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Data Analysis in Clinical Research (CLRS90010)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
August
Email: continuing-education@unimelb.edu.au
Phone: + 61 3 8344 0149
Contact hours: https://unimelb.edu.au/professional-development/contact-us
Overview
| Availability | August |
|---|---|
| Fees | Look up fees |
Data analysis methods are an integral part of modern clinical research. They are powerful techniques that enable researchers to draw meaningful conclusions from data collected through observation, survey, or experimentation.
However, data analysis is a huge discipline with different paradigms, schools of thought and alternative methodologies. Therefore consideration of the appropriate methods used must be undertaken when designing a study and selecting variables and groups.
This subject introduces students to the basic principles of qualitative and quantitative data analysis techniques. It will provide a functional grounding in the theoretical concepts behind each type of analysis, as well as exploration of the interpretation of data and the difference, where applicable, between clinical vs statistical significance.
Intended learning outcomes
On completion of this subject students should be able to:
- describe the theoretical concepts behind a range of qualitative and quantitative data analysis techniques
- compare and contrast the strengths and weaknesses of different qualitative and quantitative data analysis techniques
- describe a strategy for selecting an appropriate data analysis technique based on the study design selected and/or research data collected
- competently perform a range of basic data analysis techniques using appropriate analysis software and interpret analysis output/s
- provide a rationale for the importance of statistical power and perform power calculations
- identify and discuss the key elements associated with ensuring data integrity including storage, management, collation and coding
- critically compare and contrast statistical vs clinical significance and its relevance to clinical practice
- demonstrate confidence in discussing the validity of data analysis outcomes reported in the scientific literature.
Generic skills
- to engage with unfamiliar problems and identify relevant data analysis strategies
- to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of data analysis
- communicate advanced data analysis concepts in written and oral form;
- the ability to comprehend complex data analysis information
- exercise responsibility for their own learning;
- manage their time effectively.
Last updated: 8 November 2024