Module MA4550-KP05

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

5

Course of studies, specific fields and terms:

• Master in Health and Healthcare Science 2019, compulsory, Research in the health and care sciences
• Master in Health and Healthcare Science 2025, compulsory, Research in the health and care sciences

Classes and lectures:

• Statistical Methods (lecture, 2 SWS)
• Statistical Methods (exercise, 2 SWS)

Workload:

• 90 hours private studies and exercises
• 60 hours in-classroom work

Contents of teaching:

• Basics of descriptive and inferential statistics
• Bivariate analyses (for difference and correlation hypotheses)
• Regression analyses (linear and logistic regression)
• Survival time analyses (Kaplan-Meier and Cox regression)
• Dealing with missing values
• Data analysis by means of an established statistics program

Qualification-goals/Competencies:

• Knowledge and Understanding: Students know the difference between descriptive and inferential statistics.
• Knowledge and Understanding: Be able to name the major areas of univariate data descriptors (scale level, location measures, scatter measures, modality, skewness) and assign the various measures to scale levels according to their applicability.
• Knowledge and Understanding: Know the two main areas of inferential statistics (estimation and testing) and be able to name associated methods (point estimation and confidence interval or significance testing).
• Knowledge and Understanding: Know commonalities and similarities among measures of association, risk, and correlation.
• Knowledge and Understanding: Know the basic idea of regression as well as commonalities and similarities of linear and logistic regression.
• Knowledge and Understanding: Know the basic idea of survival time analysis.
• Knowledge and Understanding: Know about the problem of missing values.
• Use, application, and generation of knowledge: Students will be able to perform univariate and bivariate descriptive analysis of a given data set.
• Use, application, and generation of knowledge: to be able to pose research questions and hypotheses, operationalize them in such a way that they can be evaluated statistically, and choose an appropriate statistical method (as far as part of the course content).
• Use, application and generation of knowledge: to be able to check whether the assumptions of a chosen method of analysis are sufficiently fulfilled and can propose alternative methods of analysis.
• Use, application, and generation of knowledge: to be able to correctly report and interpret statistical results as well as critically examine analyses performed by others for potential problems in methodology, presentation of results, or interpretation.
• Communication and Cooperation: Students will be able to select and meaningfully communicate a scientific question and the results of a statistical analysis appropriate to answer the question.
• Communication and cooperation: to be able to point out possible weaknesses and possible misinterpretations of statistical results that might escape a non-scientifically literate addressee.
• Scientific self-concept/professionalism: students will be able to explain why it is appropriate to consider statistical aspects in all stages of quantitative health science research.
• Scientific self-concept/professionalism: to be committed to a careful statistical approach.
• Scientific self-concept/professionalism: students realistically assess their own statistical methodological competencies and independently expand and deepen their basic methodological knowledge.

Grading through:

• written exam

Responsible for this module:

• Prof. Dr. med. Alexander Katalinic

Teacher:

• Institute for Social Medicine and Epidemiology
• Prof. Dr. med. Alexander Katalinic
• Laura Schumann, M.Sc.

Literature:

• Schäfer A, Schöttker-Königer T (2015) : Statistik und quantitative Methoden für Gesundheitsfachberufe Heidelberg/Berlin: Springer
• Weiß C (2013) : Basiswissen Medizinische Statistik 6. Auflage. Heidelberg/Berlin: Springer

Language:

• German and English skills required

Notes:

Last Updated:

16.09.2025