Quantitative Data Analysis II
Course code
RAS6031.YK
old course code
Course title in Estonian
Kvantitatiivne andmeanalüüs II
Course title in English
Quantitative Data Analysis II
ECTS credits
6.0
Assessment form
Examination
lecturer of 2026/2027 Autumn semester
Kadri Täht (language of instruction:Estonian)
lecturer of 2026/2027 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Create opportunities for acquiring theoretical knowledge and practical skills in the quantitative analysis of social science data at an intermediate level. To support students' readiness for independent application of quantitative data analysis in social science research in the form of skills in data transformation, analysis, presentation and interpretation of results.
Brief description of the course
The course covers the following topics: regression with categorical variables (ANOVA), multiple linear regression models with the inclusion of different types of independent variables in the model, logistic regression: analysis of categorical variables; different data management methods and data transformations (including computing new variables or tranform ecisting variables), the use of basic variables in sociological research (education, occupation, income).
The course consists of lectures, seminars and practicums, where students are expected to actively think and work along. The participants of the course must independently work through the literature and tasks given by the lecturer, solve the tasks/assignments in the practicums, and do independent homework. The practicums are carried out in the form of practical examples and exercises. As data processing package will be used R and R Studio.
The course consists of lectures, seminars and practicums, where students are expected to actively think and work along. The participants of the course must independently work through the literature and tasks given by the lecturer, solve the tasks/assignments in the practicums, and do independent homework. The practicums are carried out in the form of practical examples and exercises. As data processing package will be used R and R Studio.
Learning outcomes in the course
Upon completing the course the student:
- has basic theoretical and practical knowledge of the application of linear regression analysis in social science studies and is able to independently apply this knowledge to the scope of the material covered in the course;
- has basic theoretical and practical knowledge of the application of logistic regression analysis in social science studies and can apply this knowledge independently in the scope of the material covered in the course;
- has basic practical skills in data organization, data transformations and the use of central characteristics such as education, occupation and income in different types of analyses;
- can independently choose a suitable analysis method for analysis within the scope of the topics and methods covered in the course and perform this analysis in the data processing package R.
- has basic theoretical and practical knowledge of the application of linear regression analysis in social science studies and is able to independently apply this knowledge to the scope of the material covered in the course;
- has basic theoretical and practical knowledge of the application of logistic regression analysis in social science studies and can apply this knowledge independently in the scope of the material covered in the course;
- has basic practical skills in data organization, data transformations and the use of central characteristics such as education, occupation and income in different types of analyses;
- can independently choose a suitable analysis method for analysis within the scope of the topics and methods covered in the course and perform this analysis in the data processing package R.
Teacher
Kadri Täht
Prerequisite course 1
Study programmes containing that course