2023/2024

Количественный анализ социологических данных

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Новизна полученных знаний»

Статус: Маго-лего

Кто читает: Департамент социологии

Когда читается: 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Корсунова Виолетта Игоревна

Язык: английский

Кредиты: 3

Контактные часы: 34

Course Syllabus

Abstract

This course introduces participants to methods of collecting data on the behavior and preferences of individuals and groups, as well as to the fundamentals of research design in this field. It may be of use for sociologists, marketing researchers, media analysts, and professionals interested in other behavioral studies. Participants will learn the principles of conducting surveys and experiments in the context of research design development and will complete practical tasks applying these methods.

Learning Objectives

• The goal of the course is to teach participants to understand basic principles of data collection in the field of human behavior and preferences. By the end of the course, students should have an understanding of research design, sample, survey and questionnaire types, and the main approaches to their development, types of experiments and the rules of conducting them, as well as a general understanding of the logic and structure of data analysis as a research stage (without delving into specific methods).

Expected Learning Outcomes

• Able to choose statistical methods appropriate to their data and substantive research problem
• Able to design a quantitative social study
• Able to read and understand most academic social sciences articles that use quantitative approach
• Able to use R programming language for complex statistical computations

Course Contents

• Advanced analysis with linear regression
• Binary logistic regression
• Multinomial logistic regression
• Ordered logistic regression
• Models for count data
• Introduction to causal inference: Instrumental variables
• Regression discontinuity design

Assessment Elements

• Home assignments
  The assignments test understanding of the course topics. Students are presented with an analytical case and need to choose an appropriate method of analysis as well as correctly perform the analysis.
• Tests
  Students need to solve a set of analytical tasks during class.
• Final exam
  Students are presented with an analytical task, and need to choose an appropriate method, perform the analysis correctly, and verbally explain their reasoning and interpretation.

Interim Assessment

• 2023/2024 4th module
  0.2 * Final exam + 0.4 * Home assignments + 0.4 * Tests

Bibliography

Recommended Core Bibliography

• 9781292034898 - Agresti, Alan; Finlay, Barbara - Statistical Methods for the Social Sciences - 2014 - Pearson - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1418314 - nlebk - 1418314
• Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604
• Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937

Recommended Additional Bibliography

• Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808
• Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression Analysis (Vol. 2nd ed). AMsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=320724
• Guido Imbens, & Thomas Lemieux. (2007). Regression Discontinuity Designs: A Guide to Practice.
• I. Rohlfing. (2012). Case Studies and Causal Inference : An Integrative Framework. Palgrave Macmillan.
• Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=212826
• Upton, G. J. G. (2016). Categorical Data Analysis by Example. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402878

Authors

• KORSUNAVA VIYALETA IGOREVNA