IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1748-d1663945.html
   My bibliography  Save this article

Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient

Author

Listed:
  • Shilai Xian

    (Department of Mathematic and Statistics, Yunnan University, Kunming 650000, China)

  • Li Zhang

    (Department of Mathematic and Statistics, Yunnan University, Kunming 650000, China)

Abstract

This study proposes a standardized robustness measurement framework for comprehensive evaluation models based on the Intraclass Correlation Coefficient (ICC(3,1)), The framework aims to address two key issues: (1) the non-unique evaluation results caused by the abundance of such models, and (2) the lack of standardization and the arbitrariness in existing robustness testing procedures. Theoretical derivation and simulation confirm that ICC(3,1) exhibits a positive correlation with Kendall’s Coefficient of Concordance (Kendall’s W) and a negative correlation with Root Mean Square Error (RMSE) and Normalized Inversion Index (NII), demonstrating superior stability, discrimination, and interpretability. Under increased noise levels, ICC(3,1) maintains a balance between robustness and sensitivity, supporting its application in robustness evaluation and method selection.

Suggested Citation

  • Shilai Xian & Li Zhang, 2025. "Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient," Mathematics, MDPI, vol. 13(11), pages 1-20, May.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1748-:d:1663945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1748/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/11/1748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paolo Paruolo & Michaela Saisana & Andrea Saltelli, 2013. "Ratings and rankings: voodoo or science?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 609-634, June.
    2. James Foster & Mark McGillivray & Suman Seth, 2012. "Rank Robustness of Composite Indices: Dominance and Ambiguity," OPHI Working Papers 26b, Queen Elizabeth House, University of Oxford.
    3. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
    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. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2018. "σ-µ efficiency analysis: A new methodology for evaluating units through composite indices," MPRA Paper 83569, University Library of Munich, Germany.
    2. Rachel M. Gisselquist, 2013. "Evaluating Governance Indexes: Critical and Less Critical Questions," WIDER Working Paper Series wp-2013-068, World Institute for Development Economic Research (UNU-WIDER).
    3. Andrea Saltelli & Arnald Puy, 2023. "What can mathematical modelling contribute to a sociology of quantification?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    4. Gisselquist, Rachel M., 2013. "Evaluating Governance Indexes: Critical and Less Critical Questions," WIDER Working Paper Series 068, World Institute for Development Economic Research (UNU-WIDER).
    5. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    6. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    7. Giuseppe Coco & Raffaele Lagravinese & Giuliano Resce, 2020. "Beyond the weights: a multicriteria approach to evaluate inequality in education," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(4), pages 469-489, December.
    8. Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach," Economies, MDPI, vol. 9(4), pages 1-17, October.
    9. Cantone, Giulio Giacomo & Tomaselli, Venera, 2024. "On the Coherence of Composite Indexes: Multiversal Model and Specification Analysis for an Index of Well-Being," MetaArXiv d5y26, Center for Open Science.
    10. Dorota Weziak-Bialowolska, 2016. "Spatial Variation in EU Poverty with Respect to Health, Education and Living Standards," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(2), pages 451-479, January.
    11. Matheus Pereira Libório & Lívia Maria Leite Silva & Petr Iakovlevitch Ekel & Letícia Ribeiro Figueiredo & Patrícia Bernardes, 2022. "Consensus-Based Sub-Indicator Weighting Approach: Constructing Composite Indicators Compatible with Expert Opinion," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1073-1099, December.
    12. Anna Matysiak & Dorota Węziak-Białowolska, 2016. "Country-Specific Conditions for Work and Family Reconciliation: An Attempt at Quantification," European Journal of Population, Springer;European Association for Population Studies, vol. 32(4), pages 475-510, October.
    13. Jordi Pons-Novell & Montserrat Guillen, 2022. "The Autonomous Capacity of the Elderly Population in Spain for Shopping and Preparing Meals," IJERPH, MDPI, vol. 19(22), pages 1-16, November.
    14. Chao Shi & Kenneth C. Land, 2021. "The Data Envelopment Analysis and Equal Weights/Minimax Methods of Composite Social Indicator Construction: a Methodological Study of Data Sensitivity and Robustness," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 16(4), pages 1689-1716, August.
    15. María del Carmen Bas & Stefano Tarantola & José Miguel Carot & Andrea Conchado, 2017. "Sensitivity Analysis: A Necessary Ingredient for Measuring the Quality of a Teaching Activity Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(3), pages 931-946, April.
    16. Ziwei Shu & Ramón Alberto Carrasco & Javier Portela García-Miguel & Manuel Sánchez-Montañés, 2022. "Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo," Mathematics, MDPI, vol. 10(12), pages 1-28, June.
    17. See, Kok Fong & Ng, Ying Chu & Yu, Ming-Miin, 2022. "An alternative assessment approach to national higher education system evaluation," Evaluation and Program Planning, Elsevier, vol. 94(C).
    18. Yelin Fu & Kong Xiangtianrui & Hao Luo & Lean Yu, 2020. "Constructing Composite Indicators with Collective Choice and Interval-Valued TOPSIS: The Case of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 117-135, November.
    19. Vladimír Holý & Jan Zouhar, 2022. "Modelling time‐varying rankings with autoregressive and score‐driven dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1427-1450, November.
    20. Martin Schlossarek & Miroslav Syrovátka & Ondřej Vencálek, 2019. "The Importance of Variables in Composite Indices: A Contribution to the Methodology and Application to Development Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(3), pages 1125-1160, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jmathe:v:13:y:2025:i:11:p:1748-:d:1663945. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.