IDEAS home Printed from https://ideas.repec.org/p/hhs/oruesi/2008_006.html
   My bibliography  Save this paper

Comparison of methods in the analysis of dependent ordered catagorical data

Author

Listed:
  • Högberg, Hans

    (Centre for Research and Development, Uppsala University and Country,Council of Gävleborg, Sweden)

  • Svensson, Elisabeth

    (Department of Business, Economics, Statistics and Informatics)

Abstract

Rating scales for outcome variables produce categorical data which are often ordered and measurements from rating scales are not standardized. The purpose of this study is to apply commonly used and novel methods for paired ordered categorical data to two data sets with different properties and to compare the results and the conditions for use of these models. The two applications consist of a data set of inter-rater reliability and a data set from a follow-up evaluation of patients. Standard measures of agreement and measures of association are used. Various loglinear models for paired categorical data using properties of quasi-independence and quasi-symmetry as well as logit models with a marginal modelling approach are used. A nonparametric method for ranking and analyzing paired ordered categorical data is also used. We show that a deeper insight when it comes to disagreement and change patterns may be reached using the nonparametric method and illustrate some problems with standard measures as well as parametric loglinear and logit models. In addition, the merits of the nonparametric method are illustrated.

Suggested Citation

  • Högberg, Hans & Svensson, Elisabeth, 2008. "Comparison of methods in the analysis of dependent ordered catagorical data," Working Papers 2008:6, Örebro University, School of Business.
  • Handle: RePEc:hhs:oruesi:2008_006
    as

    Download full text from publisher

    File URL: https://www.oru.se/globalassets/oru-sv/institutioner/hh/workingpapers/workingpapers2008/wp-6-2008.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alan Agresti & Ranjini Natarajan, 2001. "Modeling Clustered Ordered Categorical Data: A Survey," International Statistical Review, International Statistical Institute, vol. 69(3), pages 345-371, December.
    2. J. Richard Landis & Gary G. Koch, 1975. "A review of statistical methods in the analysis of data arising from observer reliability studies (Part II)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 29(4), pages 151-161, December.
    3. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    4. J. Richard Landis & Gary G. Koch, 1975. "A review of statistical methods in the analysis of data arising from observer reliability studies (Part I)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 29(3), pages 101-123, 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. Högberg, Hans & Svensson, Elisabeth, 2008. "An Overview of Methods in the Analysis of Dependent ordered catagorical Data: Assumptions and Implications," Working Papers 2008:7, Örebro University, School of Business.
    2. Debby L Gerritsen & Nardi Steverink & Dinnus HM Frijters & Marcel E Ooms & Miel W Ribbe, 2010. "Social well‐being and its measurement in the nursing home, the SWON‐scale," Journal of Clinical Nursing, John Wiley & Sons, vol. 19(9‐10), pages 1243-1251, May.
    3. N. Lu & T. Chen & P. Wu & D. Gunzler & H. Zhang & H. He & X.M. Tu, 2014. "Functional response models for intraclass correlation coefficients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2539-2556, November.
    4. Christof Schuster & David Smith, 2005. "Dispersion-weighted kappa: An integrative framework for metric and nominal scale agreement coefficients," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 135-146, March.
    5. Fabio Rapallo, 2005. "Algebraic exact inference for rater agreement models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 45-66, February.
    6. Li, Yonghai & Schafer, Daniel W., 2008. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3474-3492, March.
    7. Cheng, Song-Show & Cheng, Yu-Chun, 1998. "An ordered relation between the ANOVA estimator of the intraclass correlation and a kappa-type statistic in binary data," Statistics & Probability Letters, Elsevier, vol. 38(3), pages 275-280, June.
    8. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    9. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    10. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    11. Bambio, Yiriyibin & Bouayad Agha, Salima, 2018. "Land tenure security and investment: Does strength of land right really matter in rural Burkina Faso?," World Development, Elsevier, vol. 111(C), pages 130-147.
    12. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    13. Meltem Ucal & Simge Günay, 2022. "Household Happiness and Fuel Poverty: a Cross-Sectional Analysis on Turkey," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(1), pages 391-420, February.
    14. Fuks, Mauricio & Salazar, Esther, 2008. "Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil," Energy Economics, Elsevier, vol. 30(4), pages 1672-1692, July.
    15. Brajendra C. Sutradhar, 2018. "Semi-parametric Dynamic Models for Longitudinal Ordinal Categorical Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 80-109, February.
    16. Carlos Alberto GÓMEZ SILVA, 2014. "Clasificación de colegios según las Pruebas SABER 11 del ICFES en el Período 2001-2011: un Análisis Longitudinal a Través del Uso de Modelos Marginales (MM)," Archivos de Economía 12314, Departamento Nacional de Planeación.
    17. Nádia Simões & Nuno Crespo & Sandrina B. Moreira & Celeste A. Varum, 2016. "Measurement and determinants of health poverty and richness: evidence from Portugal," Empirical Economics, Springer, vol. 50(4), pages 1331-1358, June.
    18. Wei, Zheng & Kim, Daeyoung, 2021. "On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    19. Eleni Matechou & Ivy Liu & Daniel Fernández & Miguel Farias & Bergljot Gjelsvik, 2016. "Biclustering Models for Two-Mode Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 611-624, September.
    20. Rasheed A. Adeyemi & Temesgen Zewotir & Shaun Ramroop, 2016. "Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria," IJERPH, MDPI, vol. 13(11), pages 1-22, November.

    More about this item

    Keywords

    Agreement:ordinal data; ranking; reliability.rating scales;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:oruesi:2008_006. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/ieoruse.html .

    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.