Course - Statistical and epidemiological methods 1 - MH3021
Statistical and epidemiological methods 1
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About the course
Course content
The course gives a basic introduction at master's level in epidemiology and a selection of statistical analysis methods in medicine and health sciences. The course goes through which quantitative methods are most suitable for different research questions. Other central topics include choice of study design, sources of error in quantitative research, descriptive statistics, application of commonly used statistical methods for comparisons and correlations, as well as critical reading and assessment. Emphasis is placed on providing students with hands-on experience in various aspects related to the completion of quantitative studies, with special emphasis on design, analysis, and the presentation and interpretation of results.
Learning outcome
Knowledge:
After completing and passing the course, the student should be able to:
- Epidemiological and clinical study designs, and their strengths and weaknesses and suitability for various research questions
- Measures of disease prevalence and associations
- Random error
- Description of categorical and continuous data
- Analyses of one and two samples for both categorical and continuous data
Additionally, the students will have knowledge of:
- Systematic errors such as confounding, selection bias and measurement errors
- Regression analysis of categorical and continuous data
Skills
After completing and passing the course, the student should be able to:
- Calculate and interpret measures of disease incidence and associations
- Identify different study designs in research articles and describe their advantages and disadvantages
- Identify different sources of systematic errors
- Choose appropriate summary measures and graphical representation of quantitative data
- Perform analyses of categorical and continuous data using the provided syntax for use in statistical programs
- Interpret analyses of categorical and continuous data
- Calculate sample size
General competence
After completing and passing the course, the student should be able to:
- Read and undertake a simple critical appraisal of articles that use quantitative methods and be able to apply knowledge from quantitative studies
- Understand the presentation of statistical analyses and interpret results in research literature
- Apply their knowledge and skills to plan a quantitative study under supervision
Learning methods and activities
The course will primarily be applied and focus on interpretation of analyses. Topics will be introduced briefly through lectures or team-based learning (TBL) before the students do practical exercises.
Compulsory assignments
- Approved assignment activities
Further on evaluation
An compulsory written assignment involving interpretation of results must be approved in order to sit the exam. Approved compulsory activity is valid for three consecutive semesters after approval. Letter grade.
Specific conditions
Admission to a programme of study is required:
Advanced Clinical Nursing (MAKALLMSP)
Advanced Nursing (SPVAKSPL)
Ageing and Elderly's Health (SPVIDAEHS)
Clinical Health Science (MKLIHEL)
Clinical Nursing (MKLISP)
Global Health (MSPUHE)
Health Care Management (MHLED)
Healthcare Informatics (MHI)
MH - Subjects on scientific methods at master's level (MHMETODE-H)
Master of Science in Specialised Nursing (MSPL)
Medical Imaging Technologies (MMEDBT)
Medical studies (CMED)
Mental Health (MPHLS)
Miscellaneous Courses - Faculty of Medicine and Health Sciences (EMNE/MH)
Molecular Medicine (MSMOLMED)
Neuroscience (MSNEUR)
Nursing education in cardiology (SPVKAR)
Pharmacy (MFARMASI)
Public Health (MFHLS)
Public Health Nursing (MHELSP)
Required previous knowledge
Completed bachelor's degree or equivalent
Subject areas
- Health Science
- Public Health
Contact information
Course coordinator
Lecturers
Department with academic responsibility
Examination
Examination
Ordinary examination - Autumn 2025
School exam
The specified room can be changed and the final location will be ready no later than 3 days before the exam. You can find your room location on Studentweb.