Last modified: 31 May 2022 13:05
This course intends to develop a student’s statistical skills and understanding so that they can apply common multivariate regression modelling techniques to a range of health research data. The course will focus on the application, interpretation and communication of common regression models, including general linear models, log-linear models, logistic regression, and survival analysis. It assumes that students will already have completed a first course in statistics and have an understanding of bivariate techniques and basic mathematical skills.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Session | Second Sub Session | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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Health research often involves complex data which needs multivariate statistical techniques to answer research questions. This course will introduce you to key concepts in advanced regression modelling which can be applied to a range of data. It will cover some of the theory underlying different regression models and then take a practical approach to teach you how to investigate data in different contexts. You will use the statistical software package (SPSS) to apply different multivariate regression models, including how to check model assumptions, adjust for confounding and assess the model suitability. You will have the opportunity to analyse a variety of data sets and practice communicating the rationale for choice of statistical method and interpretation of results for a scientific audience.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 60 | |
|---|---|---|---|---|
| Assessment Weeks | 33,35,39 | Feedback Weeks | 28,32 | |
| Feedback |
Three short data analysis reports - each 20% = 60% Students will submit a short data analysis report for each of 3 main topics in teaching weeks 8, 10, 12 with feedback in weeks 10,12,14. Each topic is self contained and the feedback from each topic is not necessary before commencing the next topic. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Factual | Evaluate | Check a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit |
| Procedural | Analyse | Select and apply an appropriate regression model and interpret its results. |
| Procedural | Apply | Employ the statistical package SPSS to analyse data using regression methods |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience. |
| Assessment Type | Formative | Weighting | 40 | |
|---|---|---|---|---|
| Assessment Weeks | 27,30 | Feedback Weeks | 28,32,34 | |
| Feedback |
Students will submit a brief project plan in teaching week 2 with written feedback by teaching week 3 and optional consultation with tutor; final report will be in teaching week 5 with final feedback in teaching week 7. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using multivariate regression models. |
| Factual | Evaluate | Check a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit |
| Procedural | Analyse | Select and apply an appropriate regression model and interpret its results. |
| Procedural | Apply | Employ the statistical package SPSS to analyse data using regression methods |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience. |
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Feedback will be released with grade and will provide additional written commentary to facilitate ongoing learning |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Create | Communicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience. |
| Procedural | Apply | Employ the statistical package SPSS to analyse data using regression methods |
| Procedural | Analyse | Select and apply an appropriate regression model and interpret its results. |
| Factual | Evaluate | Check a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit |
| Conceptual | Understand | Understand and describe the rationale for using multivariate regression models. |
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