Some Basic Considerations on the Design and the Interpretation of Indicators. . .
15
planned indicator and it ends much later than assumed with testing the indicator
against the previously established validity criteria.
7 Some Summary Remarks
By classifying indicators according to fields of application and internal structure,
the article leads to the insight that an indicator is an independent evaluation model
that is independent of the actual system model. Due to its model character, however,
like all models it is subject to quality control by careful validation with the usual
consideration of validity, accuracy and sensitivity (e.g. with regard to weighting
factors in the aggregation of composite indicators).
In contrast to multi-criteria methods based on partial orders, aggregated indicator
systems lead to the one-dimensional decision aids popular with the user (e.g. in the
form of a traffic light that reflects the system state), which very quickly convey
the current system state, but in the case of a system warning require knowledge
about the structure of the aggregation rules in order to determine the partial indicator
responsible for the error display. Partial orders, on the other hand, do not make a
final decision for incomparable alternatives. Instead, they highlight them and leave
the final decision to the user. In the event of a warning, however, the user also has
knowledge of the structures that lead to the warning and is usually better able to
detect its cause.
References
Bruggemann, R., Carlsen, L., & Wittmann, J. (2014). Multi-indicator systems and modelling in
partial order. New York: Springer.
Businessdictionary. (2017, December 07). Von http://www.businessdictionary.com/definition/
indicator.html. Abgerufen.
Campbell, N. A., & Reece, B. J. (2003). Biologie. Heidelberg, Berlin: Spektrum Verlag.
Elliott, M., & Coventry, A. (2012). Critical care: The eight vital signs of patient monitoring. British
Journal of Nursing, 21(10), 621–625.
Haseloff, H.-P. (1982). Bioindikatoren und Bioindikation. Biologie in unserer Zeit. , S. 12, Nr. 1,
20–26.
HealthcareInstitute. (2017, June 25). Early-warning-score. Von http://www.ihi.org/resources/
Pages/ImprovementStories/EarlyWarningSystemsScorecardsThatSaveLives.aspx. Abgerufen.
Helios-Kliniken. (2017, December 07). Early-warning-score. Von http://www.heliosaktuell.de/
nachrichten/ein-sticker-hilft-dabei-komplikationen-vorauszusagen. Abgerufen.
Schmidt, B. (1985). Systemanalyse und Modellaufbau. Berlin: Springer.
Wittmann, J. (2016). Komplexität beim Modellieren und Simulieren: Eine Analyse und ein
Plädoyer für schlanke Modelle. In Wiedemann, T.: Tagungsband ASIM 23. Symposium Simulationstechnik 2016 Dresden (S. 99–106). Wien: Argesim.
Zeigler, B. (1990). Object-oriented simulation with hierarchical, modular models. London:
Academic.
15
planned indicator and it ends much later than assumed with testing the indicator
against the previously established validity criteria.
7 Some Summary Remarks
By classifying indicators according to fields of application and internal structure,
the article leads to the insight that an indicator is an independent evaluation model
that is independent of the actual system model. Due to its model character, however,
like all models it is subject to quality control by careful validation with the usual
consideration of validity, accuracy and sensitivity (e.g. with regard to weighting
factors in the aggregation of composite indicators).
In contrast to multi-criteria methods based on partial orders, aggregated indicator
systems lead to the one-dimensional decision aids popular with the user (e.g. in the
form of a traffic light that reflects the system state), which very quickly convey
the current system state, but in the case of a system warning require knowledge
about the structure of the aggregation rules in order to determine the partial indicator
responsible for the error display. Partial orders, on the other hand, do not make a
final decision for incomparable alternatives. Instead, they highlight them and leave
the final decision to the user. In the event of a warning, however, the user also has
knowledge of the structures that lead to the warning and is usually better able to
detect its cause.
References
Bruggemann, R., Carlsen, L., & Wittmann, J. (2014). Multi-indicator systems and modelling in
partial order. New York: Springer.
Businessdictionary. (2017, December 07). Von http://www.businessdictionary.com/definition/
indicator.html. Abgerufen.
Campbell, N. A., & Reece, B. J. (2003). Biologie. Heidelberg, Berlin: Spektrum Verlag.
Elliott, M., & Coventry, A. (2012). Critical care: The eight vital signs of patient monitoring. British
Journal of Nursing, 21(10), 621–625.
Haseloff, H.-P. (1982). Bioindikatoren und Bioindikation. Biologie in unserer Zeit. , S. 12, Nr. 1,
20–26.
HealthcareInstitute. (2017, June 25). Early-warning-score. Von http://www.ihi.org/resources/
Pages/ImprovementStories/EarlyWarningSystemsScorecardsThatSaveLives.aspx. Abgerufen.
Helios-Kliniken. (2017, December 07). Early-warning-score. Von http://www.heliosaktuell.de/
nachrichten/ein-sticker-hilft-dabei-komplikationen-vorauszusagen. Abgerufen.
Schmidt, B. (1985). Systemanalyse und Modellaufbau. Berlin: Springer.
Wittmann, J. (2016). Komplexität beim Modellieren und Simulieren: Eine Analyse und ein
Plädoyer für schlanke Modelle. In Wiedemann, T.: Tagungsband ASIM 23. Symposium Simulationstechnik 2016 Dresden (S. 99–106). Wien: Argesim.
Zeigler, B. (1990). Object-oriented simulation with hierarchical, modular models. London:
Academic.