• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
For visually-impairedFor visually-impairedUser profile (HSE staff only)Search
Advanced search
Menu
HSE University
User profile (HSE staff only)Search
Advanced search

Course Syllabus

Master 2022/2023

Quantitative Data Analysis Using R

Type: Compulsory course (Comparative Social Research)
Area of studies: Sociology
Delivered by: School of Sociology
Where: Faculty of Social Sciences
When: 1 year, 1-3 module
Mode of studies: distance learning
Online hours: 20
Open to: students of all HSE University campuses
Instructors: Anna Almakaeva, Natalia Mikhailova
Master’s programme: Comparative Soсial Research
Language: English
ECTS credits: 9
Contact hours: 100

Course Syllabus

Full Syllabus

Abstract

The course aims to provide students with an understanding of the key concepts and methods of modern statistical data analysis. It gives an overview of the skills necessary for conducting social research with quantitative survey data using R software. The course covers basic statistics, t-tests, one-way ANOVA, Pearson's chi-square, correlations and regression analysis. It also puts these skills into the broader academic context by reviewing how data analysis is used in real research practice. This course includes an online course " Introduction to Statistics" provided by Stepik (https://stepik.org/course/701/promo?search=954698307). This course consists of 3 parts: 1. Introduction into quantitative research design (1st module). 2. Introduction into R and basic statistics (2nd module). 3. Quantitative data analysis using R (3rd module).
Learning Objectives

Learning Objectives

  • Provide students with an understanding of the key principles of quantitative research and methods of modern statistical data analysis
Expected Learning Outcomes

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 academic standards of reporting results and professional scientific ethics
  • know the key concepts of statistics
  • know the main methods and techniques of statistical data analysis
  • To be able to analyze secondary data at the level required for an MA thesis
  • to be able to interpret and present the results of data analysis in oral and written form
  • be able to choose correct statistical methods and procedures according to the research questions and the level of measurement, read and transform data in R Studio, calculate basic statistics in R Studio, interpret and present results of in oral and written form
  • have skills in using R Studio for reading and transforming data, calculation of basic statistics and interpretation of results
Course Contents

Course Contents

  • Introduction into R
  • Introduction into sociological inquiry
  • Measurement
  • Data transformations
  • Descriptive statistics
  • Visualization
  • 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

Assessment Elements

  • non-blocking Home assignment
  • non-blocking Test
  • non-blocking Essay
  • non-blocking Reading published research
  • non-blocking Exam
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.11 * Reading published research + 0.2 * Home assignment + 0.29 * Essay + 0.11 * Test + 0.29 * Exam
  • 2022/2023 3rd module
    0.11 * Test + 0.2 * Home assignment + 0.29 * Essay + 0.11 * Reading published research + 0.29 * Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Adventures in social research : data analysis using IBM SPSS Statistics, Babbie, E., 2013
  • An introduction to R : a programming environment for data analysis and graphics, Venables, W. N., 2009
  • Applied missing data analysis, Enders, C. K., 2010
  • Applied multivariate data analysis, Everitt, B. S., 2001
  • Basic statistic for social research, Hanneman, R. A., 2013
  • Categorical data analysis, Agresti, A., 2002
  • 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
  • ggplot2 : elegant graphics for data analysis, Wickham, H., 2009
  • Introduction to social research, Babbie, E., 2011
  • R in action : Data analysis and graphics with R, Kabacoff, R. I., 2011
  • Statistics : a tool for social research, Healey, J. F., 2012
  • The basics of social research, Babbie, E., 2014
  • The essentials of statistics : a tool for social research, Healey, J. F., 2007
  • The practice of social research, Babbie, E., 2013

Recommended Additional Bibliography

  • Hatekar, N. (2010). Principles of Econometrics : An Introduction (using R). New Delhi, India: SAGE Publications India Pvt., Ltd. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=354910
  • Layder, D. (1998). Sociological Practice : Linking Theory and Social Research. London: SAGE Publications Ltd. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=775757
  • Mirkin, B. Core concepts in data analysis: summarization, correlation and visualization. – Springer Science & Business Media, 2011. – 388 pp.
  • Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.