Course - Statistical and epidemiological methods 1 - MH3022
Statistical and epidemiological methods 1
Choose study yearNew from the academic year 2024/2025
About
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 causal relationships
- Random errors
- 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
Ferdigheter
After completing and passing the course, the student should be able to:
- Calculate and interpret measures of disease incidence and causal relationships
- 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
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, with a lesser focus on formulas and calculations. Topics can be introduced shortly before the students do practical exercises with ready-made syntax (primarily .do files are used in Stata, but SPSS syntax can be presented if needed).
Compulsory assignments
- Approved assignment activities
Further on evaluation
An compulsory assignment involving the evaluation and interpretation of results from a research paper 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)
Midwifery (MJORDMOR)
Miscellaneous Courses - Faculty of Medicine and Health Sciences (EMNE/MH)
Molecular Medicine (MSMOLMED)
Neuroscience (MSNEUR)
Nursing education in cardiology (SPVKAR)
Pharmacy (MFARMASI)
Physical Activity and Health (MSPAHE)
Public Health (MFHLS)
Public Health Nursing (MHELSP)
Required previous knowledge
Completed bachelor's degree or equivalent
Subject areas
- Health Science
- Public Health