Abstract
The problem of testing statistical hypotheses of independence of two multiresponse variables is considered. The final aim of the investigation is the formation of methods that discover relevant information existing in a database with (given) population survey results. We show in this research that a formulation of null hypothesis has an impact on p-values of a given subset of data. It is possible therefore to establish: a significant dependence of responses in one sense and a lack of such dependence in another sense. Such a discovery can be meaningful for producers of questionnaire surveys. Specific algorithms for automated data mining can be formulated that consider specific null hypotheses on independency or dependency of survey questions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agresti A., Liu L-M, “Modelling a categorical variable allowing arbitrary many category choices”, Biometrics 55, 936–943, 1999.
Andersen E. B., “The statistical analysis of categorical data”, Springer Verlag, 1990.
Bilder C. R., Loughin T. M., Nettleton D., “Multiple marginal independence testing for pick any/C variables”, Commun. Statist.-Simula., 29, 1285–1316, 2000.
Bilder C R., Loughin T. M., “On the first-order Rao-Scott correction of the Umesh-loughin-Sherer statistic”, Biometrics 57, 1253–1255, 2001.
Bilder C. R., Loughin T. M., “Testing for conditional multiple marginal independence”, Biometrics 58, pp. 200–208, 2002.
Bishop Y. M. M., Fienberg S. E., Holland P. W., “Discrete Multivariate Analysis: Theory and Practice, Cambridge-Mass. The MIT Press, 1975.
Decady Y. J., Thomas D. R., “A simple test of association for contingency tables with multiple column responses”, Biometrics 56, 893–896, 2000.
Klopotek M.A., “Methods of Identification and Interpretations of Belief Distributions in the Dempster-Shafer Theory” (in Polish), Publisher: Instytut Podstaw Informatyki PAN, Warszawa 1998.
Loughin T., Scherer P. N., “Testing association in contingency with multiple column responses”, Biometrics 54, 630–637, 1998.
Matuszewski A., Trojanowski K., “Models of multiple response independence”, in: M. A. Klopotek, M. Michalewicz, S. T. Wierzchon (ed.), “Intelligent Information Systems 2001”, Physica-Verlag (Springer), pp.209–219, 2001.
Mirkin B., “Eleven ways to look at the chi-squared coefficient for contingency tables”, American Statistician, 55, pp. 111–120, 2001.
Olesen K. G., Causal probabilistic networks with both discrete and continuous variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(15), 1993.
Spirtes P., Glymour C., Scheines R., “Causation, prediction and search”, Lecture Notes in Statistics 81, 1993
Thomas D. R., Decady Y. J., “Analyzing categorical data with multiple responses per subject”, SSC Annual Meeting, Proceedings of the Survey Methods Section, 121–130, 2000.
Wierzchoń S T., Kłopotek M.A.: “Evidential Reasoning. An Interpretative Investigation.” Publisher: Wydawnictwo Akademii Podlaskiej, Siedlce, 2002
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bali, G.C., Matuszewski, A., Kłopotek, M.A. (2003). Dependence of Two Multiresponse Variables: Importance of The Counting Method. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36562-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-36562-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00843-9
Online ISBN: 978-3-540-36562-4
eBook Packages: Springer Book Archive