Abstract
There is a worsening crisis in official statistics in most if not all countries. Agency resources have been strained for many years, while they are faced with demands for more information faster. Politicians have become ever more concerned with getting ‘good’ numbers. These forces impinge negatively on the quality of data even where computerization has increased and become more sophisticated. Further, individuals and firms are increasingly reluctant to supply sensitive data or even any data.
This paper examines the data quality situation at international, national, and local scales from the viewpoint of data users. A distressing finding is that they are ordinarily unable to obtain quality information disaggregated geographically or socially. Examples from the US, the Netherlands, Portugal, and Finland are presented. Less detailed information on quality problems and responses by international organizations and other national agencies is summarized. Techniques for remedial action and suggestions for actions by users follow.
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References
Alonso, W.; Starr, R. (eds.): The Politics of Numbers. New York: Russell Sage Foundation, 1987.
Anker, H.; Oppenhuis, E.V.: Dutch Parliamentary Election Study 1994. Steinmetz Archive/SWIDOC, Amsterdam [Stichting Kiezersonderzock Nederland (SKON)] 1995.
Bailar, J.C.; Bailar, B.A.: Comparison of Two Procedures for Imputing Missing Survey Values. American Statistical Association. Proceedings of the Section on Survey Research Methods, pp. 462–467 (1978).
Burrough, P.: Principles of Geographical Information Systems for Land Resources Assessment. Oxford: Clarendon Press. 1986.
Burrough, P.: Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science 40, pp. 477–492 (1989).
Chrisman, N.: The role of quality information in the long-term functioning of a geographical information system. Cartographica 21, p. 79, 1984.
Economist, The: Damned List. 16, 23 November, 1996.
Efron, B.: Missing data, imputation, and the bootstrap. Technical Report No. 153, Division of Biostatistics, Stanford University, Stanford CA, 27 pp. 1992.
Ernst, L.R.: Variance of the Estimated Means for Several Imputation Methods. American Statistical Association. Proceedings of the Section on Survey Research Methods, pp. 716–720 (1980).
Fienberg, Stephen E.: Political Pressure and Statistical Quality: An American Perspective on Producing Relevant National Data. Journal of Official Statistics 5, pp. 207–222 (1989).
Goedegebuure, Robert V.: Global Statistics: EU. Netherlands Official Statistics 11, 38–39 (1996).
Goodchild, Michael F.; Dubue, O.: A model of error for choropleta maps, with applications to geographic information systems. Proceedings of Auto-Carto 8. Falls Church, Va: ASPRS and ACSM, pp. 165–172 (1987).
Goodchild, Michael F.; Min-Hua, W.: Modeling error in raster-based spatial data. Proceedings of the Third International Symposium on Spatial Data Handling. Sydney, pp. 97–106 (1988).
Goodchild, Michael F.; Gopal, S. (eds.): The Accuracy of Spatial Databases. London: Taylor \samp Francis, 1989.
Laaksonen, S.: Correcting for Nonresponse in Household Data. Central Statistical Office of Finland. Tutkimuksia nro 147, pp. 5–12 (n.d.).
Lanke, J.: Hot Deck Imputation Techniques that permit Standard Methods for Assessing Precision of Estimates. Statistics Swednenden. Statistical Review 21, pp. 105–110 (1983).
Little, Roberick J.; Rubin, D.: Statistical Analysis with Missing Data. New York: John Wiley \samp Sons. 1987.
MacEachren, Alan M.: How Maps Work. The Guilford Press, New York, 1995.
Redfern, P.: Which Countries will Follow the Scandinavian Lead in Taking a Register-Based Census of Population. Journal of Official Statistics 2, pp. 415–424 (1986).
Schafer, Joseph L.: Algonthms for Multiple Imputation and Posterior Simulation from Incomplete Multivariate Data with Ignorable Nonresponse. Ph.D. Dissertation, Department of Statistics, Harvard University. 1991.
Short, J.R.; Kim, Y.; Kuus, M.; Wells, H.: The Dirty Little Secret of World Cities Research: Data Problems in Comparative Analysis. International Journal of Urban and Regional Research 20, 697–717 (1996).
Simon, H.: Prediction and Prescription in Systems Modeling. Operations Research 38, pp. 7–14 (1990).
Statistics Canada: Statistics Canada's Policy on Informing Users of Data Quality and Methodology. Journal of Official Statistics 3, pp. 83–92 (1987).
U.S. Department of Commerce: Census of Population and Housing 1980: Public Use Microdata Samples Technical Documentation / prepared by the Data User Services Division, Bureau of the Census.—Washington: The Bureau, 1983. Comparable documentation exists for the 1990 census. 1983.
Van Bochove, Cornelis A.: From Assembly Line to Electronic Highway Junction: A time-track Transformation of the Statistical Process. Netherlands Official Statistics 11, 5–36 (1996).
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems, Man, and Cybernetics 3, 28–44 (1973).
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, Parts 1, 2, 3. Information Sciences 8: 199–249, 301–357; 9: 43–80 (1975).
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Williams, A.V. Soft numbers: problems with the quality of official data. GeoJournal 44, 309–320 (1998). https://doi.org/10.1023/A:1006821206790
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DOI: https://doi.org/10.1023/A:1006821206790