Nonreactive and Big Data in the Social Sciences: Methods and Approaches
- 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
- 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
- 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 sciencesDifferent 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 RWhat 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.
- Class Attendance
- Class Participation
- EssayIn 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
- Group presentationStudents in groups up to 4 people present design of research with Nonreactive and / or Big data gathering and analysis techniques.
- Interim assessment (1 module)0.1 * Class Attendance + 0.15 * Class Participation + 0.5 * Essay + 0.25 * Group presentation
- 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
- 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