Advanced methods of Data Analysis in Sociology
Course: Sociаl Technologies
Structural unit: Faculty of Sociology
            Title
        
        
            Advanced methods of Data Analysis in Sociology
        
    
            Code
        
        
            ОК6
        
    
            Module type 
        
        
            Обов’язкова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2024/2025
        
    
            Semester/trimester when the component is delivered
        
        
            2 Semester
        
    
            Number of ECTS credits allocated
        
        
            8
        
    
            Learning outcomes
        
        
            1.1 Knowledge of heuristic possibilities and limitations of of quantitative statistical data analysis methods in modern empirical sociological research.
2.1 Ability to use models of multidimensional data analysis methods in empirical sociological research.
2.2 Ability to design and evaluate measurement models for the construction of complex sociological indices.
2.3 Ability to use modern approaches and methods to visualize data and analysis results.
2.4. Ability to combine for further analysis empirical data obtained in sociological research by different methods from different sources
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            Students should be familiar with the basics of statistical data analysis of empirical sociological research, including correlation analysis and multiple linear regression models, and be able to work with computer programs for statistical data analysis.
        
    
            Course content
        
        
            1. The purpose and structure of the course. Review of the latest trends in statistical analysis of social data.
2. Linear regression model. Use of dichotomous (dummy) independent variables.
3. Logistic regression model.
4. Analysis of multidimensional frequencies tables. Hierarchical loglinear model.
5. Measurement of latent variables. Exploratory factor analysis.
6. Structural equations modeling and confirmatory factor analysis as the tools for parsimony measurement models.
7. Using the SPSS syntax language.
8. Visualization of data and data analysis results.
9. Preparation for the analysis of data obtained from various sources.
10. Weighing of sample data.
11. Methods of working with missing data.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Bors D. Data Analysis for the Social Sciences. Integrating Theory and Practice, Second Edition, 2018.- 664 p. – pp. 34-96, 167-254, 312-580
2. Schumacker, Randall E. A beginner’s guide to structural equation modeling / Randall E. Schumacker, Richard G. Lomax. – Fourth edition, 2016.- 351 p. – pp. 1-14, 85-105
3. Gorbachyk A.P., Salnikova S.A. Data Analysis in Sociological Research Using SPSS (in Ukrainian). pp. 24-78, 112-146
4. Engebretsen M. Kennedy H. (eds) Data Visualization in Society .- Amsterdam University Press, 2020.- 464 p. -- p. 17-77, 111-141
5. Laaksonen S. Survey Methodology and Missing Data .- Springer, 2018.- 224 p.-- 99-133, 141-217
        
    
            Planned learning activities and teaching methods
        
        
            Lecture, practical training, personal work
        
    
            Assessment methods and criteria
        
        
            1 semester
1. Test work 1 (test) for topics 1-4, LO1.1, LO2.1 –  18 points / 30 points.
2. Test work 2 (test) for topics 5- 6, LO1.1, LO2.2 – 18 points / 30 points.
3. Performing tasks in training classes LO1.1, LO2.1, LO2.2, LO2.3, LO2.4  – 24 points / 40 points.
final assessment - credit 
2 semester
1. Test work 1 (test) for topics 1-2, LO2.1, LO2.3 –  12 points / 20 points 
2. Test work 2 (test) for topics 3-5, LO2.1, LO2.4 – 12 points / 20 points.
3. Performing tasks in training classes LO1.1, LO2.1, LO2.2, LO2.3, LO2.4 – 12 points / 20 points.
final assessment – exam, LO1.1, LO2.1, LO2.2, LO2.3, LO2.4
        
    
            Language of instruction
        
        
            ukrainian
        
    Lecturers
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