- UCAS course code
- UCAS institution code
BASS Philosophy and Criminology
Year of entry: 2023
- View tabs
- View full page
Course unit details:
Applied Statistics for Social Scientists
|Unit level||Level 1|
|Teaching period(s)||Semester 2|
|Available as a free choice unit?||Yes|
The main topics to be covered are:
- Data exploration and visualization using R
- Descriptive statistics
- Modelling with Continuous Data
- Modelling with categorical Variables
The aims of this course are for each student to achieve:
- an introductory to data cleaning and exploration
- an understanding of basic statistics tests
- an understanding of multivariate statistical analysis
- an ability to use statistical software R
- be able to import and export data in R
- be able to prepare data in R
- be able to produce visualisations of data
- be able to understand and run descriptive statistics in R
- be able to understand and run a battery of test of hypothesis in R
- be able to understand and run a variety of statistical models in R
Teaching and learning methods
Lectures, practicals and coursework.
Please note the information in scheduled activity hours are for guidance only and may change.
|Written assignment (inc essay)||75%|
Feed-back on individual based essay
Agresti, A. (2018). Statistical Methods for the Social Sciences (5th ed.). Pearson.
Fogarty, B. (2019). Quantitative Social Science Data with R.
Hothorn, T., & Everitt, B. (2014). A handbook of statistical analyses using R (Third edition). CRC Press, Taylor & Francis Group.
Landers, R. N. (2019). A step-by-step introduction to statistics for business. SAGE.
Leon-Guerrero, A., & Frankfort-Nachmias, C. (2018). Essentials of social statistics for a diverse society (Third edition). SAGE.
Wickham, H., & Grolemund, G. (2017). R for Data Science: import, tidy, transform, visualize, and model data. O’Reilly UK Ltd.
|Scheduled activity hours|
|Practical classes & workshops||8|
|Independent study hours|
|Todd Hartman||Unit coordinator|