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Course Description Course Name : Applied Educational Statistics II Course Code : PDR 704 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall and Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. FULYA CENKSEVEN ÖNDER Learning Outcomes of the Course : Learns to prepare the data for multivariate statistical analysis Understands the assumptions of multivariate statistical techniques Determines the statistical technique suitable for the research questions. Analyzes the multivariate statistical techniques by SPSS Tabulates and interprets the findings of the analysis in a suitable way Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The aim of this course is to provide the students with the ability to determine the multivariate statistical technique suitable for the problems and prepare the data for multivariate statistical analysis, insert the data to the database in a suitable way, analyze the multivariate statistical techniques by SPSS, tabulate the findings of the analysis in a suitable way and interpret the findings. Course Contents : This course covers assumptions of multivariate statistical techniques, multivariate ANOVA (MANOVA), simple and multiple linear regression, logistic regression, two-way ANOVA for mixed measures, non-parametric techniques, factor analysis. Language of Instruction : Turkish Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction: Course content, importance, instructional methods, techniques and assessment Review of course content and references Lecture, discussion 2 Multivariate ANOVA (MANOVA) Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 3 Multivariate ANOVA (MANOVA) Scanning the topic to be taught and readiing the suggested books and articles Expository teaching strategies, discussion 4 Multivariate ANOVA (MANOVA) Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 5 Lineaar regression Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 6 Lineaar regression Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 7 Lineaar regression Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 8 Logistic regression Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 9 Two-way ANOVA for mixed measures Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 10 Factor analysis Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 11 Factor analysis Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 12 Factor analysis Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 13 Non-parametric analysis Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 14 Non-parametric analysis Scanning the topic to be taught and reading the suggested books and articles Expository teaching strategies, discussion 15 Overall evaluation of course Scanning all topics covered over the term lecture, discussion 16/17 Final Exam preparation for the exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Büyüköztürk, Ş., Köklü, N. ve Çokluk-Bökeoğlu, Ö. (2010) Sosyal Bİlimler için İstatistik. Pegem A Yayıncılık. Ankara.  Esin, Ekni & Gamgam. (2009) İstatistik. Özdamar, K. (2009). Paket Programlar ile İstatistiksel Veri Analizi. Kaan Kitabevi, Eskişehir.  Esin, A., Ekni, M., Gamgam, H. (2006). İstatistik. Gazi Kitabevi, Ankara.  Baştürk, R. (2011). Nonparametrik İstatistiksel Yöntemler. Anı Yayıncılık, 2. baskı, Ankara  Tabachnick, B., & Fidell, L. (2007) Using multivariate statistics, Boston: Allyn & Bacon. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 0 30     Homeworks/Projects/Others 10 70 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 On the basis of the Educational Sciences BA degree qualificatons, improves his/her knowledge in the same field at the level of expertise. 3 2 Understands the multi-dimensional causes of a problem and evaluates the problem as a whole 2 3 Analyzes a scientific article in the field of educational sciences. 5 4 Uses the theoretical and practical knowledge s/he gained at the level of expertise in the field of educational sciences. 3 5 Uses and develops individual assessment techniques. 3 6 Guides the graduates of the field of educational sciencies within the framework of the knowledge and experiences that specialized training provides. 1 7 Gains knowledge and experience about the appliations which can make the field of expertise functional in educational field. 0 8 Makes prediction about the partner behaviors through the applications concerning the field of expertise. 3 9 Solves a problem in the context of educational sciences in a scientific perspective. 5 10 Integrates the theoretical knowledge within the scope of the field of educational sciences with the field of application. 2 11 Takes responsibility for solving the problems at the local and national level during the educational science-oriented applications. 0 12 Communicates effectively and properly with students, teachers, school administrators, and members of families and working group. 0 13 Has a good command of foreign language to be able to follow the foreign resources related to the field. 3 14 Informs his/her colleagues about the processes regarding the field of expertise and conclusions reached. 3 15 Uses the different statistical techniques and information and communication technologies that s/he needs in the field of expertise. 5 16 Provides the support of the institution employees to make the applications concerning educational sciences successful. 0 17 Develops internalized knowledge in the field of expertise and the related disciplines at the level of expertise. 4 18 Identifies the sources of the problems in the field of educational sciences and contributes to the solution of these problems. 2 19 Provides the cooperation of all partners in educational environment stating the necessity of the field of expertise. 1 20 Assesses the necessities which reveal through the applications related to the field of expertise in a critical way. 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 4 56 Assesment Related Works     Homeworks, Projects, Others 10 4 40     Mid-term Exams (Written, Oral, etc.) 0 0 0     Final Exam 1 5 5 Total Workload: 143 Total Workload / 25 (h): 5.72 ECTS Credit: 6