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Bachelor 2019/2020

Nonreactive and Big Data in the Social Sciences: Methods and Approaches

Category 'Best Course for New Knowledge and Skills'
Type: Elective course (Sociology)
Area of studies: Sociology
Delivered by: School of Sociology
When: 4 year, 1 module
Mode of studies: Full time
Instructors: Alexander Byzov
Language: English
ECTS credits: 4

Course Syllabus

Abstract

The growth of Internet penetration and the possibility of collecting and analyzing big data have produced new challenges and have offered new opportunities for researchers and official statistics. Within several years nonreactive and big data has become the main trend in the social sciences. Nonreactive methods include nonparticipant observation and analysis of digital fingerprints such as likes or shares, as well as private documents such as blogs, social media profiles and comments, or public online documents such as mass media materials. This course will give an introduction to key quantitative approaches to the collection of nonreactive data in social sciences. The course is taught in the form of lectures, seminars, and individual work. All teaching is conducted in English. The goal of the course is to introduce the opportunities of nonreactive and big data for social scientists and learn basic methods and tools to collect nonreactive data. Within the course some R packages will be used for data analysis (it is freely available at https://www.r-project.org). Basic knowledge of quantitative sociological methods is required. Familiarity with the R statistical programming language is very helpful but not required. R program: https://cran.r-project.org/ R-studio: https://www.rstudio.com/
Learning Objectives

Learning Objectives

  • Know basic methods of collecting nonreactive data in social sciences
  • Know different types of big data in social sciences
  • Skills to collect online data (VK, Twitter etc).
  • Skills to anaylze textual data
Expected Learning Outcomes

Expected Learning Outcomes

  • Know basic concepts of reactive and nonreactive data, its opportunities, limitations, and applications in social sciences
  • Know basic concepts of Big data, its opportunities, limitations, and relevance to social sciences
  • Know basic concepts of R programming language
  • Have skills to write R code for basic data analysis tasks
  • Have skills to scrap online data through various API, automatization of actions in browser etc
  • Have skills to analyze textual data
Course Contents

Course Contents

  • Introduction to the course. Nonreactive methods in social sciences.
    Reactive and nonreactive methods. The typology of reactive and nonreactive data. The opportunities and limitations of reactive and nonreactive in social sciences.
  • Big data in social sciences
    Different approaches of applying big data in social sciences. Traps in big data. Sources of bias. Transparency. Replicability. Ethical concerns.
  • Introduction to Nonreactive Data and Big Data in R
    What is R. Packages. Files. Variables. Data storage in R (vectors, lists, data frames etc.). Regular expressions. Limitations of R. Packages in R for social media's APIs (Twitter, Facebook, Vkontakte etc.). Packages in R for data retrieval without APIs (rvest, httr etc.). Network analysis in R. Webscraping in R. Collection of Twitter data, Vkontakte data, Facebook data, Youtube data.
Assessment Elements

Assessment Elements

  • non-blocking Class Attendance
  • non-blocking Class Participation
  • non-blocking Essay
    In this individual work students should write either a review on various nonreactive and big data gathering and analysis techniques or a research with application of these techniques
  • non-blocking Group presentation
    Students in groups up to 4 people present design of research with Nonreactive and / or Big data gathering and analysis techniques.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.1 * Class Attendance + 0.15 * Class Participation + 0.5 * Essay + 0.25 * Group presentation
Bibliography

Bibliography

Recommended Core Bibliography

  • Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1175341
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data : A Revolution That Will Transform How We Live, Work, and Think. Boston: Eamon Dolan/Houghton Mifflin Harcourt. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1872664
  • R in action : Data analysis and graphics with R, Kabacoff R. I., 2011

Recommended Additional Bibliography

  • Kozinets, R. V. . (DE-588)1035573849, (DE-576)310515769. (2010). Netnography : doing ethnographic research online / Robert V. Kozinets. Los Angeles, Calif. [u.a.]: Sage. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.310515823