Методология и методы исследований в социологии: количественные методы исследований

Магистратура 2021/2022

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Статус: Курс обязательный (Сравнительные социальные исследования / Comparative Social Research)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Факультет социальных наук

Когда читается: 1-й курс, 2 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Алмакаева Анна Михайловна

Прогр. обучения: Сравнительные социальные исследования

Язык: английский

Кредиты: 6

Контактные часы: 52

Full Syllabus

Abstract

The course aims to provide students with understanding of key concepts and methods of modern statistical data analysis. It gives an overview and practice of the skills necessary for conducting independent research with quantitative survey data, using R software. The course also puts these skills into the broader academic context by reviewing how statistics are used in published scientific journal articles.

Learning Objectives

Provide students with understanding of key concepts and methods of modern statistical data analysis

Expected Learning Outcomes

Have skills in interpreting results and writing research papers
Have skills in the evaluation of the quality of published research papers
Have skills in using R Studio for statistical data analysis
know the key concepts of statistics
know the main methods and techniques of statistical data analysis
know the main procedures of data transformation and data analysis using R
to be able to analyze secondary data at the level required for an MA thesis
to be able to choose correct statistical methods and procedures according to the research questions
to be able to interpret and present the results of data analysis in oral and written form

Course Contents

Statistical inference in social sciences
T-tests, One-way ANOVA
Non-parametric analogues of T-tests and one-way ANOVA
Pearson's chi-square for contingency tables
Correlations
Linear regression
Regression diagnostics: heteroscedasticity, multicollinearity, non-linearity
Categorical independent variables in linear regression and interaction effects
Binary logistic regression

Assessment Elements

Reading published research
Essay
Home assignment
Grading criteria: 1) The correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
Exam
The task of the first reexam is similar to the first exam. Students will have 3 hours to do it. The second reexam is similar to the first one. The weight of the reexam in the final grade is 0.3.

Interim Assessment

2021/2022 2nd module
0.35 * Essay + 0.2 * Home assignment + 0.35 * Exam + 0.1 * Reading published research

Bibliography

Recommended Core Bibliography

Handbook of univariate and multivariate data analysis and interpretation whith SPSS, Ho, R., 2006

Recommended Additional Bibliography

Cramer D. Advanced Quantitative Data Analysis. 2003.
Gelman, A. B., & Hill, J. (2015). Data analysis using regression and multilevel/hierarchical models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E4FBAE7
Mirkin, B. Core concepts in data analysis: summarization, correlation and visualization. – Springer Science & Business Media, 2011. – 388 pp.
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
Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.
Willem Mertens, Amedeo Pugliese, & Jan Recker. (2017). Quantitative Data Analysis. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.3.319.42700.3

Course Syllabus