This module builds on already established statistical knowledge. It aims to further develop key statistical skills and the ability to apply these in conducting research. Learning will be oriented towards:
i. Assessing the strengths and limitations of various data generating techniques, including
Survey design
Carrying out small scale surveys for practice
Web scraping
ii. how to use statistical software appropriately to generate data to answer research questions
iii. how to use and contextualise the generated data to test broader scientific theories, and to communicate the findings to academic and lay audiences.
iv. storage and management of different types of data projects.
22 Contact hours
128 Hours of private study
Total hours for the module: 150
100% coursework
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
Munzert, S., Rubba, C., Meißner, P. and Nyhuis, D. 2014. Automated data collection with R: A practical guide to web scraping and text mining. John Wiley & Sons.
Zhao Y. 2012. R and Data Mining. Examples and Case Studies. Elsevier Academic Press, Waltham, MA.?
Tumasjan A, Sprenger TO, Sandner PG, and Welpe IM. 2011. Election forecasts with twitter. How 140 characters reflect the political landscape. Social Science Computer Review 29(4), 402–418.?
Rea, L.M. and Parker, R.A., 2014. Designing and conducting survey research: A comprehensive guide. John Wiley & Sons.
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1 demonstrate knowledge and critical understanding of how quantitative research informs our understanding;
2 demonstrate a systematic understanding of the core problems and concepts of generating data;
3 critically evaluate the different techniques covered in the module, and select the appropriate method based on the research question under study;
4 demonstrate an ability to store and manage datasets as part of a larger project workflow;
5 demonstrate an ability to carry out their own (small) surveys (either as observational method or for experiments), but also understand the limitations and problems of this approach;
6 demonstrate an ability to carry out their own (small) web scraping projects, but also understand the limitations and problems of this approach;
7 communicate the ideas, and problems of the approaches used to both specialist and non-specialist audiences, and use the generated data in their own data analysis.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 make effective use of IT facilities for solving problems;
2 communicate straightforward arguments and conclusions reasonably accurately and clearly;
3 manage their own learning and development.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.
