Количественный анализ социологических данных

Магистратура 2021/2022

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Современный социальный анализ)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 1-й курс, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Корсунова Виолетта Игоревна

Прогр. обучения: Современный социальный анализ

Язык: английский

Кредиты: 6

Контактные часы: 34

Full Syllabus

Abstract

The course covers different types of regression modeling, including further insights into linear regression and diagnostics for linear models, binary, multinomial, ordered regression, models for count data, along with causal inference methods. Students are assumed to have basic knowledge of statistics and be familiar with several conventional statistical methods, most importantly linear regression, and with the R programming environment.

Learning Objectives

The main objective of the course is to give an introduction to a variety of extensions of linear regression analysis widely used in modern social sciences, as well as implementations of the respective methods in R, a popular, free programming language for statistical computing. By the end of the course, students will be able to choose relevant methods of analysis, implement necessary techniques, and interpret the results of modeling.

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

Test 1
Test 2
In-class assignments
An unweighted average of grades for in-class assignments.
Final exam
The exam is held online (in Skype) in the form of a test covering all topics.

Interim Assessment

2021/2022 4th module
0.2 * Test 2 + 0.2 * Final exam + 0.2 * Test 1 + 0.4 * In-class assignments

Bibliography

Recommended Core Bibliography

Agresti, A., & Finlay, B. (2014). Statistical Methods for the Social Sciences: Pearson New International Edition (Vol. Pearson new international ed., 4. ed). Harlow England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=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

KORSUNOVA VIOLETTA IGOREVNA

Course Syllabus