Last modified: 18 Oct 2024 15:16
This intermediate-level course intends to advance a student's statistical skills and understanding of common and more advanced regression modelling techniques so that they can apply them to a wide range of health research data. The course will focus on introducing the student to the concepts underpinning generalised linear models. They will deepen their understanding of linear and logistic regression and learn how to analyse outcomes such as count data and time-to-event data using regression for count data and survival analysis. This course will focus on the application, interpretation, and communication of the learned methodologies. It assumes that students will already have completed a first course in introductory statistics and have an understanding of hypothesis testing and basic mathematical skills.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | 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 more advanced statistical techniques to answer research questions. This course will introduce you to key concepts in more advanced regression modelling (generalised linear models) which can be applied to a range of data, such as numerical, binary, count and time-to-event 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 package (SPSS) to apply different 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 | 40 | |
|---|---|---|---|---|
| Assessment Weeks | 35 | Feedback Weeks | 40 | |
| Feedback |
Students will submit a final report based on the analysis arising from their project plan in teaching week 10 with final feedback by teaching week 12. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using generalised linear 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 a statistical software to analyse data using generalised linear models |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and/or graphical displays as appropriate for a scientific audience. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 29 | Feedback Weeks | 30 | |
| Feedback |
Students will complete MCQ assessment in MyAberdeen which will cover course materials. Feedback will be obtained via on online session. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using generalised linear 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 a statistical software to analyse data using generalised linear models |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and/or graphical displays as appropriate for a scientific audience. |
| Assessment Type | Summative | Weighting | 40 | |
|---|---|---|---|---|
| Assessment Weeks | 39 | Feedback Weeks | 40 | |
| Feedback |
Students will complete MCQ assessment in MyAberdeen which will cover course materials. Feedback will be obtained via on online session. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using generalised linear 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 |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and/or graphical displays as appropriate for a scientific audience. |
| Assessment Type | Formative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | 29 | Feedback Weeks | 30 | |
| Feedback |
Students will submit a brief project plan in teaching week 4 with written feedback by teaching week 5 and optional consultation with tutor. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using generalised linear models |
| 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 |
|---|---|---|
| Conceptual | Understand | Understand and describe the rationale for using generalised linear models |
| Procedural | Analyse | Select and apply an appropriate regression model and interpret its results |
| Reflection | Create | Communicate the process and results of regression models using written, tabular and/or graphical displays as appropriate for a scientific audience. |
| Factual | Evaluate | Check a model's assumptions, adjust for confounding and use strategies to assess a model's suitability and fit |
| Procedural | Apply | Employ a statistical software to analyse data using generalised linear models |
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