Research Article
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Year 2019, Volume: 23 Issue: 4, 532 - 540, 01.08.2019
https://doi.org/10.16984/saufenbilder.477181

Abstract

References

  • A. Agresti, Categorical Data Analysis, Third Edition. John Wiley&Sons, Inc., Hoboken, New Jersey, 2012.
  • T.W. Anderson, Probability Model For Analyzing Time Changes in Attitudes.ss.17-66. P.F. Lazarsfeld, ed. 1954. In Mathematical Thinking in Social Science, Glencoe, III., The Free Pres., 1954.
  • T.W. Anderson, L. A., Goodman, Statistical Inference about Markov Chains. Ann. Math. Statistics, 28, 89-110, 1957.
  • M. M. Y. Bishop, E. S. Fienberg, W. P. Holland, Discrete Multivariate Analysis Theory and Practice. Springer, New York, 1974.
  • R. R. Bush, F. Mosteller, Stochastic Models for Learning. John Wiley&Sons, Inc., Hoboken, New Jersey, 1955.
  • F. Eskandar, M. R. Meslikani, Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain. Metrika, 59, 173-191, 2004.
  • L. A. Goodman, Statistical Methods for Analyzing Processes of Change. Amer. J. Sociol., 68, 57-78, 1962.
  • A. Madansky, Test of Homogeneity for Correlated Samples. Jour. American Statist. Assoc., 58, 97-119, 1963.
  • J. K. Vermunt, Log-linear Models for Event Histories., Sage, Thousand Oaks, CA, 1997.
  • A. von Eye, C. Spiel, Standart and nonstandard Log-Linear Symmetry Models for Measuring Change in Categorical Variables, The American Statistician, 50(4), 300-305, 1996.

Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application

Year 2019, Volume: 23 Issue: 4, 532 - 540, 01.08.2019
https://doi.org/10.16984/saufenbilder.477181

Abstract

In this study, the use of the Markov chain to measure the change in
time-dependent transitions is emphasized. Contingency tables were used to
measure the time-dependent change of categorical data. Theoretically how to
apply the Markov chain in the log-linear model with the help of one-step or
higher-step transition matrices was demonstrated. In addition, the stationarity
approach and the assessment of the order of the chain were given as the
assumption of the model. In the real data application, 1217 undergraduate
students, studying in Faculty of Political Science, Engineering, Science
departments of Ankara University, were used. It was taken their cumulative
average grades for 4 years, average grades for 8 semesters, beginning in the
academic year 2013-2014.Whether the change in the success of the students is
measurable in 8 semesters and 4 years, has been investigated. According to the
results, before making any prediction: it concluded that one-step transition
probabilities are not stationary and the three-step transition matrix is the
second-order Markov Chain.

References

  • A. Agresti, Categorical Data Analysis, Third Edition. John Wiley&Sons, Inc., Hoboken, New Jersey, 2012.
  • T.W. Anderson, Probability Model For Analyzing Time Changes in Attitudes.ss.17-66. P.F. Lazarsfeld, ed. 1954. In Mathematical Thinking in Social Science, Glencoe, III., The Free Pres., 1954.
  • T.W. Anderson, L. A., Goodman, Statistical Inference about Markov Chains. Ann. Math. Statistics, 28, 89-110, 1957.
  • M. M. Y. Bishop, E. S. Fienberg, W. P. Holland, Discrete Multivariate Analysis Theory and Practice. Springer, New York, 1974.
  • R. R. Bush, F. Mosteller, Stochastic Models for Learning. John Wiley&Sons, Inc., Hoboken, New Jersey, 1955.
  • F. Eskandar, M. R. Meslikani, Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain. Metrika, 59, 173-191, 2004.
  • L. A. Goodman, Statistical Methods for Analyzing Processes of Change. Amer. J. Sociol., 68, 57-78, 1962.
  • A. Madansky, Test of Homogeneity for Correlated Samples. Jour. American Statist. Assoc., 58, 97-119, 1963.
  • J. K. Vermunt, Log-linear Models for Event Histories., Sage, Thousand Oaks, CA, 1997.
  • A. von Eye, C. Spiel, Standart and nonstandard Log-Linear Symmetry Models for Measuring Change in Categorical Variables, The American Statistician, 50(4), 300-305, 1996.
There are 10 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Research Articles
Authors

Nihan Potas 0000-0002-0393-3135

Cemal Atakan 0000-0001-6943-1675

Publication Date August 1, 2019
Submission Date November 1, 2018
Acceptance Date December 27, 2018
Published in Issue Year 2019 Volume: 23 Issue: 4

Cite

APA Potas, N., & Atakan, C. (2019). Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application. Sakarya University Journal of Science, 23(4), 532-540. https://doi.org/10.16984/saufenbilder.477181
AMA Potas N, Atakan C. Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application. SAUJS. August 2019;23(4):532-540. doi:10.16984/saufenbilder.477181
Chicago Potas, Nihan, and Cemal Atakan. “Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application”. Sakarya University Journal of Science 23, no. 4 (August 2019): 532-40. https://doi.org/10.16984/saufenbilder.477181.
EndNote Potas N, Atakan C (August 1, 2019) Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application. Sakarya University Journal of Science 23 4 532–540.
IEEE N. Potas and C. Atakan, “Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application”, SAUJS, vol. 23, no. 4, pp. 532–540, 2019, doi: 10.16984/saufenbilder.477181.
ISNAD Potas, Nihan - Atakan, Cemal. “Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application”. Sakarya University Journal of Science 23/4 (August 2019), 532-540. https://doi.org/10.16984/saufenbilder.477181.
JAMA Potas N, Atakan C. Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application. SAUJS. 2019;23:532–540.
MLA Potas, Nihan and Cemal Atakan. “Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application”. Sakarya University Journal of Science, vol. 23, no. 4, 2019, pp. 532-40, doi:10.16984/saufenbilder.477181.
Vancouver Potas N, Atakan C. Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application. SAUJS. 2019;23(4):532-40.