IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v19y2018i4p693-710n6.html
   My bibliography  Save this article

The Choice Of Normalization Method And Rankings Of The Set Of Objects Based On Composite Indicator Values

Author

Listed:
  • Walesiak Marek

    (Wroclaw University of Economics, Department of Econometrics and Computer Science, Jelenia Góra. Poland .)

Abstract

The choice of the normalization method is one of the steps for constructing a composite indicator for metric data (see, e.g. Nardo et al., 2008, pp. 19-21). Normalization methods lead to different rankings of the set of objects based on composite indicator values. In the article 18 normalization methods and 5 aggregation measures (composite indicators) were taken into account. In the first step the groups of normalization methods, leading to identical rankings of the set of objects, were identified. The considerations included in Table 3 reduce this number to 10 normalization methods. Next, the article discusses the procedure which allows separating groups of normalization methods leading to similar rankings of the set of objects separately for each composite indicator formula. The proposal, based on Kendall’s tau correlation coefficient (Kendall, 1955) and cluster analysis, can reduce the problem of choosing the normalization method. Based on the suggested research procedure the simulation results for five composite indicators and ten normalization methods were presented. Moreover, the proposed approach was illustrated by an empirical example. Based on the analysis of the dendrograms three groups of normalization methods were separated. The biggest differences in the results of linear ordering refer to methods n2, n9a against the other normalization methods.

Suggested Citation

  • Walesiak Marek, 2018. "The Choice Of Normalization Method And Rankings Of The Set Of Objects Based On Composite Indicator Values," Statistics in Transition New Series, Polish Statistical Association, vol. 19(4), pages 693-710, December.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:4:p:693-710:n:6
    DOI: 10.21307/stattrans-2018-036
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2018-036
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2018-036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Glenn Milligan & Martha Cooper, 1988. "A study of standardization of variables in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 181-204, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giuseppe RICCIARDO LAMONICA, 2002. "La funzionalita' nelle zone omogenee delle Marche," Working Papers 165, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    3. Dawid Majcherek & Marzenna Anna Weresa & Christina Ciecierski, 2020. "Understanding Regional Risk Factors for Cancer: A Cluster Analysis of Lifestyle, Environment and Socio-Economic Status in Poland," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    4. Karolina Pawlak & Luboš Smutka & Pavel Kotyza, 2021. "Agricultural Potential of the EU Countries: How Far Are They from the USA?," Agriculture, MDPI, vol. 11(4), pages 1-21, March.
    5. Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    6. Anca Gabriela Ilie & Marinela Luminita Emanuela Zlatea & Cristina Negreanu & Dan Dumitriu & Alma Pentescu, 2023. "Reliance on Russian Federation Energy Imports and Renewable Energy in the European Union," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 780-780, August.
    7. Smith, Lindsey P. & Ng, Shu Wen & Popkin, Barry M., 2014. "No time for the gym? Housework and other non-labor market time use patterns are associated with meeting physical activity recommendations among adults in full-time, sedentary jobs," Social Science & Medicine, Elsevier, vol. 120(C), pages 126-134.
    8. Raquel Lourenço Carvalhal Monteiro & Valdecy Pereira & Helder Gomes Costa, 2019. "Analysis of the Better Life Index Trough a Cluster Algorithm," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 477-506, April.
    9. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.
    10. Vincent Claude B & Eastman Byron, 2009. "Defining the Style of Play in the NHL: An Application of Cluster Analysis," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-23, January.
    11. Marek Walesiak, 2018. "The Choice Of Normalization Method And Rankings Of The Set Of Objects Based On Composite Indicator Values," Statistics in Transition New Series, Polish Statistical Association, vol. 19(4), pages 693-710, December.
    12. Dauda Usman & Ismail Mohamad, 2013. "A Novel Center Point Initialization Technique for K-means Clustering Algorithm," Modern Applied Science, Canadian Center of Science and Education, vol. 7(9), pages 1-10, September.
    13. Mitra, Suman & Yao, Mingqi & Ritchie, Stephen G., 2021. "Gender differences in elderly mobility in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 203-226.
    14. Roman Seidl & Corinne Moser & Michael Stauffacher & Pius Krütli, 2013. "Perceived Risk and Benefit of Nuclear Waste Repositories: Four Opinion Clusters," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1038-1048, June.
    15. Masayoshi Oka, 2022. "Census-Tract-Level Median Household Income and Median Family Income Estimates: A Unidimensional Measure of Neighborhood Socioeconomic Status?," IJERPH, MDPI, vol. 20(1), pages 1-23, December.
    16. Michael C. Thrun & Alfred Ultsch, 2021. "Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 280-312, July.
    17. Douglas Steinley & Lawrence Hubert, 2008. "Order-Constrained Solutions in K-Means Clustering: Even Better Than Being Globally Optimal," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 647-664, December.
    18. Brandon Cunningham & Jacob LaRiviere & Casey J. Wichman, 2021. "Clustered into control: Heterogeneous causal impacts of water infrastructure failure," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1417-1439, July.
    19. Martin Christian Höcker & Yassien Bachtal & Andreas Pfnür, 2022. "Work from home: bane or blessing? Implications for corporate real estate strategies [Work from Home: Fluch oder Segen? Implikationen für das betriebliche Immobilienmanagement]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 8(2), pages 101-137, October.
    20. Hongyu Zheng & Yuefei Zhuo & Zhongguo Xu & Cifang Wu & Jianhong Huang & Qi Fu, 2021. "Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai," Growth and Change, Wiley Blackwell, vol. 52(4), pages 2509-2539, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:stintr:v:19:y:2018:i:4:p:693-710:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.