Master 2020/2021

Basic Statistics and Introduction into "R"

Category 'Best Course for Career Development'

Type: Compulsory course (Comparative Social Research)

Area of studies: Sociology

Delivered by: School of Sociology

Where: Faculty of Social Sciences

When: 1 year, 1 module

Mode of studies: distance learning

Instructors: Anna Almakaeva

Master’s programme: Comparative Soсial Research

Language: English

ECTS credits: 4

Contact hours: 28

Course Syllabus

Abstract

The course aims to provide students with an understanding of key concepts and methods of modern statistical data analysis. It gives an overview 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 an understanding of the basic concepts of statistical analysis
• Provide students with an understanding of the basic principles of R programming

Expected Learning Outcomes

• know the key concepts of basic statistics, main principles and procedures R programming, main procedures of data transformation and statistical analysis in R Studio.
• 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

• Introduction into sociological inquiry
  Two main research designs: qualitative and quantitative and their peculiar features. Stages of quantitative research. Possible units of analysis in quantitative research.
• Introduction into R
  Installing R and R Studio. Exploring R Studio space and windows. Reading and installing working directory. Installing packages. Getting help. Using R as a calculator. Types of objects and assigning objects. Creating vectors, matrixes, lists, data frames. Using package “haven” for importing data frames. Indexing objects.
• Measurement
  Definition of measurement. Levels of measurement/types of scales (nominal, ordinal, interval, ratio), properties. Examples of different scale types from famous cross-cultural studies (European Social Survey, World Values Survey, European Values Study). Possible mathematical transformations with different scale types. Most frequent mistakes in defining scale type. Indexing.
• Data transformation
  Inspecting data sets, creating subsets. Creating new variables, scale reduction, scale normalization, creating indexes, changing variable type.
• Descriptive statistics
  Calculating proportions, mean, mode, median (for odd and even scales), standard deviation and variance. Treating missing data in R. R functions for calculating basis statistics. Frequencies and cross-tables. Different ways of calculating proportions for cross-tables. Creating frequencies and cross-tables in R. Using package “stargazer” for exporting results
• Visualization
  Basic graphs in R. Package ggplot2 and its opportunities for visualization.

Assessment Elements

• Home assignment
  Grading criteria:1) Correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
• Reading previous research
• Essay
• 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

• Interim assessment (1 module)
  0.3 * Essay + 0.4 * Exam + 0.2 * Home assignment + 0.1 * Reading previous research

Bibliography

Recommended Core Bibliography

• 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
• Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1175341

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