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Study information

Statistical Modelling

Module titleStatistical Modelling
Module codeSSIM915
Academic year2023/4
Credits15
Module staff

Dr Chris Playford (Lecturer)

Duration: Term123
Duration: Weeks

11

Number students taking module (anticipated)

25

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 ActivitiesGuided independent studyPlacement / study abroad
20130

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning & Teaching activities2010 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 Study60Reading and preparing for lectures and labs (around 4-6 hours per week)
Guided Independent Study70researching and writing assessments and assignments (researching, planning and writing coursework)

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In-class exercises15 minutes each x 41-9Peer and oral feedback

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Data analysis short report 1 501,750 words plus tables, graphs based on data analysis 1-9Written feedback
Data analysis short report 2501,750 words plus tables, graphs based on data analysis 1-9Written feedback
0
0
0
0

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Data analysis short report 1 Data analysis short report 1 (1,750 words plus tables and graphs)1-9August/September re-assessment period
Data analysis short report 2Data analysis short report 1 (1,750 words plus tables and graphs)1-9August/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

https://r4ds.had.co.nz/

 

ggplot2: Elegant Graphics for Data Analysis

https://ggplot2-book.org/

 

UK Data Service

https://www.ukdataservice.ac.uk

 

National Centre for Research Methods

http://www.ncrm.ac.uk  

Key words search

Statistical Modelling; Generalised Linear Models; Social Data Science; Linear regression; logistic regression; categorical data analysis

Credit value15
Module ECTS

7.5

NQF level (module)

7

Available as distance learning?

No

Origin date

16/02/2022