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A methodology is presented to develop and analyze vectors of data quality attribute scores. Each data quality vector component represents the quality of the data element for a specific attribute (e.g., age of data). Several methods for aggregating the components of data quality vectors to derive one data quality indicator (DQI) that represents the total quality associated with the input data element are presented with illustrative examples. The methods are compared and it is proven that the measure of central tendency, or arithmetic average, of the data quality vector components as a percentage of the total quality range attainable is an equivalent measure for the aggregate DQI. In addition, the methodology is applied and compared to realworld LCA data pedigree matrices. Finally, a method for aggregating weighted data quality vector attributes is developed and an illustrative example is presented. This methodology provides LCA practitioners with an approach to increase the precision of input data uncertainty assessments by selecting any number of data quality attributes with which to score the LCA inventory model input data. The resultant vector of data quality attributes can then be analyzed to develop one aggregate DQI for each input data element for use in stochastic LCA modeling.
KeywordsData quality vector LCA input data quality LCI input data quality Life Cycle Assessment Life Cycle Inventory stochastic LCA modeling
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- Fraleigh, J. B.;Beauregard, R. A. (1987): Linear Algebra. Addison Wesley Publishing Company, Inc., MAGoogle Scholar
- Funtowicz, S. O.;Ravetz, J. R. (1990): Uncertainty and Quality in Science for Policy. Kluwer Academic Publishers, The NetherlandsGoogle Scholar
- Goicoechea, A.;Hansen, D.R.;Duckstein, L. (1982): Multiobjective Decision Analysis with Engineering and Business Applications. John Wiley & Sons, Inc., NYGoogle Scholar
- Hadley, G. (1962): Linear Programming. Addison-Wesley Publishing Company, Inc., MAGoogle Scholar
- Hamburg, M. (1983): Statistical Analysis for Decision Making. 3rd Ed. Harcourt Brace Janovich, Inc., NYGoogle Scholar
- Hillier, E. S.;Lieberman, G. J. (1980): Introduction to Operations Research. 3rd Ed. Holden-Day, Inc., CAGoogle Scholar
- Kennedy, D. J.; Montgomery, D. C.; Quay, B. H. (1996): Stochastic Environmental Life Cycle Assessment Modeling: A Probabilistic Approach to Incorporating Variable Input Data Quality. Int. J. LCA 1 (4)Google Scholar
- Microsoft® Excel Version 5.0 User’ s Guide (1994): Microsoft Corporation, Kirkland, WAGoogle Scholar
- Protter, M. H.;Morrey, C. B. Jr. (1964): Modern Mathematical Analysis. Addison-Wesley Publishing Company, Inc., MAGoogle Scholar
- Statgraphics® Reference Manual Version 5 (1991): STSC, Inc., Rockville, MDGoogle Scholar
- Weidema, B. P.; Wesnoes, M. S. (1995): Data Quality Management for Life Cycle Inventories - An Example of Using Data Quality Indicators. Presented to the 2nd SETAC World Congress, VancouverGoogle Scholar
- Wu, N.;Coppin, R. (1981): Linear Programming and Extensions. McGraw-Hill Book Company, NYGoogle Scholar