Information Package / Course Catalogue
| Economics | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
ECTS Course Information Package
| School/Faculty/Institute | Faculty of Economics, Administrative and Social Sciences | ||||
| Course Code | ECON 207 | ||||
| Course Title in English | Quantitative Methods for Economists I | ||||
| Course Title in Turkish | Ekonomistler için Nicel Yöntemler I | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Introductory | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 137 hours per semester | ||||
| Number of Credits | 5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Co-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Registration Restrictions | Only undergraduate students | ||||
| Overall Educational Objective | To learn the foundational skills in statistical analysis and quantitative methods for economists | ||||
| Course Description | This course covers fundamental concepts of survey research, predictive models, and causal inference to analyze real-world data with R statistical software. No prior knowledge of statistics or coding required. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Recognize and interpret quantitative information 2) Explain the basic concepts of quantitative reasoning, such as variables, constants and estimates 3) Understand how inferences are drawn from quantitative analysis 4) Determine and use appropriate quantitative methods to solve problems 5) Accurately interpret the results of data analyses |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. | |||||
| 2) Demonstrates knowledge and skills in understanding the interactions of different areas of economics. | |||||
| 3) Displays a sound comprehension of microeconomic and macroeconomic theory. | |||||
| 4) Applies economic concepts to solve complex problems and enhance decision-making capability. | |||||
| 5) Uses quantitative techniques to analyze different economic systems. | |||||
| 6) Applies theoretical knowledge to analyze issues regarding Turkish and global economies. | |||||
| 7) Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. | |||||
| 8) Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings. | |||||
| 9) Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. | |||||
| 10) Exhibits individual and professional ethical behavior and social responsibility. | |||||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. | H | HW,Exam |
| 2) | Demonstrates knowledge and skills in understanding the interactions of different areas of economics. | N | |
| 3) | Displays a sound comprehension of microeconomic and macroeconomic theory. | N | |
| 4) | Applies economic concepts to solve complex problems and enhance decision-making capability. | N | |
| 5) | Uses quantitative techniques to analyze different economic systems. | N | |
| 6) | Applies theoretical knowledge to analyze issues regarding Turkish and global economies. | N | |
| 7) | Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. | H | HW,Exam |
| 8) | Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings. | S | HW,Exam |
| 9) | Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. | S | Participation |
| 10) | Exhibits individual and professional ethical behavior and social responsibility. | N | |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | H | Participation,HW |
| Prepared by and Date | FIRAT BİLGEL , August 2025 |
| Course Coordinator | NAROD ERKOL |
| Semester | Fall |
| Name of Instructor |
Course Contents
| Week | Subject |
| 1) | Introduction to R and RStudio |
| 2) | Introduction to R and RStudio |
| 3) | Estimating causal effects with randomized experiments |
| 4) | Estimating causal effects with randomized experiments |
| 5) | Inferring Population Characteristics via Survey Research |
| 6) | Inferring Population Characteristics via Survey Research |
| 7) | Predicting outcomes using linear regression |
| 8) | Predicting outcomes using linear regression |
| 9) | Midterm |
| 10) | Estimating causal effects with observational data |
| 11) | Estimating causal effects with observational data |
| 12) | Probability |
| 13) | Quantifying uncertainty |
| 14) | Quantifying uncertainty |
| 15) | Final examination period |
| 16) | Final examination period |
| Required/Recommended Readings | Data Analysis for Social Science, by Elena Llaudet and Kosuke Imai, Princeton University Press, 2022. | ||||||||||||||||||
| Teaching Methods | Flipping Learning | ||||||||||||||||||
| Homework and Projects | Yes | ||||||||||||||||||
| Laboratory Work | None | ||||||||||||||||||
| Computer Use | Yes | ||||||||||||||||||
| Other Activities | None | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
erkoln@mef.edu.tr Attendance/participation: Students are expected to prepare for the lecture via assigned videos and reading materials. Students are responsible to follow the announcements, course materials available on Blackboard system. Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor is only responsible for the information sent/received through Blackboard system and emails using @mef account. The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs. Grading and evaluation: Evaluation will be based on the student learning outcomes. It is strongly recommended to complete all the work in a timely fashion. Late submissions will not be accepted. Missing midterm exam: No make up unless a legitimate proof of absence is presented. Missing quizzes: No make up |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 1 | 70 | ||
| Homework Assignments | 9 | 0 | 6 | 54 | |||
| Midterm(s) | 1 | 5 | 1 | 6 | |||
| Final Examination | 1 | 6 | 1 | 7 | |||
| Total Workload | 137 | ||||||
| Total Workload/25 | 5.5 | ||||||
| ECTS | 5 | ||||||