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Бакалаврская программа «Социология и социальная информатика»

Special Topics of Social Informatics

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 1 модуль

Course Syllabus

Abstract

We will build our work around the understanding of how statistics and computation work in social, applied and CS research, focusing on acquiring deeper understanding of statistical inference based on observational and experimental data and focusing on “big data” research using digital methods. If you are planning to do a quantitative study for your thesis, this course will help you to think about suitable research design early on, hopefully leading to better results and less issues. If you think on continuing your career or education in quantitative directions – from high-profile MSc programmes in quantitative social science, data, business analytics or UX analytics, this course might help you to build necessary conceptual and computational foundation in these areas. We will use simulations, pictures and intuition more than math and use online course materials and extra reading as a backbone for our discussion and simulations, so this course should be accessible for those without Data Science minor or alternative experience. However, the course will require large amount of independent work.
Learning Objectives

Learning Objectives

  • equipping students with tools to transform ideas and apply theories to practice, using modern (“digital”) research methods and tools of social informatics
Expected Learning Outcomes

Expected Learning Outcomes

  • Applies modern social research methods and models to study behavior, decision making and social phenomena using tools of social informatics
  • Formulates goals and research questions to observational and experimental studies using tools of social informatics
  • Creates research proposals describing possible application of modern research methods and tools of social informatics to the areas of choice, presents and discusses it
Course Contents

Course Contents

  • Introduction to the course. Digital research design. Statistics, Experiment, Simulation
    Big data and social research at the digital age. Modern approaches to social research. Research Designs. Social Informatics and its research and applied problematics. Statistical inference as a foundation of scientific decisions. Biases. Errors. Paradoxes of statistical decisions. Inferential and predictive models. Inference based on observation and experimental data. Causality. Simulation.
  • Observing Behavior using Digital Data
    Types and sources of digital data. Social media and applications as data sources. Validity issues. Survey data. Limitations of behavioural data.
  • Research Questions and Methods. Experiments
    Goal – RQ mapping for digital research. Match between questions and methods. Observational vs Experimental data. Experimental and Quasi-experimental settings. Crowdsourcing.
  • Human Computation & Citizen Science
    Design for science. Crowdsourcing for human computation. Citizen science and distributed work.
  • Modern statistical inference
    Approaches to statistical inference and decision making. Multiple comparison problem and researcher degrees of freedom. Replication. Pre-registration and open science. This topic is studied in online format using MOOC “Improving your statistical inferences” https://www.coursera.org/learn/statistical-inferences (weeks 1-4 of the MOOC).
  • Digital Research Designs
    Combining digital and traditional data: a holistic approach to understand human behaviour. Social informatics tools in DRD. Course conclusion
Assessment Elements

Assessment Elements

  • non-blocking Test
  • non-blocking Research Design Proposal
    If the task or part of the task with separate deadline was submitted up to an hour after the deadline, the score for it is reduced by 10%, up to 6 hours - by 30%, up to 24 hours – by 60%, after that the task or its part is not accepted resulting in 0 grade.
  • non-blocking Peer Review of Research Design Presentation
  • non-blocking Online Course Tests
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.25 * Online Course Tests + 0.25 * Peer Review of Research Design Presentation + 0.25 * Research Design Proposal + 0.25 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Manzo, G. (2014). Analytical Sociology : Actions and Networks. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=714658

Recommended Additional Bibliography

  • Chen, S.-H. (2018). Big Data in Computational Social Science and Humanities. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1944799
  • Eric Silverman. (2018). Methodological Investigations in Agent-Based Modelling: With Applications for the Social Sciences. Web server without geographic relation, Web server without geographic relation (org): Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.36B944A9