230
However, in scientific measurement, a Basic Evaluation Equation is generally
expected to convey widely transferable information. Values, on the right-hand side
of the equation, can in principle be interpreted in the same way everywhere and
always thanks to their metrological traceability (see Sect. 3.3.1). Analogously, measurands, on the left-hand side of the equation, should be interpretable beyond the
here-and-now situation. This is accomplished by explicitly defining the measurand,
by means of a model which identifies the measurand by description instead of by
purely indexical means, and therefore by taking into account the possible differences between the measurand and the property that produces the transduction: the
information is empirically acquired about the effective property but is reported
about the intended property, and a purpose of the model is to establish a connection
between these two. Sometimes these differences can be considered explicitly, if the
model has a mathematical form in which a value of the intended property is calculated as a function of both the effective property and appropriate corrections (as
when the measurand is the temperature of an object in given environmental conditions but the temperature is measured in different conditions, and there is a known
law connecting such environmental conditions to the property under
measurement).
But, as usual, there is a price to be paid for improving the transferability of the
measurement information: the greater the specificity of the information, the greater
its uncertainty, which in this case is uncertainty about the definition of the measurand, called definitional uncertainty in the VIM (JCGM, 2012: 2.27) (see Sect. 3.2.4).
Thus, ignoring the distinction between the intended and effective property amounts
to the elimination of definitional uncertainty from the model. For example, considering the temperature of water in a container, the effective property is the temperature of that part of the water with which the thermometer interacts, in the context of
the unknown conditions of the water in the container at the time of the interaction.
However, the measurand could be defined by a specification of the conditions of the
object (i.e., the water) and the environment (i.e., the container), for example by
assuming that the water is thermally uniform and the measurement takes place at a
given environmental pressure. Assuming this makes the information more transferable, but at the cost of non-null definitional uncertainty: we must take into account
the differences between the specified conditions (i.e., “the water is thermally uniform and the measurement takes place at a given environmental pressure”) and the
actual conditions of the interaction of the object and the measuring instrument.
25
The place of definitional uncertainty in the Framework is depicted in Fig. 7.21. As
noted in Sect. 3.2.4, there are many types of definitional uncertainty in the context
of measurement in the human sciences (sometimes referred to as “threats to validity”). One particular threat is construct underrepresentation: this is where the effective property is less complex or rich than the intended property. An example in the
case of RCA would be, for example, where the property is considered to pertain to
25 The measurand definition and its influence on the design and the operation of measurement are
subjects that still require investigation—see, e.g., Baratto (2008) and Morawski (2013).
7 Modeling measurement and its quality
However, in scientific measurement, a Basic Evaluation Equation is generally
expected to convey widely transferable information. Values, on the right-hand side
of the equation, can in principle be interpreted in the same way everywhere and
always thanks to their metrological traceability (see Sect. 3.3.1). Analogously, measurands, on the left-hand side of the equation, should be interpretable beyond the
here-and-now situation. This is accomplished by explicitly defining the measurand,
by means of a model which identifies the measurand by description instead of by
purely indexical means, and therefore by taking into account the possible differences between the measurand and the property that produces the transduction: the
information is empirically acquired about the effective property but is reported
about the intended property, and a purpose of the model is to establish a connection
between these two. Sometimes these differences can be considered explicitly, if the
model has a mathematical form in which a value of the intended property is calculated as a function of both the effective property and appropriate corrections (as
when the measurand is the temperature of an object in given environmental conditions but the temperature is measured in different conditions, and there is a known
law connecting such environmental conditions to the property under
measurement).
But, as usual, there is a price to be paid for improving the transferability of the
measurement information: the greater the specificity of the information, the greater
its uncertainty, which in this case is uncertainty about the definition of the measurand, called definitional uncertainty in the VIM (JCGM, 2012: 2.27) (see Sect. 3.2.4).
Thus, ignoring the distinction between the intended and effective property amounts
to the elimination of definitional uncertainty from the model. For example, considering the temperature of water in a container, the effective property is the temperature of that part of the water with which the thermometer interacts, in the context of
the unknown conditions of the water in the container at the time of the interaction.
However, the measurand could be defined by a specification of the conditions of the
object (i.e., the water) and the environment (i.e., the container), for example by
assuming that the water is thermally uniform and the measurement takes place at a
given environmental pressure. Assuming this makes the information more transferable, but at the cost of non-null definitional uncertainty: we must take into account
the differences between the specified conditions (i.e., “the water is thermally uniform and the measurement takes place at a given environmental pressure”) and the
actual conditions of the interaction of the object and the measuring instrument.
25
The place of definitional uncertainty in the Framework is depicted in Fig. 7.21. As
noted in Sect. 3.2.4, there are many types of definitional uncertainty in the context
of measurement in the human sciences (sometimes referred to as “threats to validity”). One particular threat is construct underrepresentation: this is where the effective property is less complex or rich than the intended property. An example in the
case of RCA would be, for example, where the property is considered to pertain to
25 The measurand definition and its influence on the design and the operation of measurement are
subjects that still require investigation—see, e.g., Baratto (2008) and Morawski (2013).
7 Modeling measurement and its quality