Module GW4750-KP11

Duration

2 Semester

Turnus of offer

every summer semester

Credit points

11

Course of studies, specific fields and terms:

• Master in Health and Healthcare Science 2025, optional subject, Health Services Research
• Master in Health and Healthcare Science 2019, optional subject, Health Services Research

Classes and lectures:

• Advanced Study Designs (seminar, 1 SWS)
• Applied Qualitative Research (seminar, 1 SWS)
• Advanced Methods of Knowledge Synthesis and Modeling (seminar, 2 SWS)
• Advanced data analysis with R (seminar, 2 SWS)

Workload:

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

Contents of teaching:

• Randomized and non-randomized studies for evaluation in healthcare
• Advanced statistical methods for the evaluation of healthcare (health services research or quality management)
• Developing healthcare science questions
• Identification and application of appropriate methods for answering various healthcare science questions
• Study selection and data extraction for systematic reviews
• Quality assessment of primary studies
• Meta-analysis (effect sizes, fixed-effect and random-effects models, heterogeneity, forest plots)
• Presentation and reporting of systematic reviews
• Deepening data analysis, theory development, and presentation of results in qualitative research
• Triangulation in qualitative research
• Ethical aspects in research involving vulnerable groups
• Model assumptions for linear and logistic regression
• Cross-validation
• Data exploration with ggplot2
• Graphical representation of model results
• Visualization of uncertainties
• Creation of interactive dashboards and visualizations

Qualification-goals/Competencies:

• Knowledge and understanding: Students can describe a wide range of study types and analyze their suitability for descriptive, explanatory, or evaluative health services research questions. In particular, they can describe and explain the strengths and weaknesses of various randomized and non-randomized studies, as well as routine data-based studies and mixed-methods studies.
• Knowledge and understanding: Students understand the importance of systematic reviews and meta-analyses for generating evidence in health and healthcare sciences.
• Knowledge and understanding: Students are familiar with the individual steps involved in conducting a systematic review and meta-analysis (e.g., search strategies, quality assessment, statistical models).
• Knowledge and understanding: Students are familiar with suitable AI-based tools that support the implementation of systematic reviews and meta-analyses.
• Knowledge and understanding: Students are familiar with suitable software that supports the assessment of bias risk and enables meta-analyses.
• Knowledge and understanding: Students deepen their knowledge of different strategies for qualitative data analysis.
• Knowledge and understanding: Students recognize the specific challenges involved in implementing qualitative and quantitative study designs to answer healthcare science questions. They reflect on the methodological perspectives of different research approaches.
• Knowledge and understanding: Students are familiar with the theoretical foundations of linear and logistic regression models as well as key methods for model validation.
• Knowledge and understanding: Students understand the prerequisites, assumptions, and limitations of linear and logistic regression models and can classify their use in different
• Knowledge and understanding: Students have an in-depth understanding of the importance of visualizations in exploratory data analysis and the presentation of results.
• Use, application, and generation of knowledge: Students are able to independently develop systematic search strategies and implement them in relevant databases.
• Use, application, and generation of knowledge: Students can select primary studies according to specified criteria, evaluate them critically, and extract data reliably.
• Use, application, and generation of knowledge: Students apply statistical methods to conduct and interpret meta-analyses, take heterogeneity and bias into account, and draw reflective conclusions.
• Use, application, and generation of knowledge: Students can prepare the results of systematic reviews and meta-analyses in accordance with international standards (e.g., PRISMA) and classify them in scientific discourse.
• Use, application, and generation of knowledge: Students can apply different methods of qualitative data analysis appropriately to the subject matter, acquire skills in the development of empirically based theories, and are able to present the results of qualitative analyses in a comprehensible manner.
• Use, application, and generation of knowledge: Students are able to prepare and analyze data sets in R and document results in a reproducible manner.
• Use, application, and generation of knowledge: Students can implement linear and logistic regression models in R, calculate quality measures, check model assumptions, and interpret the results in a well-founded manner.
• Use, application, and generation of knowledge: Students apply model validation procedures critically and reflectively.
• Use, application, and generation of knowledge: Students create appealing and scientifically accurate visualizations of data and model results using R.
• Use, application, and generation of knowledge: Students are able to answer complex research questions with the help of suitable statistical models and critically reflect on the results.
• Use, application, and generation of knowledge: Students can use Shiny to create dashboards and interactive visualizations and use them for presentation and knowledge transfer in research and practice.
• Communication and cooperation: Students are able to communicate their approach and results when creating a systematic review in a clear, transparent, and audience-appropriate manner (e.g., in interdisciplinary teams or publications).
• Kommunikation und Kooperation: Die Studierenden können bei komplexen Evidenzsynthesen kooperativ im Team arbeiten (z. B. beim Screening oder der doppelten Datenextraktion) und die Ergebnisse kritisch abgleichen.
• Kommunikation und Kooperation: Die Studierenden können ihre Analysestrategien und Ergebnisse klar, transparent und zielgruppenorientiert präsentieren – sowohl schriftlich als auch mündlich.
• Kommunikation und Kooperation: Die Studierenden tauschen sich sach- und fachbezogen untereinander aus und reflektieren die Notwendigkeit der Vernetzung mit bedeutenden Stakeholdern (z. B. Wissenschaftlern, Gatekeepern, Patientenvertretern) für die Beantwortung versorgungswissenschaftlicher Forschungsfragen.
• Kommunikation und Kooperation: Die Studierenden sind in der Lage, in Teams bei der Bearbeitung komplexer Datensätze konstruktiv zusammenzuarbeiten, Ergebnisse abzugleichen und kritisch zu diskutieren.
• Wissenschaftliches Selbstverständnis / Professionalität: Die Studierenden reflektieren systematische Übersichtsarbeiten und Meta-Analysen als zentrale Methoden evidenzbasierter Forschung und Praxis und sind in der Lage, deren Bedeutung für wissenschaftliches Arbeiten und evidenzbasiertes Handeln kritisch einzuordnen.
• Wissenschaftliches Selbstverständnis / Professionalität: Die Studierenden entwickeln ein kritisches Bewusstsein für Qualität, Transparenz und Reproduzierbarkeit als zentrale Standards wissenschaftlicher Praxis.
• Wissenschaftliches Selbstverständnis / Professionalität: Die Studierenden können die gesellschaftliche und wissenschaftliche Relevanz systematischer Übersichtsarbeiten für Entscheidungsprozesse in Forschung, Politik und Praxis einschätzen.
• Wissenschaftliches Selbstverständnis / Professionalität: Die Studierenden entwickeln ein kritisches Bewusstsein für den verantwortungsvollen Einsatz von Datenanalysen und können die Grenzen von Interpretationen klar benennen.

Grading through:

• written exam

Responsible for this module:

• Prof. Dr. phil. Matthias Bethge

Teacher:

• Institute of Health Sciences
• Institute of Family Medicine
• Institute for Social Medicine and Epidemiology
• Prof. Dr. phil. Matthias Bethge
• Prof. Dr. Katharina Röse
• Dr. rer. hum. biol. Nora Eisemann
• Mag. rer. nat. Stella Lemke

Literature:

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Language:

• German and English skills required

Notes:

Last Updated:

30.09.2025