Learning outcomes of the course
After completing the study unit, the student will be able to
• know which data have been collected in the Survey of Health, Ageing and Retirement in Europe (SHARE), and how and when the data have been collected
• understand how the SHARE data is structured and how data files for analyses can be constructed
• learn about theories on wellbeing, ageing and retirement
• acquire statistical skills to analyze cross-sectional data
• acquire statistical skills to analyze longitudinal data
The study unit develops the following generic skills/generic competences: Digitalization, Internationality, Critical thinking
Contents of the course
• Course introduction and introduction to SHARE European Research Infrastructure Consortium (SHARE-ERIC)
• Overview of SHARE: survey design, questionnaire modules and data structure
• Use of easySHARE and regular SHARE data
• Theoretical premises of wellbeing, ageing and retirement
• Building datasets for research and using survey weights
• Applying easySHARE data to make cross-sectional (linear regression analysis, logistic regression analysis and multilevel analysis) and longitudinal (pooled OLS, fixed and random effects, hybrid models) analyses related to wellbeing and ageing
Modes of study and assessment criteria
Independent learning based on course materials.
Independent work 135 h/135 h.
The course is available for the students of the University of Eastern Finland and for Open University students.
Assessment criteria:
Pass / fail (80 % correct answers)
The assessment includes lecture examinations and learning assignments
Further information
The course is provided in English and will be conducted in an eLearning environment (Moodle) where the learning materials are provided (lecture videos and slides, empirical exercises using Stata, and links to additional materials). The student registers to use the SHARE data and uses the easySHARE data during the course. The student runs analyses with statistical software; course materials are provided with Stata. Syntaxes are provided also in R.
Prerequisites
Before enrolling in the course, the student is expected to have a basic knowledge of descriptive and inferential statistics (e.g. normal distribution, testing hypotheses, effect size and statistical significance). In addition, basic skills using a statistical software, either Stata or R, is required.
Recommended previous courses at UEF:
Basics in statistical methods for social scientists (in Finnish)
Introduction to statistical methods (in English)
Supporting courses at UEF:
Advanced Course in Statistical Methods 1 (in Finnish)
Please note that it is very challenging to pass the course without the basic knowledge of statistical analyses. However, if the student has no previous experience on statistics or the use of Stata or R, in the beginning of the course, the student is directed to materials where these areas are covered (not included in the 5 ECTS).
Time and location
Time: 01.09.2026 –
Periods 1–4
Location: eLearn platform (Moodle)
Evaluation
Pass/Fail
- Opettaja
Inna Lisko