Description
Summary
This module aims to give a more solid grounding in research methods, building on the statistical skills learnt in CHLD0064 (Applied Statistics for Health Research I). You will learn skills and tools that will be invaluable in interpreting published research as well as developing your own studies.Ìýhe module is made up of 3 core parts (Introduction to Regression Analysis, Dealing with missing data and Sample size estimations and power calculations) and 2 optional parts (any 2 of the following choices: Logistic Regression, Time-to-event/survival analysis, Analysing 2x2 tables, ANOVA/GLMs using SPSS, Reliability and Validity, Bayesian Analysis). The sample size session involves working with a prepared web link and excel spreadsheets. Dealing with Missing Data, ANOVA/GLMs, and Introduction to Regression sessions all have interactive SPSS practicals on the day, and Time-to-event/survival and Logistic analysis giveÌýexample SPSS outputs which are discussed (you will have access to the datasets involved to replicate in your own time if you wish to).
Learning Objectives and Outcomes
After completing this module, you should be able to:
- Understand and undertake basic regression models
- Be able to perform sample size calculations
- Have a full understanding of techniques for dealing with missing data and be able to perform multiple imputation within SPSS
- Depending on the optional parts students take, they will also gain familiarity and fundamental understanding of a combination of 2 of the following topics: Logistic regression, ANOVA/GLMs using SPSS, Reliability and validity, Bayesian analysis, Analysis of 2x2 tables, Survival/Time-to-event data analysisÌýÌý
Who is this module for?
This module is optional to all GOS ICH Masters and MRes programmes. Applied Statistics for Health Research I (CHLD0064) is a prerequisite for this module.
Teaching and Learning Methods
The module is delivered as a series of workshops, each consisting of lectures interspersed with practical activities, and all sessions are accompanied by comprehensive notes.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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