Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | 14 | nr 2 | 287--318
Tytuł artykułu

Coherence and Comparability as Criteria of Quality Assessment in Business Statistics

Treść / Zawartość
Warianty tytułu
Języki publikacji
The problems of coherence and comparability exceed the classical notion of analysis of survey errors, because they do not concern single surveys or variables but the question of how results of two or more surveys can be used together and how relevant data can effectively be compared to obtain a better picture of social and economic phenomena over various aspects, e.g. space or time. This paper discusses characteristics of the main concepts of coherence and comparability as well as a description of differences and similarities between these two notions. Types of coherence and various aspects of perception of these notions in business statistics are analysed. Main sources of lack of coherence and comparability, factors affecting them (e.g. methodology, time, region, etc.) and methods of their measurement in context of information obtained from businesses will be also presented. (original abstract)
Opis fizyczny
  • Statistical Office in Poznan, Poland
  • BARCELLAN, R., (2005). The use of benchmarking techniques in the compilation of the European quarterly national accounts situation and perspectives, Working Papers and Studies, European Commission, Euro Indicators, Office for Official Publications of the European Communities, Luxembourg, available at
  • BERGDAHL, M., BLACK, O., BOWATER, R., CHAMBERS, R., DAVIES, P., DRAPER, D., ELVERS, E., FULL, S., HOLMES, D., LUNDQVIST, P., LUNDSTROM, S, NORDBERG, L., PERRY, J., PONT, M., PRESTWOOD, M., RICHARDSON, I., SKINNER, CH., SMITH, P., UNDERWOOD, C., WILLIAMS, M., (2001). Model Quality Report in Business Statistics, General Editors: P. Davies, P. Smith,
  • BRUZZONE, L., ROLI, F., SERPICO, S. B., (1995). An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, pp. 1318- 1321.
  • CASCIANO, M. C., DE GIORGI, V., OROPALLO, F., SIESTO, G., (2012). Estimation of Structural Business Statistics for Small Firms by Using Administrative Data, Rivista Di Statistica Ufficiale No. 2-3, Instituto Nazionale Di Statistica, pp. 55-74.
  • COOK, L., (2007). International experience in setting up an economic statistics compilation programme, Regional Workshop for African countries on Compilation of Basic Economic Statisticsjointly organized by United Nations Statistics Division (UNSD) and African Centre for Statistics at Economic Commission for Africa (ACS), United Nations, Department of Economic and Social Affairs, Statistics Division, 16th - 19th October 2007, Addis-Ababa, Ethiopia, Doc. No. ESA/STAT/AC.136.3, document available in the Internet at the website %20documents/WS-BES-ECA-136-3-Intl-experience-Len%20Cook.pdf.
  • DAVIES, P., (2000). Assessing the Quality of Business Statistics, Office for National Statistics, Proceedings from the Second International Conference on Establishment Surveys, June 17th - 21st, 2000, Buffalo, New York,
  • DESTATIS, (2008). National Accounts Quarterly Calculations of Gross Domestic Product in accordance with ESA 1995 - Methods and Data Sources. New version following revision 2005, Fachserie 18 Series S. 23, Statistisches Bundesamt (Federal Statistical Office), Wiesbaden, Germany, CalculationsGrossDomesticProductAccordance.pdf?_blob=publicationFile.
  • EUROSTAT, (2009). ESS Handbook for Quality Reports, Series: Methodologies and Working papers, Office for Official Publications of the European Communities, Luxembourg.
  • EUROSTAT, (2008). Quality Control of Urban Audit Variables, Unit D2, Office for Official Publications of the European Communities, Luxembourg, April 2008.
  • EUROSTAT, (2003). Handbook "How To Make A Quality Report", Series: Methodological Documents, Working Group "Assessment of quality in statistics", Sixth meeting, Luxembourg, 2nd - 3rd October 2003, Document No. Doc. Eurostat/A4/Quality/03/Handbook, available at the website nto%20de%20soporte/Methodological%20documents%20handbook %20%20 how%20to%20make%20a%20quality%20report%20-%20NACIONES%20UNIDAS.pdf.
  • EUROSTAT, (2005). European Statistics Code of Practice for the National and Community Statistical Authorities, Statistical Office of European Union, Eurostat, Luxembourg, available at dnss/docViewer.aspx?docID=2636.
  • GARCÍA LAPRESTA, J., MARTÍNEZ PANERO, M., (2002). Borda Count Versus Approval Voting: A Fuzzy Approach, Public Choice 112(1-2), pp. 167-184.
  • GATEW, K., (1977). Статистическо характеризание на структурнии измениения, Трудовэ на Висшия, Икономический Институт - К. Маркс, София, vol. 3, pp. 10-42 (in Russian).
  • IURCOVICH, L., KOMNINOS, N., REID, A., HEYDEBRECK, P., PIERRAKIS, Y., (2006). Mutual Learning Platform. Regional Benchmarking Report. Blueprint for Regional Innovation Benchmarking, European Commission, Committee of the Regions, IRE Innovation Network, available at
  • JÓZEFOWSKI, T., MLODAK, A., (2009). Observation of flows of population in Polish statistics - problems and challenges, [in:] E. Elsner, H. Michel (eds.) "Assistance for the Younger Generation. Statistics and Planning in Big Agglomerations", Institut für Angewandte Demographie IFAD, Berlin, pp. 61-76.
  • KAZINIEC, L. S., (1968). О методах сводной оценки стуктурных сдвигов, Вестник Статистики, No. 11 (in Russian).
  • KÖRNER, T., PUCH, K., (2011). Statistics and Science. Coherence of German Labour Market Statistics, Volume 19, Statistisches Bundesamt (Federal Statistical Office), Wiesbaden. Available at
  • erenceLabourMarket1030819119004.pdf?_blob=publicationFile.
  • LITVAK, B., (1983). Distances and consensus rankings, l Cybernetics and systems analysis, 19 (1), 71{81. (Translated from Kibernetika, No. 1, pp. 5763, January-February, 1983).
  • MALINA, A., ZELIAS, A., (1998). On Building Taxonometric Measures on Living Conditions, Statistics in Transition, vol. 3, pp. 523-544.
  • MLODAK, A., (2006a). Taxonomic analysis in regional statistics, ed. by DIFIN -Advisory and Information Centre, Warszawa, Poland (in Polish).
  • MLODAK, A., (2006b). Multilateral normalisations of diagnostic features, Statistics in Transition, vol. 7, pp. 1125-1139.
  • MLODAK, A., (2002). An Approach to the Problem of Spatial Differentiation of Multi-feature Objects Using Methods of Game Theory, Statistics in Transition, Vol. 5, pp. 857-872.
  • OECD, (2008). Handbook on Constructing Composite Leading Indicators: Methodology and User Guide, Global Inventory of Statistical Standards, Organization for Economic Cooperation and Development, link:
  • STATCAN, (2006). Metadata to Support the Survey Life Cycle, Invited Paper, Joint UNECE/Eurostat/OECD work session on statistical metadata (METIS), Topic (iii): Metadata and the Statistical Cycle, Submitted by Statistics Canada for the Conference of European Statisticians, United Nations Statistical Commission and Economic Commission for Europe, European Commission Statistical Office of the European Communities (Eurostat) Organisation For Economic Cooperation and Development (OECD), Statistics Directorate, Geneva, 3rd - 5th April 2006, p.5.e.pdf.
  • DE WAAL, T., PANNEKOEK, J., SCHOLTUS, S., (2011). Handbook of Statistical Data Editing and Imputation, John Wiley & Sons, Inc., Hoboken, New Jersey.
  • YANCHEVA, D., ISKROVA, K., (2011). Reducing the administrative burden for the business in Bulgaria: Single Entry Point for Reporting Fiscal and Statistical Information, [in:] Proceedings from BLUE-ETS Conference on Burden and Motivation in Official Business Surveys Statistics Netherlands, Heerlen, March 22 & 23, 2011, pp. 189-198.
  • ZELIAS, A., (2002). Some Notes on the Selection of Normalization of Diagnostic Variables, Statistics in Transition, vol. 5, pp. 787-802.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.