and its quality
Maul, A., Mari, L., Torres Irribarra, D., & Wilson, M. (2018). The quality of measurement results
in terms of the structural features of the measurement process. Measurement, 116, 611–620.
Maul, A., Mari, L., & Wilson, M. (2019). Intersubjectivity of measurement across the sciences.
Measurement, 131, 764–770.
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments (CSE technical report). Washington, DC: University of California at Los Angeles,
Center for the Study of Evaluation, National Center for Research on Evaluation, Standards,
and Student Testing.
Morawski, R. Z. (2013). An application-oriented mathematical meta-model of measurement.
Measurement, 46, 3753–3765.
Muller, J. Z. (2018). The tyranny of metrics. Princeton, NJ: Princeton University Press.
Nitko, A. J. (1983). Educational tests and measurement: An introduction. New York: Harcourt
Brace Jovanovich.
Nunally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
Petri, D., Mari, L., & Carbone, P. (2015). A structured methodology for measurement development. IEEE Transactions on Instrumentation and Measurement, 64, 2367–2379.
Piotrowski, J. (1992). Theory of physical and technical measurement. Amsterdam: Elsevier.
Porter, T. M. (1995). Trust in numbers—The pursuit of objectivity in science and public life.
Princeton, NJ: Princeton University Press.
Possolo, A. (2015). Simple guide for evaluating and expressing the uncertainty of NIST measurement results. NIST Technical Note 1900. Retrieved from www.nist.gov/publications/
simple-guide-evaluating-and-expressing-uncertainty-nist-measurement-results
Roberts, F. S. (1979). Measurement theory with applications to decision-making, utility and the
social sciences. Reading, MA: Addison-Wesley.
Rossi, G. B., & Crenna, F. (2018). A formal theory of the measurement system. Measurement,
116, 644–651.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.
Taylor, J. R. (1997). An introduction to error analysis—The study of uncertainties in physical
measurements. Sausalito: University Science Book.
Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ:
Erlbaum. (Now published by Taylor and Francis, New York).
Wilson, M., & Sloane, K. (2000). From principles to practice: An embedded assessment system.
Applied Measurement in Education, 13(2), 181–208.
Wright, B. D., & Masters, G. N. (1981). Rating scale analysis. Chicago, IL: MESA Press.
7 Modeling measurement and its quality