Measuring the Change by Using Markov Chain Approach in Time-Dependent Transitions and a Real Data Application
In this study, the use of the Markov chain to measure the change intime-dependent transitions is emphasized. Contingency tables were used tomeasure the time-dependent change of categorical data. Theoretically how toapply the Markov chain in the log-linear model with the help of one-step orhigher-ste...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2019-08-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/649082 |
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| Summary: | In this study, the use of the Markov chain to measure the change intime-dependent transitions is emphasized. Contingency tables were used tomeasure the time-dependent change of categorical data. Theoretically how toapply the Markov chain in the log-linear model with the help of one-step orhigher-step transition matrices was demonstrated. In addition, the stationarityapproach and the assessment of the order of the chain were given as theassumption of the model. In the real data application, 1217 undergraduatestudents, studying in Faculty of Political Science, Engineering, Sciencedepartments of Ankara University, were used. It was taken their cumulativeaverage grades for 4 years, average grades for 8 semesters, beginning in theacademic year 2013-2014.Whether the change in the success of the students ismeasurable in 8 semesters and 4 years, has been investigated. According to theresults, before making any prediction: it concluded that one-step transitionprobabilities are not stationary and the three-step transition matrix is thesecond-order Markov Chain. |
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| ISSN: | 2147-835X |