Statistical Modelling
Module title | Statistical Modelling |
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Module code | SSIM915 |
Academic year | 2023/4 |
Credits | 15 |
Module staff | Dr Chris Playford (Lecturer) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 11 |
Number students taking module (anticipated) | 25 |
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Module description
Statistical models help us deal with the messy complexity of the social world. In this module, you will learn how to select and estimate models which are appropriate for social science data. Using these models is a core data science skill. Taking this course will help you to understand the overall framework of generalised linear models and to fit regression models suitable for continuous or categorical outcomes using the statistical software R. You will also understand better how to work with data. The course is suitable for students with some prior experience of quantitative methods.
Module aims - intentions of the module
The aims of this module are to enable students to be able to:
- Develop skills in working with social science data using R
- Estimate regression models for continuous or categorical outcomes
- Understand the appropriate model for the data
- Interpret and compare the outputs of statistical models
- Explain the findings from their analyses
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. understand the appropriate statistical model for the data;
- 2. specify statistical models using R;
- 3. interpret the output from regression models for continuous or categorical outcomes;
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. demonstrate proficiency in the use of R for statistical modelling;
- 5. understand code in R and implement appropriate commands to perform relevant analyses;
- 6. understand the overall framework of Generalised Linear Models;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. demonstrate familiarity with a range of social data science models;
- 8. demonstrate understanding of data management skills;
- 9. communicate and explain the findings from statistical models;
Syllabus plan
Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:
- Accessing secondary data
- Data management in R
- Linear regression models for continuous outcomes
- Regression models for binary outcomes
- Modelling categorical and count outcomes
- Modelling hierarchical data – mixed models
- Interaction effects, transformations and model diagnostics
- Survey weighting
- Handling missing data
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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20 | 130 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled Learning & Teaching activities | 20 | 10 x 2-hour lectures and computer labs .These lectures cover the main concepts of the course. These practical sessions cover the application of techniques |
Guided Independent Study | 60 | Reading and preparing for lectures and labs (around 4-6 hours per week) |
Guided Independent Study | 70 | researching and writing assessments and assignments (researching, planning and writing coursework) |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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In-class exercises | 15 minutes each x 4 | 1-9 | Peer and oral feedback |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Data analysis short report 1 | 50 | 1,750 words plus tables, graphs based on data analysis | 1-9 | Written feedback |
Data analysis short report 2 | 50 | 1,750 words plus tables, graphs based on data analysis | 1-9 | Written feedback |
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0 |
Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
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Data analysis short report 1 | Data analysis short report 1 (1,750 words plus tables and graphs) | 1-9 | August/September re-assessment period |
Data analysis short report 2 | Data analysis short report 1 (1,750 words plus tables and graphs) | 1-9 | August/September re-assessment period |
Indicative learning resources - Basic reading
Ralston, Gayle, Connelly and Playford (Forthcoming) What is statistical modelling in R? London: Bloomsbury.
Treiman, D. J. (2009). Quantitative Data Analysis: Doing social research to test ideas. San Francisco: Jossey-Bass.
Agresti, A. and Finlay, B. (2014) Statistical methods for social sciences. Upper Saddle Hall, NJ: Prentice Hall (4th edition).
Bartholomew, D. J., Steele, F., Moustaki, I., & Galbraith, J. I. (2008). Analysis of Multivariate Social Science Data. Boca Raton, FL: CRC Press.
Indicative learning resources - Web based and electronic resources
R for Data Science
ggplot2: Elegant Graphics for Data Analysis
UK Data Service
https://www.ukdataservice.ac.uk
National Centre for Research Methods
Credit value | 15 |
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Module ECTS | 7.5 |
NQF level (module) | 7 |
Available as distance learning? | No |
Origin date | 16/02/2022 |