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Regular version of the site

Social Network Analysis with R

2021/2022
Academic Year
ENG
Instruction in English
4
ECTS credits
Course type:
Elective course
When:
1 year, 3 module

Instructor


Kuskova, Valentina

Course Syllabus

Abstract

This course contains three independent, but interconnected components: 1. Theoretical: network theory and theory of networks, and their role in homological network of focal constructs of interest; 2. Methodological: methods of analysis and software programs used to analyze network data; 3. Applied: the theory and instruments learned in class are then used in individual and group work to design a research project in student’s own area of interest.
Learning Objectives

Learning Objectives

  • To develop and/or foster critical reviewing skills of published empirical research using network analytic methods.
  • To provide students with an understanding of the basic principles of network analysis and lay the foundation for future learning in the area.
  • To explore the advantages and disadvantages of various network analytic tools and methods, and demonstrate how they relate to other methods of analysis.
  • To develop student familiarity, through hands-on experience, with the major network modeling programs, so that they can use them and interpret their output.
  • To develop student familiarity, through hands-on experience, with the major network modeling programs, so that they can use them and interpret their output.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to analyze, verify, evaluate the completeness of information, can integrate information found from various sources and compensate for lack of data by adjusting models.
  • Be able to create technological requirements and regulatory documents related to professional activities, describe them to others and control their implementation.
  • Be able to effectively generate new ideas and products in their professional activities.
  • Be able to effectively solve problems in professional and social activities.
  • Be able to evaluate and reprocess methods and techniques of network analysis for a given problem.
  • Be able to solve different tasks of professional activity, effectively communicate with experts from other areas.
  • Have a working skill on use of new methods and techniques of network analysis, additional packages and tools, without direct supervision.
  • Know the interdisciplinary texts with the usage of language and apparatus of applied mathematics.
Course Contents

Course Contents

  • Network Approach in Social Science Research
  • Network Data, Matrices, Graphic Representation of Social Networks, and Basic Network Measures
  • Personal Ties and Social Support
  • Position Analysis in Social Networks
  • Ties that Benefit
  • Social Networks in Society
  • Network models in R
Assessment Elements

Assessment Elements

  • non-blocking Homework (5 total, varied points)
  • non-blocking In-class Labs (10 total, varied points)
  • non-blocking Quizzes (Best 9 out of 10, varied points)
  • non-blocking Course Project
Interim Assessment

Interim Assessment

  • 2021/2022 3rd module
    0.2 * Homework (5 total, varied points) + 0.1 * Quizzes (Best 9 out of 10, varied points) + 0.2 * In-class Labs (10 total, varied points) + 0.5 * Course Project
Bibliography

Bibliography

Recommended Core Bibliography

  • Hans-Peter Kohler, Jere Behrman, & Susan Watkins. (2001). The density of social networks and fertility decisions: evidence from south nyanza district, kenya. Demography, (1), 43. https://doi.org/10.1353/dem.2001.0005
  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). BIRDS OF A FEATHER: Homophily in Social Networks. Annual Review of Sociology, 27, 415. https://doi.org/10.1146/annurev.soc.27.1.415
  • Nan Lin. (1999). Social Networks and Status Attainment. Annual Review of Sociology, 25, 467. https://doi.org/10.1146/annurev.soc.25.1.467
  • R. Pitts. (1979). The Medieval River Trade Network of Russia Revisited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.13ECA4E0
  • Uzzi, B., & Spiro, J. (2005). Collaboration and Creativity: The Small World Problem. American Journal of Sociology, 111(2), 447–504. https://doi.org/10.1086/432782

Recommended Additional Bibliography

  • Bat Batjargal. (2007). Network triads: transitivity, referral and venture capital decisions in China and Russia. Journal of International Business Studies, (6), 998. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.pal.jintbs.v38y2007i6p998.1012
  • Gerlach, M. L. (1992). The Japanese Corporate Network: A Blockmodel Analysis. Administrative Science Quarterly, 37(1), 105–139. https://doi.org/10.2307/2393535
  • Gibson, J. L. (2001). Social Networks, Civil Society, and the Prospects for Consolidating Russia’s Democratic Transition. American Journal of Political Science (Wiley-Blackwell), 45(1), 51. https://doi.org/10.2307/2669359
  • Guseva, A., & Rona-Tas, A. (2001). Uncertainty, Risk, and Trust: Russian and American Credit Card Markets Compared. American Sociological Review, 66(5), 623–646. https://doi.org/10.2307/3088951
  • Marsden, P. V. (1987). Core Discussion Networks of Americans. American Sociological Review, 52(1), 122–131. https://doi.org/10.2307/2095397
  • McPherson, M., Smith-Lovin, L., & Brashears, M. E. (2006). Social Isolation in America: Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71(3), 353–375. https://doi.org/10.1177/000312240607100301
  • Mizruchi, M. S. (1996). WHAT DO INTERLOCKS DO? An Analysis, Critique, and Assessment of Research on Interlocking Directories. Annual Review of Sociology, 22, 271. https://doi.org/10.1146/annurev.soc.22.1.271