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Regular version of the site
Master 2019/2020

# Basic Statistics and Introduction into "R"

Type: Compulsory course (Comparative Social Research)
Area of studies: Sociology
Delivered by: School of Sociology
When: 1 year, 1 module
Mode of studies: offline
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 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.
• 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.
• 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.
• 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

#### 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