GRADUATE SCHOOL
Master of Business Administration (MBA) (With Thesis)
BA 522 | Course Introduction and Application Information
| Course Name |
Social Network Analysis
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
BA 522
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | DiscussionQ&ALecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | The aim of this course is to provide theoretical and applied skills for the use of social network analysis methods in scientific research. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The social network approach examines the behavioral dynamics that cause the formation of the social structure on the one hand, and the effect of the structure on behavior on the other. This course examines the social science theories that form the basis of this unique approach and teaches how to design a research based on these theories. In this context, graph methods and metrics required for relational social analysis are taught in an applied manner |
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|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction: Social networks and relational social research. | Gençer (2020), chapter 1 |
| 2 | Social network research process and method. Application skills: Introduction to R or Python | Cross ve Parker (2004), chapter 1 |
| 3 | Examination of the social structure: network visualization, center, periphery, and identification of structural similarity. Application skills: Reading and visualizing social networks with R or Python | Gençer (2020), chapter 11 |
| 4 | Identification of general structural features using basic graph theory. Comparison of different social systems with graph metrics. Application skills: Network measurements with R or Python | Gençer (2020), chapter 2 |
| 5 | Local structural features of actors: centrality. Application skills: Social network case studies with R or Python | Gençer (2020), chapter 3 |
| 6 | The location of actors within the general structure: global centrality measurements. Application skills: Using centrality measurements with actor features | Gençer (2020), chapter 4 |
| 7 | Research design and sample selection in social network analysis | Frank (2005) |
| 8 | Collecting and managing social network research data. Application skills: Collecting social media or similar data with Python or R | Gençer (2020), chapter 10, Gençer (2020b) |
| 9 | Networks surrounding individuals: ego networks, and groups. Structural holes and the effect of weak ties in business networks. | Gençer (2020), chapter 6 |
| 10 | Network dynamics: reciprocity, structural balance, assortativity, and homophily. Application skills: Creating random networks with R or Python | Gençer (2020), chapter 5 |
| 11 | Modeling of structural change: statistical network models and simulations. Application skills: Fitting and interpreting statistical network models with R | Gençer (2020), chapter 8 |
| 12 | Modeling of the general structure: block-models Application skills: Block model application with R | Gençer (2020), chapter 9 |
| 13 | Social Media Analysis | Gençer (2020), chapter 12 |
| 14 | Project presentations | |
| 15 | Semester Review | |
| 16 | Final Exam |
| Course Notes/Textbooks | Gençer, M. (2020). Applied Social Network Analysis With R: Emerging Research and Opportunities (pp. 1-120). Hershey, PA: IGI Global. Doi:10.4018/978-1-7998-1912-7 ISBN13: 9781799819127 |
| Suggested Readings/Materials | Cross, R. L., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business Press. Frank, O. (2005). Network sampling and model fitting. İn “Models and methods in social network analysis” , s31-56. Carrington, P. J., Scott, J., & Wasserman, S. (Eds.). Cambridge university press. Gençer, M, 2020b (forthcoming), Socian network research methods in organizations, Yönetim Araştırmaları Dergis |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
10
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
30
|
| Presentation / Jury |
1
|
20
|
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
2
|
40
|
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
4
|
56
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
3
|
20
|
60
|
| Presentation / Jury |
1
|
40
|
40
|
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
2
|
10
|
20
|
| Final Exam |
0
|
||
| Total |
224
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to demonstrate general business knowledge and skills. |
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| 2 | To able to master the state-of-the-art literature in the area of specialization. |
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| 3 | To be able to evaluate the performance of business organizations through a holistic approach. |
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| 4 | To be able to effectively communicate scientific ideas and research results to diverse audiences. |
X | ||||
| 5 | To be able to deliver creative and innovative solutions to business-related problems. |
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| 6 | To be able to solve business related problems using analytical and technological tools and techniques. |
X | ||||
| 7 | To be able to take a critical perspective in evaluating business knowledge. |
X | ||||
| 8 |
To be able to exhibit an ethical and socially responsible behavior in conducting research and making business decisions. |
X | ||||
| 9 | To be able to carry out a well-designed independent and empirical research. |
X | ||||
| 10 | To be able to use a foreign language to follow information about the field of finance and participate in discussions in academic environments. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest