Multivariate Data Analysis in R
Course: Sociаl Technologies
Structural unit: Faculty of Sociology
            Title
        
        
            Multivariate Data Analysis in R
        
    
            Code
        
        
            ОК12
        
    
            Module type 
        
        
            Обов’язкова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2021/2022
        
    
            Semester/trimester when the component is delivered
        
        
            2 Semester
        
    
            Number of ECTS credits allocated
        
        
            3
        
    
            Learning outcomes
        
        
            1.1 Know modern methods of linear regression analysis.
1.2 Know the methods of experimental design.
2.1 Be able to build and interpret linear regression models and determine their quality.
2.2 Be able to analyze and summarize the ranks in the systems of characteristics.
2.3 Be able to build and interpret models with factorial design.
4.1 Demonstrate skills of independent research work, mastering new knowledge.
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            1. Know the methods of collecting sociological information, basic methods of analysis of sociological data.
2. Be able to work with sociological data, read professional sociological publications and formulate ideas in English.
3. Have the skills to use methodologies and conceptual foundations of sociological analysis; have basic skills in R.
        
    
            Course content
        
        
            1. Programming in Rstudio.
2. Building of regression models.
3. Methods of analysis of rank characteristics.
4. Factorial design.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Auspurg Katrin, Hinz Thomas. Factorial Survey Experiment. - Sage, Series: Quantitative Applications in the Social Survey, 2015. - Vol.175. - 143 p.
2. Wooldridge Jeffrey M. Introduction to econometrics.- Cengage Learning, 2015, 603 p. (pp.18-211)
3. Treiman Donald J. Quantitative Data Analysis. Doing Social Research to Test Ideas.- Jossey-Bass, 2008, 444 p. (pp 151-333)
4. Hox, Joop J., Ita G. Kreft, and Piet L. J. Hermkens (1991): The Analysis of Factorial Surveys.- 1991, Sociological Methods & Research vol. 19, issue 4, pages 493-510
5. Sydorov MV-S. Practical implementation of factorial design by means of R.- Sociological studies, scientific and practical journal.- Lutsk: №.2 (7), 2015, p.58-66
6. Sydorov MV-S., Sereda OS, Mramornova OM Using LimeSurvey for online implementation of factorial design in surveys.- Current issues of sociology, psychology, pedagogy: Collection of scientific papers. - К.: Logos, - vol.4 (29), 2015, pp.134-111
        
    
            Planned learning activities and teaching methods
        
        
            Lecture, practical lesson, self-study work
        
    
            Assessment methods and criteria
        
        
            1. Distance course "Introduction to R" DataCamp - the presence of a certificate RS 1.1, RS 4.1. 6 points / 2 points
2. Homework in the form of tests RS2.1, RS2.2, RS2.3, RS 4.1 (total 7 during the study of the course) – 49 points / 28 points (7 maximum or 4 minimum points for each)
3. Final test 1 RS1.1, RS2.1 – 15 points / 10 points
4. Final control work 2 RS1.1, RS2.1, RS 4.1. – 15/10 points
5. Final control work 3 RS1.1, RS1.2, RS2.1, RS 2.2, RS 2.3. – 15 points / 10 points
final assessment – credit
        
    
            Language of instruction
        
        
            ukrainian
        
    Lecturers
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