Usage as Complementary Correspondence Analysis and Logistic Regression in a Scientific Survey on Self Healing Methods

DOI: 10.4236/ojs.2014.411086   PDF   HTML   XML   2,950 Downloads   3,560 Views   Citations


The aim of this study is to show complementary usage of logistic and correspondence analysis in a research subject to self-healing methodologies. Firstly, the number of the variables is reduced by logistic regression according to relationship between dependent and independent variables and then research carries on searching variables. The relationship among the behaviours of individuals and their demographic characteristics is modelled by logistic regression and shown graphically by correspondence analysis. In application, first of all, the effect of age, sex, marital status, education level, occupation and income level and present health condition, on appreciating self-health, is explained by a model. As a result of that model, it can be said that the effect of age, occupation and present health condition is reasonable. After analysing that model, the relationship between categorical variables (age, sex, occupation, preferred precautions, and worth of personal health) is shown graphically by multiple correspondence analysis.

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Greenacre, Z. , Terlemez, L. and Sentürk, S. (2014) Usage as Complementary Correspondence Analysis and Logistic Regression in a Scientific Survey on Self Healing Methods. Open Journal of Statistics, 4, 912-920. doi: 10.4236/ojs.2014.411086.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Karaca, A.R. (1994) “Kendi Kendini Tedavi Reçetesiz Ilaçve OTC. Turkish Pharmacists’s Association News (TEB Haberler), No: 10, pp. 23-26.
[2] Hosmer, David, W. and Lemeshow, S. (2000) Applied Logistic Regression. John Wiley & Sons, Inc., Hoboken.
[3] O’Connel, A.A. (2007) Logistic Regression models for Ordinal Response Variables. Sage Publication, Inc., Thousand Oaks.
[4] Agresti, A. (1996) An Introduction to Categorical Data Analysis. John Wiley and Sons, New York.
[5] Agresti, A. (1990) Categorical Data Analysis. John Wiley and Sons, New York.
[6] Andersen, E.B. (1990) The Statistical Analysis of Categorical Data. Springer-Verlag, Berlin.
[7] Johnson, A.R. and Wichern, D.W. (1998) Applied Multivariate Statistical Analysis. Prentice-Hall.
[8] Özdamar, K. (2004) Paket Programlar ile Istatistiksel Veri Analizi. Anadolu Üniversitesi Yaylnlarl, Eskisehir.
[9] Greenacre, M.J. and Hastie, T. (1987) The Geometric Interpretation of Correspondence Analysis. American Statistical Association, 82, 437-447.
[10] Greenacre, M.J. (1984) Theory and Applications of Correspondence Analysis. Academic Press, London.
[11] Greenacre, M.J. and Blasius, J. (2006) Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC, New York.
[12] Greenacre, M.J. (2007) Correspondence Analysis in Practice. 2nd Edition, Chapman & Hall/CRC, New York.
[13] Güler, E. and Asan, Z. (2004) Kendi Kendine Tedavive Kisisel Sagllgln Önemi. Meslekiçi Sürekli Egitim Dergisi, No: 7-8, pp. 80-91.

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