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Course Description Course Name : Sampling Techniques Course Code : ISB-553 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. GÜZİN YÜKSEL Learning Outcomes of the Course : Have the ability to learn the basic definitions and their usage in the fields Know the basic concepts of sampling Use the sampling techniques in research Know the theory of equal probability sampling Know the theory of unequal probability sampling Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course aims to teach the students different sampling techniques to collect unbiased and true data Course Contents : Basic concepts of sampling theory, complete count and sampling, central limit theorem, sample selection with replacement and without replecement, sampling distribution, precision, determination of sample size, simple random sampling, stratified random sampling, allocation of sample (equal allocation, proportional allocation, optimum allocation, Neyman allocation), systemetic sampling. Language of Instruction : Turkish Work Place : Department Seminar Room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Basic concepts of sampling theory Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 2 Complete count and sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 3 Central limit theorem Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 4 Weak-law of large numbers Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 5 Sample selection with replacement and without replecement Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 6 Sampling distribution, precision Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 7 Determination of sample size Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 8 Mid-term exam Review the topics discussed in the lecture notes and sources Written exam 9 Simple random sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 10 Simple random sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 11 Stratified random sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 12 Stratified random sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 13 Allocation of sample (equal allocation, proportional allocation) Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 14 Allocation of sample (optimum allocation, Neyman allocation) Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 15 Systemetic sampling Reading the references Lecture, Question & Answer, Demonstration, Drill - Practise 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Yamane T. , 1967, Elementary Sampling Theory, Prentice Hall. Deming W. E. , 1950, Some Theory of Samling, John Wiley and Sons.  Hülya Çıngı(1994) Örnekleme Kuramı.H.Ü. Fen Fakültesi Basımevi  Kish L. , (1965), Survey Sampling, John Wiley and Sons.  Cochran W. G. , (1971),Sampling Techniques, John Wiley and Sons. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 60     Homeworks/Projects/Others 5 40 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. 4 2 Conduct scientific research on Mathematics, Probability and Statistics. 4 3 Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. 4 4 Possess comprehensive information on the analysis and modeling methods used in Statistics. 2 5 Present the methods used in analysis and modeling in the field of Statistics. 3 6 Discuss the problems in the field of Statistics. 4 7 Implement innovative methods for resolving problems in the field of Statistics. 3 8 Develop analytical modeling and experimental research designs to implement solutions. 3 9 Gather data in order to complete a research. 5 10 Develop approaches for solving complex problems by taking responsibility. 5 11 Take responsibility with self-confidence. 5 12 Have the awareness of new and emerging applications in the profession 4 13 Present the results of their studies at national and international environments clearly in oral or written form. 4 14 Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. 4 15 Update his/her knowledge and skills in statistics and related fields continously 5 16 Communicate effectively in oral and written form both in Turkish and English. 5 17 Use hardware and software required for statistical applications. 3 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 5 5 25     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 20 20 Total Workload: 139 Total Workload / 25 (h): 5.56 ECTS Credit: 6