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Examination of Covariance Structures for Experimental Design with Repeated Measure

Received: 3 April 2020     Accepted: 3 May 2020     Published: 27 May 2020
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Abstract

This research examine different covariance structures for experimental design with repeated measure data. Multiple responses taken sequentially from same experimental unit at different periods of time for quantitative data are referred as Repeated Measurement. Weight of 105 broilers in grams for six group obtained from jewel farm Gombe were used as research materials/data. Eleven different covariance structures including the modified one (UN, UNC, TOEP, TOEPH, ANTE(1), AR(1), ARH(1), CS, CSH, HF and ARFA(1)) were examined. AIC, AICC, BIC, HQIC, CAIC and the modified criteria ASIC were used to examine covariance structures and bring the best among them using the named information criteria. The result shows that sphericity assumptions was violated a such the best covariance structure was ARH(1) while the least structure was CSH. Also on the basis of goodness of fit criteria HQIC was found to be the best information criteria. When examined the best information criteria and covariance structure with the modified ones, the modified ASIC and ARFA(1) found to be the best. In conclusion examine different covariance structures with repeated measure data give a very good result defending on the kind of data.

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3)
DOI 10.11648/j.ajtas.20200903.16
Page(s) 63-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Covariance Structures. Experimental Design, Repeated Measures, Information Criteria, Sphericity Test

References
[1] J. Algina, R. R. Wilcox and R. K. Kowalchuk, "The Analysis of Repeated Measure. A Quantitative Research Synthetic," British Journal of Mathematical and Statistical Psychology, pp. 1735-1748, 2000.
[2] N. K. Rauf, "Classification of Multivariate Repeated Measures data with Temporal Autocorrelation," Advances in Data Analysis and Classifiation, pp. 175-199, 2008.
[3] L. A. Guttman, New Approach to Factor Analysis: The Radex in P. F. Lazarsfeld (Ed.), Mathematical thinking in the Social sciences, New York: Columbia University Press, 1954.
[4] L. Brandon, Misspecification of the Covariance Matrix in the Linear Mixed Model: A Monte Carlo Simulation, MN, USA: University of Minnesota, 2013.
[5] D. J. Yanosky II, Comparability of Covariance Structures and Accuracy of Information Criteria in Mixed Model Methods for Longitudinal Data Analysis., Athens, Georgia: University of Georgia Press, 2007.
[6] J. Ferron, R. Dailey and Q. Yi, "Effect of Misspecifying the First Level error Structure in Two Level Models of Change," Multivariate Behav. Res., pp. 379-403, 2002.
[7] S. D. Maxwell and H. D. Delaney, Designing Experiments and Analyzing Data: A Model Comparison Perspective, 2nd ed., Mahwah, NJ: Laurence Erlbaum Associated Publishers, 2004.
[8] E. R. Girden, ANOVA: Repeated Measures, Sage: Newbury Park CA, 1992.
[9] S. W. Greenhouse and S. Geisser, "On Methods in the Analysis of Profile Data," Psychometrika, pp. 95-112, 1959.
[10] H. Huynh and L. S. Feldt, "Estimation of the Box Correction for Degrees of Freedom from Sample Data in the Randomized Block and Split Plot Design," Journal of Educational Statistics, pp. 15-51, 1976.
[11] R. P. McDonald, Factor Analysis and Related Methods, Lawrence Erlbaum Associates, 1985.
[12] S. A. Mulaik, The Foundation of Factor Analysis, New York: McGray Hill, 1972.
[13] R. I. Jennrich and M. D. Schluchter, "Unbalanced Repeated Measures Models with Structured Covariance Matrix," Biometrics, pp. 805-820, 1986.
[14] R. C. Littell, G. A. Milleken, W. W. Stroup and R. D. Wolfinger, SAS System for Mixed Models, Cary: NC; SAS Institute, 1996.
Cite This Article
  • APA Style

    Abraham Okolo, Saidu Sauta Abdulkadir, Ikeme John Dike, Abubakar Adamu. (2020). Examination of Covariance Structures for Experimental Design with Repeated Measure. American Journal of Theoretical and Applied Statistics, 9(3), 63-73. https://doi.org/10.11648/j.ajtas.20200903.16

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    ACS Style

    Abraham Okolo; Saidu Sauta Abdulkadir; Ikeme John Dike; Abubakar Adamu. Examination of Covariance Structures for Experimental Design with Repeated Measure. Am. J. Theor. Appl. Stat. 2020, 9(3), 63-73. doi: 10.11648/j.ajtas.20200903.16

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    AMA Style

    Abraham Okolo, Saidu Sauta Abdulkadir, Ikeme John Dike, Abubakar Adamu. Examination of Covariance Structures for Experimental Design with Repeated Measure. Am J Theor Appl Stat. 2020;9(3):63-73. doi: 10.11648/j.ajtas.20200903.16

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  • @article{10.11648/j.ajtas.20200903.16,
      author = {Abraham Okolo and Saidu Sauta Abdulkadir and Ikeme John Dike and Abubakar Adamu},
      title = {Examination of Covariance Structures for Experimental Design with Repeated Measure},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {9},
      number = {3},
      pages = {63-73},
      doi = {10.11648/j.ajtas.20200903.16},
      url = {https://doi.org/10.11648/j.ajtas.20200903.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200903.16},
      abstract = {This research examine different covariance structures for experimental design with repeated measure data. Multiple responses taken sequentially from same experimental unit at different periods of time for quantitative data are referred as Repeated Measurement. Weight of 105 broilers in grams for six group obtained from jewel farm Gombe were used as research materials/data. Eleven different covariance structures including the modified one (UN, UNC, TOEP, TOEPH, ANTE(1), AR(1), ARH(1), CS, CSH, HF and ARFA(1)) were examined. AIC, AICC, BIC, HQIC, CAIC and the modified criteria ASIC were used to examine covariance structures and bring the best among them using the named information criteria. The result shows that sphericity assumptions was violated a such the best covariance structure was ARH(1) while the least structure was CSH. Also on the basis of goodness of fit criteria HQIC was found to be the best information criteria. When examined the best information criteria and covariance structure with the modified ones, the modified ASIC and ARFA(1) found to be the best. In conclusion examine different covariance structures with repeated measure data give a very good result defending on the kind of data.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Examination of Covariance Structures for Experimental Design with Repeated Measure
    AU  - Abraham Okolo
    AU  - Saidu Sauta Abdulkadir
    AU  - Ikeme John Dike
    AU  - Abubakar Adamu
    Y1  - 2020/05/27
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ajtas.20200903.16
    DO  - 10.11648/j.ajtas.20200903.16
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 63
    EP  - 73
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20200903.16
    AB  - This research examine different covariance structures for experimental design with repeated measure data. Multiple responses taken sequentially from same experimental unit at different periods of time for quantitative data are referred as Repeated Measurement. Weight of 105 broilers in grams for six group obtained from jewel farm Gombe were used as research materials/data. Eleven different covariance structures including the modified one (UN, UNC, TOEP, TOEPH, ANTE(1), AR(1), ARH(1), CS, CSH, HF and ARFA(1)) were examined. AIC, AICC, BIC, HQIC, CAIC and the modified criteria ASIC were used to examine covariance structures and bring the best among them using the named information criteria. The result shows that sphericity assumptions was violated a such the best covariance structure was ARH(1) while the least structure was CSH. Also on the basis of goodness of fit criteria HQIC was found to be the best information criteria. When examined the best information criteria and covariance structure with the modified ones, the modified ASIC and ARFA(1) found to be the best. In conclusion examine different covariance structures with repeated measure data give a very good result defending on the kind of data.
    VL  - 9
    IS  - 3
    ER  - 

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Author Information
  • Department of Statistics and Operations Research, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Statistics and Operations Research, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Statistics and Operations Research, School of Physical Sciences, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Mathematics, Faculty of Science, Gombe State University, Gombe, Nigeria

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