Quantitative Data Analysis II
Course code
RAS6014.YK
old course code
Course title in Estonian
Kvantitatiivne andmeanalüüs II
Course title in English
Quantitative Data Analysis II
ECTS credits
5.0
Assessment form
Examination
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To create possibilities for the development of theoretical knowledge and practical skills for carrying out quantitative data analysis on intermediate level. To create opportunities for having the skills for carrying out independently quantitative research, choosing appropriate methods, present and interpret the findings.
Brief description of the course
The course consists of lectures/seminars and practical classes where students are expected to think and work along actively.
For lectures/seminars, students must examine the literature and tasks given by the lecturer. The objective of practical classes is to complement the content of lectures/seminars with practical examples and tasks. For learning practical knowledge and skills in data analysis, data processing package SPSS Statistics is used.
Topics covered in the course:
• Multiple lineal regression (OLS), including different type of explanatory variables in the model;
• Logistic regression for binary outcome variables;
• Data management, incl. creating new variables, use of core indicators in the analysis (education, occupation, income);
• Exploratory factor analysis;
• Cluster analysis
In addition to lectures/practical classes and compulsory reading, students must complete two home assignments consisting of practical data analysis tasks within the scope of materials covered. It requires independent work with data (using the data processing package SPSS Statistics), presentation of one’s results (solution for the task + syntax file for the way of solution). Independent work forms a part of the final grade for the course.
For lectures/seminars, students must examine the literature and tasks given by the lecturer. The objective of practical classes is to complement the content of lectures/seminars with practical examples and tasks. For learning practical knowledge and skills in data analysis, data processing package SPSS Statistics is used.
Topics covered in the course:
• Multiple lineal regression (OLS), including different type of explanatory variables in the model;
• Logistic regression for binary outcome variables;
• Data management, incl. creating new variables, use of core indicators in the analysis (education, occupation, income);
• Exploratory factor analysis;
• Cluster analysis
In addition to lectures/practical classes and compulsory reading, students must complete two home assignments consisting of practical data analysis tasks within the scope of materials covered. It requires independent work with data (using the data processing package SPSS Statistics), presentation of one’s results (solution for the task + syntax file for the way of solution). Independent work forms a part of the final grade for the course.
Learning outcomes in the course
Upon completing the course the student:
- has elementary theoretical knowledge and practical skills for applying linear regression analysis for sociological research and has the skills to apply the knowledge and skills independently;
- has elementary theoretical knowledge and practical skills for applying exploratory factor analysis for sociological research and has the skills to apply the knowledge and skills independently;
- is able to make independent decisions for choosing suitable analysis methods and is capable independently carry out respective analysis within the scope of the material studies in the course;
- has elementary theoretical knowledge and practical skills for applying binary logistic regression analysis for sociological research and has the skills to apply the knowledge and skills independently;
- has elementary theoretical knowledge and practical skills for applying basic cluster analysis for sociological research and has the skills to apply the knowledge and skills independently.
- has elementary theoretical knowledge and practical skills for applying linear regression analysis for sociological research and has the skills to apply the knowledge and skills independently;
- has elementary theoretical knowledge and practical skills for applying exploratory factor analysis for sociological research and has the skills to apply the knowledge and skills independently;
- is able to make independent decisions for choosing suitable analysis methods and is capable independently carry out respective analysis within the scope of the material studies in the course;
- has elementary theoretical knowledge and practical skills for applying binary logistic regression analysis for sociological research and has the skills to apply the knowledge and skills independently;
- has elementary theoretical knowledge and practical skills for applying basic cluster analysis for sociological research and has the skills to apply the knowledge and skills independently.
Teacher
Dotsent Kadri Täht ja lektor Eve-Liis Roosmaa
Prerequisite course 1
The course is a prerequisite
Study programmes containing that course