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Бакалавриат 2020/2021

Введение в сетевой анализ

Статус: Курс по выбору (Социология)
Направление: 39.03.01. Социология
Когда читается: 4-й курс, 3 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для всех кампусов НИУ ВШЭ
Язык: английский
Кредиты: 6

Course Syllabus

Abstract

This course is an introductory course in network analysis, designed to familiarize undergraduate students with the general concepts and basic techniques of network analysis in sociological research, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software.
Learning Objectives

Learning Objectives

  • 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 and/or foster critical reviewing skills of published empirical research using network analytic methods.
Expected Learning Outcomes

Expected Learning Outcomes

  • 1. Ability to work with complex data and use methods of network analysis and statistics appropriately.
  • Ability to translate conceptual thinking into publishable quality papers
  • Ability to advance own knowledge in the area of network research methods.
  • Ability to reflect on learned network research methods and tools
  • Ability to conduct written and oral communication in English to convey research ideas
  • Ability to conduct written and oral communication in English language to convey professional and scientific ideas
  • Ability to present and defend a scientific argument in front of a wide audience
Course Contents

Course Contents

  • Social network analysis: Methods or theory? Structural approach. Interdisciplinary interest in network analysis. Network theories most popular in sociology. Key network concepts: network, structure, nodes, ties, sociogram, structural and compositional variables, etc. Types of network data. Sampling and data collection in network analysis.
  • Survey instruments for collecting network data. Network data collection and ethical issues. Basic measures of network characteristics. Graphic representation of network relations.
  • Network measures for dyads and triads. The forbidden triad. Clustering. Identifying tightly connected groups and subgroups in social networks. Small-world phenomenon. Homophily principal in personal relationships. Cultural and historical differences in network connectivity. Personal ties and social support.
  • Centrality and Influence. Measures of Centrality. Two-mode networks: transformation, graphical representation, and analysis. Centrality and two-mode networks in the studies of power and influence.
  • The strength of weak ties. Social capital at the individuals and community level. Social capital in companies’ economic activities. Social capital in the labor market and its role in social mobility. Structural holes in competition.
  • Social networks and education. Representation of mental models as social networks. Diffusion of innovation through social networks. Social networks and technology. Deviant behavior, crime and social networks. Social stratification, social change, and social networks
  • Network models
Assessment Elements

Assessment Elements

  • non-blocking Final project
    The examination is carried out in writing: passing the final project through the LMS to the teacher.
  • non-blocking Homework Assignments
    In this class, homeworks are essential for learning. Simply put, you CANNOT learn statistics by simply attending the class. Homeworks will be more along the lines of the real-life problems that you will have to solve in the future, and you will have a week after the topic was introduced in class to work on these. Homework assignments are handed out in class (during seminars) and will be available electronically. I strongly recommend that you do not wait until the due date to complete those, and work on the problems a few at a time throughout the assigned period.
  • non-blocking Quizzes
    You cannot meaningfully participate in the seminar if you have missed my lecture and did not do any reading. Therefore, to encourage you to prepare for seminars, every seminar will have a quiz on the lecture material and all assigned readings for the week. This includes the very first seminar, which will focus on Lecture 1 material. You are allowed to miss any one quiz (skip a seminar, not prepare, etc.) – in other words, I will count the best 11 out of 12 quizzes that we will have. If you submit all twelve, I will count best nine. All quizzes will be done online and submitted to me via SurveyMonkey (links will be given in class).
  • non-blocking In-class Labs
    There will be a lab assignment in almost every seminar, depending on our progress. Since we will be learning R, and learning quickly, you will need to devote a substantial time to it. Seminar labs should help you with this task. At the end of the lab, you will submit your completed assignment for the day (or as much as you were able to complete) to me via LMS.
  • non-blocking Final project
    The examination is carried out in writing: passing the final project through the LMS to the teacher.
  • non-blocking Homework Assignments
    In this class, homeworks are essential for learning. Simply put, you CANNOT learn statistics by simply attending the class. Homeworks will be more along the lines of the real-life problems that you will have to solve in the future, and you will have a week after the topic was introduced in class to work on these. Homework assignments are handed out in class (during seminars) and will be available electronically. I strongly recommend that you do not wait until the due date to complete those, and work on the problems a few at a time throughout the assigned period.
  • non-blocking Quizzes
    You cannot meaningfully participate in the seminar if you have missed my lecture and did not do any reading. Therefore, to encourage you to prepare for seminars, every seminar will have a quiz on the lecture material and all assigned readings for the week. This includes the very first seminar, which will focus on Lecture 1 material. You are allowed to miss any one quiz (skip a seminar, not prepare, etc.) – in other words, I will count the best 11 out of 12 quizzes that we will have. If you submit all twelve, I will count best nine. All quizzes will be done online and submitted to me via SurveyMonkey (links will be given in class).
  • non-blocking In-class Labs
    There will be a lab assignment in almost every seminar, depending on our progress. Since we will be learning R, and learning quickly, you will need to devote a substantial time to it. Seminar labs should help you with this task. At the end of the lab, you will submit your completed assignment for the day (or as much as you were able to complete) to me via LMS.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.5 * Final project + 0.2 * Homework Assignments + 0.2 * In-class Labs + 0.1 * Quizzes
Bibliography

Bibliography

Recommended Core Bibliography

  • Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=132264
  • Lazega, E., & Snijders, T. A. B. (2016). Multilevel Network Analysis for the Social Sciences : Theory, Methods and Applications. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1119294
  • Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
  • Nooy, W. de, Mrvar, A., & Batagelj, V. (2005). Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138973

Recommended Additional Bibliography

  • Jeremy Boissevain, & J. Clyde Mitchell. (2018). Network Analysis : Studies in Human Interaction (Vol. Reprint 2018). Berlin/Boston: De Gruyter Mouton. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1926819