Identification of correlation structure using rotated factor loadings

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    This work seeks to identify the correlation structure of variables in terms of few underlying but unobservable factors. The method was applied to age and five different tests results obtained from 200 patients in a hospital. Two factors were identified using the scree plot and the Kaiser criterion. The factor loadings obtained by the method of principal components gave an inadequate fit to the data. An algebraic approach was applied using orthogonal rotation, and the loadings were found to give a clear and interpretable pattern. Consequently, the variables: age, fasting blood sugar and diastolic blood pressure were found to cluster about the first factor F1 called Age-Cardiovascular factor. Similarly, the remaining variables malaria, typhoid and haemoglobin clustered about the second factor F2 and the given name was Hemo-typhomalaria factor. Diagnostic checks were carried out and the factor model generated by the rotated loadings was found to be adequate.

  • Keywords

    Factor Loadings; Orthogonal Matrix; Orthogonal Rotation; Principal Component and Communality.

  • References

      [1] Alexander, S. (2004). Factor Analysis in Environmental Studies. HAIT Journal of Science and Engineering Volume 2 issues 1-2, pp. 54-94.

      [2] Ani, G. and Sean, P. (2013).A Beginner’s Guide to Factor Analysis. Tutorials in Quantitative Methods for Psychology vol. 9(2), pp. 79-94.

      [3] Bernard, P. M. (2012). The Effect of Diabetes on High Blood Pressure. Journal of Medical Science. Vol. 7, Issue 6, pp. 51-59.

      [4] Brett W. (2012).Exploratory Factor Analysis, A first-step guide for Novice Unpublished. Naim Publishers, New York. ISBN: 0-2533-344-7.

      [5] Femke A., Ingrid V., Win D., and Marian V. (2012). On the relationship between blood pressure and haemoglobin. Journal of Medical Statistics, Vol. 10, No. 7; pp. 72-81.

      [6] Hopkin J. (2009) Insulin resistance and Hypertension. American Journal of Psychology H1597-Hi602:2009.

      [7] Ina D., George B. and Frank P. (2010). Type 2 Diabetes mellitus and increased Risk for Malaria infection: Emerging Infectious Diseases. Vol. 16, No. 10, October 2010.

      [8] Kaiser, S. (1960). On Determination of Number of Factors in Factor Analysis. International Journal of Statiatics. Vol. 3; No. 8; pp. 34-47.

      [9] Lutkepol H. (2005): New introduction to multiple Time Series Analysis. Springer Berlin Heidebelg New York. ISBN 3-540-40172-5. SPIN 10932797.

      [10] Madukosiri, G.M. (2012). Illness Pattern and Relationship between the Prevalence of Malaria and other infection. Agriculture and Biological Journal of North America (ABJNA), 2012.3.10.4B.

      [11] Michael W. (2007) an Overview of Analytical Rotation in Exploratory Factor Analysis. International Conference paper on Artificial Neural Network.Vol.4, pp. 57-66.

      [12] Okuhara, K; Titu, S. and Williams, M. (2000). Application of Factor Analysis on volcanic soil constituents. Journal of Geological Sciences. Vol. 8; No. 4; pp. 13-21.

      [13] Oscar, S. and Prasanna, K. (2012). Co-infection of Typhoid and Malaria. Journal of Medical Laboratory and Diagnosis vol. 2(3) pp. 22-26.

      [14] Rechard, K. and Dean, H (1992). The Origin of Factor Analysis. CBMS-NSF Regional Conference Series in Applied Statistics. vol. 64, No. 3, pp. 52-58.

      [15] Sylvia, M. (1997).On vital functions of Haemoglobin. J. Health inst., 19: 61-63.

      [16] Uneke, P. N. (2002). Medline search method for the study of malaria and typhoid. Journal of Health Science. Vol. 5, Issue 5, pp. 332-340. DOI: 103923/jhsci. 2002 332-340.

      [17] Williams, K.; John, M. and Benedict T. (2010). On the identification of factor Analysis. Journal of Research in Physical Sciences. Vol. 6; No. 3; pp. 18-27.




Article ID: 6931
DOI: 10.14419/ijasp.v5i1.6931

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.