226
The idealized model of direct measurement described in the preceding sections
needs to be generalized to the more realistic case in which uncertainties are taken
into account.
7.4 Measurement quality according to the model
As operationalized by the Hexagon Framework, the Basic Evaluation Equation
needs to be augmented with an assessment of the quality of the information conveyed by measurement. As discussed in Sect. 3.2, this role is played by measurement uncertainty, which is inversely related to information quality: the better the
quality the less the uncertainty. By taking uncertainty into account, the model of
direct measurement introduced above is improved and generalized. In the human
sciences, the quality of measurement is usually assessed in terms of validity (see
Sect. 4.3) and reliability (see Sect. 3.2.1): while reliability is usually assessed via a
quantifier, this is seldom the case for validity, and if so, then it would only be quantified for some components of what is generally termed “validity”.
The acknowledgment of the structural, and not only operational, importance of
measurement uncertainty is relatively new in physical metrology: “the need to find
an agreed way of expressing measurement uncertainty in metrology” was stated in
the Recommendations issued by the International Committee of Weights and
Measures (CIPM) in 1980–1981 (quoted in JCGM, 2008). In the human sciences,
the need to investigate the validity of the measurement has been a basic element of
measurement practice since the early twentieth century (see Sect. 4.3). This can be
interpreted as a revision of the basic black box model: given an input property, a
measurement is expected to produce not only a value but also an estimate of the
quality of the information that such a value provides on the measured property.
As mentioned, until the recent past, measured values were reported together with
estimates of measurement errors. The relation between error and uncertainty in
measurement is complex: uncertainty has sources that are not what would traditionally be described as errors, as in the case of definitional uncertainty, and some errors
could be known only with some uncertainty; hence error not only may generate
uncertainty, but also may have its own uncertainty.
23
More importantly, the emphasis on uncertainty is a result of a conceptual shift in the recent metrological literature from a purely empirical to a model-based approach, incorporating both
23 Complicating matters, some authors refer to error and uncertainty interchangeably (Kirkup &
Frenkel, 2006), as noted by Taylor: “In science, the word error does not carry the usual connotations of the terms mistake or blunder. Error in a scientific measurement means the inevitable uncertainty that attends all measurements. [… Here] error is used exclusively in the sense of uncertainty,
and the two words are used interchangeably” (1997: p. 3). More or less explicitly, this denies that
there is anything new in what has happened on this matter in the last decades, as witnessed in
particular by the publication in 1993 of the Guide to the expression of uncertainty in measurement
(GUM) (JCGM, 2008).
7 Modeling measurement and its quality
The idealized model of direct measurement described in the preceding sections
needs to be generalized to the more realistic case in which uncertainties are taken
into account.
7.4 Measurement quality according to the model
As operationalized by the Hexagon Framework, the Basic Evaluation Equation
needs to be augmented with an assessment of the quality of the information conveyed by measurement. As discussed in Sect. 3.2, this role is played by measurement uncertainty, which is inversely related to information quality: the better the
quality the less the uncertainty. By taking uncertainty into account, the model of
direct measurement introduced above is improved and generalized. In the human
sciences, the quality of measurement is usually assessed in terms of validity (see
Sect. 4.3) and reliability (see Sect. 3.2.1): while reliability is usually assessed via a
quantifier, this is seldom the case for validity, and if so, then it would only be quantified for some components of what is generally termed “validity”.
The acknowledgment of the structural, and not only operational, importance of
measurement uncertainty is relatively new in physical metrology: “the need to find
an agreed way of expressing measurement uncertainty in metrology” was stated in
the Recommendations issued by the International Committee of Weights and
Measures (CIPM) in 1980–1981 (quoted in JCGM, 2008). In the human sciences,
the need to investigate the validity of the measurement has been a basic element of
measurement practice since the early twentieth century (see Sect. 4.3). This can be
interpreted as a revision of the basic black box model: given an input property, a
measurement is expected to produce not only a value but also an estimate of the
quality of the information that such a value provides on the measured property.
As mentioned, until the recent past, measured values were reported together with
estimates of measurement errors. The relation between error and uncertainty in
measurement is complex: uncertainty has sources that are not what would traditionally be described as errors, as in the case of definitional uncertainty, and some errors
could be known only with some uncertainty; hence error not only may generate
uncertainty, but also may have its own uncertainty.
23
More importantly, the emphasis on uncertainty is a result of a conceptual shift in the recent metrological literature from a purely empirical to a model-based approach, incorporating both
23 Complicating matters, some authors refer to error and uncertainty interchangeably (Kirkup &
Frenkel, 2006), as noted by Taylor: “In science, the word error does not carry the usual connotations of the terms mistake or blunder. Error in a scientific measurement means the inevitable uncertainty that attends all measurements. [… Here] error is used exclusively in the sense of uncertainty,
and the two words are used interchangeably” (1997: p. 3). More or less explicitly, this denies that
there is anything new in what has happened on this matter in the last decades, as witnessed in
particular by the publication in 1993 of the Guide to the expression of uncertainty in measurement
(GUM) (JCGM, 2008).
7 Modeling measurement and its quality