Basic Statistics and Introduction into "R"
- Provide students with an understanding of the basic concepts of statistical analysis
- Provide students with an understanding of the basic principles of R programming
- 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
- Introduction into RInstalling 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 “foreign” and importing data frames. Attaching and detaching objects, indexing objects.
- MeasurementDefinition 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.
- Introduction into sociological inquiryTwo main research designs: qualitative and quantitative and their peculiar features. Stages of quantitative research. Possible units of analysis in quantitative research.
- Data transformationPackage “car”: installation, reading and most common problems. Inspecting data-sets, creating subsets. Creating new variables, scale reduction, scale normalization, creating indexes, changing variable type.
- Descriptive statisticsCalculating 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
- VisualizationBasic graphs in R. Package ggplot2 and its opportunities for visualization.
- Home assignmentGrading criteria:1) Correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
- Reading previous research
- ExamThe 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 (1 module)0.3 * Essay + 0.3 * Exam + 0.2 * Home assignment + 0.2 * Reading previous research
- 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
- 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