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Course Description Course Name : Scientific Research Methods Course Code : IG 208 Course Type : Compulsory Level of Course : First Cycle Year of Study : 2 Course Semester : Spring (16 Weeks) ECTS : 4 Name of Lecturer(s) : Assoc.Prof.Dr. GÜZİN YÜKSEL Learning Outcomes of the Course : Gains the ability to do data analysis. is able to find solutions to the problems of operational work. Learns how to use SPSS. Gains the ability to analyze data through SPSS. Develops the skills in problem analysis and problem solving Develops the skills in data processing and manipulation. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To enable students to gain skills in making comments and analysis of the problems through theoretical and practical knowledge about data analysis and basic statistical methods used in the Food Science and Technology and Related Fields. Course Contents : Introduction to basic computer skills, Preparation of data, Descriptive Statistics, Correlation, Statistical Tests, ANOVA Analysis, Regression Analysis, Coding survey data, Reliability Analysis Language of Instruction : English Work Place : Classes of Food Engineering Department and Laboratory   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to basic computer skills Reading source and application Presentations, applications, comments and class discussions 2 Introduction to SPSS Program Reading source and application Presentations, applications, comments and class discussions 3 Preparation of data Reading source and application Presentations, applications, comments and class discussions 4 Data screening and transformation Reading source and application Presentations, applications, comments and class discussions 5 Descriptive Statistics Reading source and application Presentations, applications, comments and class discussions 6 Correlation Reading source and application Presentations, applications, comments and class discussions 7 Tests for means Reading source and application Presentations, applications, comments and class discussions 8 Midterm Review the topics discussed in the lecture notes and sources Written Exam 9 Statistical Tests Reading source and application Presentations, applications, comments and class discussions 10 ANOVA Analysis Reading source and application Presentations, applications, comments and class discussions 11 ANOVA Analysis Reading source and application Presentations, applications, comments and class discussions 12 Regression Analysis Reading source and applicationReading source and application Presentations, applications, comments and class discussions 13 Regression Analysis Reading source and application Presentations, applications, comments and class discussions 14 Coding survey data Reading source and application Presentations, applications, comments and class discussions 15 Reliability Analysis Reading source and application Presentations, applications, comments and class discussions 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)  Statistics, James T. Mc Clave and H. Dietrich, 1994. 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 2 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 Gains the ability to use knowledge and skills in his/her field. 4 2 Improve a process-based system using the methods of measurement and evaluation 3 3 Has knowledge in the fields of basic science, engineering and food science and technology 2 4 Determines, identifies and resolves the problems in the areas regarding food engineering and technology applications 4 5 Researches and analyzes complex systems using scientific methods 4 6 Uses objective and subjective methods to evaluate food quality and interprets the results 3 7 Selects and uses modern technical systems in food engineering and technology applications 4 8 Uses laboratories, does food analyses and evaluates, interprets and reports the results, 5 9 Has skills of Independent decision-making, self-confidence, creativity and the ability to take responsibility 3 10 Complies with teamwork 2 11 Analytically and critically evaluates the learned information. 3 12 Knows the necessity of lifelong learning. 3 13 Communicates effectively and healthily in the relevant field and uses communication technologies 3 14 Knows a foreign language at a level to follow the literature about foods and communicate 4 15 is respectful of professional ethics 4 16 Has ability to plan, implement and develop a food process 3 17 Knows the legislation and management systems related to foods 1 18 Constantly improves himself/herself determining his/her training needs in accordance with his/her interests and abilities in the scientific, cultural, artistic and social fields besides his/her professional development 0 * 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 2 28     Out of Class Study (Preliminary Work, Practice) 14 1 14 Assesment Related Works     Homeworks, Projects, Others 2 8 16     Mid-term Exams (Written, Oral, etc.) 1 15 15     Final Exam 1 20 20 Total Workload: 93 Total Workload / 25 (h): 3.72 ECTS Credit: 4