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Exploring Partial Order of European Countries

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Abstract

Partial Order Theory has been recently more and more employed in applied science to overcome the intrinsic disadvantage hidden in aggregation, if a multiple attribute system is available. Despite its numerous positive features, there are many practical cases where the interpretation of the partial order can be rather troublesome. In these cases the analysis of underlying dimensions could be useful to uncover particular data structures. The paper shows a way of addressing the problem with the help of an actual case study, which deals with European opinions on services of general interest. In particular, a partial order of countries is firstly provided and then a method to detect dimensions is discussed and applied. The analysis stems directly from the Partially Order Set (poset) and Lattice theory with particular references to dimension theory and Formal Concept Analysis. The study is eventually able to pinpoint role and relevance of different attributes characterizing EU countries which are used to define the partial order.

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Notes

  1. The influence was quantified in terms of W matrix of Hasse diagrams (Annoni 2007; Brüggemann et al. 2001).

  2. On the contrary if you simultaneously consider quality and contract then conflicts would arise only due to these different criteria jointly considered. In this particular case, only Spain and Netherlands come out incomparable since Spain is worse than Netherlands for fix phone contract while it is better than Netherlands for rail quality.

  3. The statement of the theorem is wider, here only some results, useful to our specific case, are reported.

  4. Price of postal service is a very low discriminating item.

References

  • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 12th ACM SIGMOD Conference on Management of data (pp. 207–216) Washington, DC.

  • Annoni, P. (2007). Different ranking methods: Potentialities and pitfalls for the case of European opinion polls. Environmental and Ecological Statistics, 14, 453–471.

    Article  Google Scholar 

  • Annoni, P., & Brüggemann, R. (2008). The dualistic approach of FCA: A further insight into Ontario Lake sediments. Chemosphere, 70(11), 2025–2031.

    Article  Google Scholar 

  • Bartel, H. G., & Brüggemann, R. (1998). Application of formal concept analysis to structure-activity relationships. Fresenius’ Journal of Analytical Chemistry, 361, 23–28.

    Article  Google Scholar 

  • Bartel, H. G., & Nofz, M. (1997). Exploration of NMR data of glasses by means of formal concept analysis. Chemometrics and Intelligent Laboratory Systems, 36, 53–63.

    Article  Google Scholar 

  • Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications for Subjective Survey Data. Economics and Social Behaviour, 91, 67–72.

    Google Scholar 

  • Borg, I., & Shye, S. (1995). Facet theory. Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Brans, J. P., & Vincke, P. H. (1985). A Preference Ranking Organization Method (The PROMETHEE Method for Multiple Criteria Decision - Making). Management Science, 31, 647–656.

    Article  Google Scholar 

  • Brüggemann, R., & Carlsen, L. (2006). Partial order in environmental sciences and chemistry. Berlin: Springer.

    Book  Google Scholar 

  • Brüggemann, R., Halfon, E., Welzl, G., Voigt, K., & Steinberg, C. E. W. (2001). Applying the concept of partially ordered sets on the ranking of near-shore sediments by a battery of tests. Journal of Chemical Information and Computer Sciences, 41, 918–925.

    Google Scholar 

  • Brüggemann, R., Sørensen, P. B., Lerche, D., & Carlsen, L. (2004). Estimation of averaged ranks by a local partial order model. Journal of Chemical Information and Computer Sciences, 44, 618–625.

    Google Scholar 

  • Brüggemann, R., & Voigt, K. (1995). An evaluation of online databases by methods of lattice theory. Chemosphere, 31, 3585–3594.

    Article  Google Scholar 

  • Brüggemann, R., Voigt, K., & Steinberg, C. E. W. (1997). Application of formal concept analysis to evaluate environmental databases. Chemosphere, 35, 479–486.

    Article  Google Scholar 

  • Brüggemann, R., Welzl, G., & Voigt, K. (2003). Order Theoretical Tools for the Evaluation of Complex Regional Pollution Patterns. Journal of Chemical Information and Computer Sciences, 43, 1771–1779.

    Google Scholar 

  • Carlsen, L. (2007). Hierarchical partial order ranking. Environ. Pollut. (in press). Available online 4 Jan 2008. doi:10.1016/j.envpol.2007.11.023.

  • Carlsen, L., & Walker, J. D. (2006). Prioritizing PBT substances. In R. Brüggemann & L. Carlsen (Eds.), Partial order in environmental sciences and chemistry (pp. 153–160). Berlin: Springer.

    Chapter  Google Scholar 

  • Carlsen, L., & Walker, J. D. (2003). QSARs for prioritizing PBT substances to promote pollution prevention. QSAR & Combinatorial Science, 22, 49–57.

    Article  Google Scholar 

  • Carpineto C., & Romano, G. (2004). Concept data analysis theory and applications. England: Wiley.

    Book  Google Scholar 

  • Dushnik, B. & Miller, E. W. (1941). Partially ordered sets. American Journal of Mathematics, 63, 600–610.

    Article  Google Scholar 

  • Eurobarometer 58.0. (2002). September–October 2002. EORG Codebook, European Opinion Research Group.

  • Ganter, B., & Wille, R. (1999). Formal concept analysis—mathematical foundations. Berlin: Springer.

    Google Scholar 

  • Gifi, A. (1990). Nonlinear multivariate analysis. New York: Wiley.

    Google Scholar 

  • Johnson, V. E., & Albert, J. H. (1999). Ordinal data modeling. New York: Springer.

    Google Scholar 

  • Lansdowne, Z. F. (1997). Outranking methods for multicriterion decision making: Arrow’s and Raynaud’s conjecture. Social Choice and Welfare, 14, 125–128.

    Article  Google Scholar 

  • Linting, M., Meulman, J. J., Groenen, P. J. F., & Vanderkooij, A. Y. (2007). Stability of principal component analysis: An empirical study using the balanced bootstrap. Psychol Methods, 12(3), 359–379.

    Article  Google Scholar 

  • Myers, W. L., Patil, G. P., & Cai, Y. (2005). Exploring patterns of habitat diversity across landscapes using partial ordering. Technical Report Number 2005-0701 Technical Reports and Reprints Series, The Pennsylvania State University, PA 16802, 1–34.

  • Patil, G. P., & Taille, C. (2004). Multiple indicators, partially ordered sets and linear extensions: Multi-criterion ranking and prioritization. Environmental and Ecological Statistics, 11, 199–228.

    Article  Google Scholar 

  • Pudenz, S., Brüggemann, R., & Bartel, H. G. (2002). QSAR of ecotoxicological data on the basis of data-driven if-then-rules. Ecotoxicology, 11, 337–342.

    Article  Google Scholar 

  • Shye, S., Elizur, D., & Hoffman, M. (1994). Introduction to facet theory. Content design and intrinsic data analysis in behavioral research, Applied Social Research Methods Series, 35. Thousand Oaks London New Delhi: SAGE Publications.

    Google Scholar 

  • Smith, E. V., Jr., & Smith, R. M. (2004). Introduction to Rasch measurement: Theory, models and applications. Maple Grove, Minnesota: JAM Press.

    Google Scholar 

  • Sørensen, P. B., Brüggemann, R., Carlsen, L., Mogensen, B. B., Kreuger, J., & Pudenz, S. (2003). Analysis of monitoring data of pesticide residues in surface waters using partial order ranking theory—Data interpretation and model development. Environmental Toxicology and Chemistry, 22, 661–670.

    Article  Google Scholar 

  • Trotter, W. T. (1975). Inequalities in dimension theory for posets. Proceedings of the American Mathematical Society, 47, 311–316.

    Article  Google Scholar 

  • Trotter, W. T. (1992). Combinatorics and partially ordered sets: Dimension theory. Baltimore: The Johns Hopkins University Press.

    Google Scholar 

  • Vincke, P. H. (1999). Robust and neutral methods for aggregating preferences into an outranking relation. European Journal of operational Research, 112, 405–412.

    Article  Google Scholar 

  • Voigt, K., Welzl, G., & Brüggemann, R. (2004). Data analysis of environmental air pollution monitoring systems in Europe. Environmetrics, 15, 577–596.

    Article  Google Scholar 

  • Wille, R. (1982). Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets (Series C, Vol. 83, pp. 445–470). Dordrecht: D. Reidel Publishing Company.

  • Wolff, K. E. (1993). A first course in formal concept analysis—How to understand diagrams. In F. Faulbaum (Ed.), SoftStat’93, Advances in Statistical Software, 4 (pp. 429–438).

  • Yevtushenko, S. A. (2000). System of data analysis “Concept explorer”. In Proceedings of the 7th National Conference on Artificial Intelligence (pp. 127–134). Russia, K II-2000.

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Correspondence to Paola Annoni.

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Annoni, P., Brüggemann, R. Exploring Partial Order of European Countries. Soc Indic Res 92, 471–487 (2009). https://doi.org/10.1007/s11205-008-9298-4

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