Quantitative and Qualitative Methods (C8300)

15 credits, Level 5

Spring teaching

This module complements the term 1 module 'Discovering Statistics'. It has three components: (a) advanced statistical analysis for various linear models and questionnaire construction and interpretation; (b) observational methods; (c) qualitative data-gathering and analysis. There is one assessed empirical report using some of these techniques, which will enable students to develop skills in research design, data-gathering and analysis and which will therefore equip students for their empirical research projects.

There is also an assessed problem set that gives students the opportunity to develop their critical thinking around validity and operationalisation. The statistical analysis parts of the module build upon existing knowledge of statistical theory (in term 1 module). Practical workshops complement the lectures by providing hands-on experience and guidance in using the methods and through small group work on relevant tasks, and encourage consistent development of statistical skills and critical thinking through weekly quizzes.

This module builds on knowledge gained in the core psychology modules C8511: Psychology as a science; C8891: Analysing data; and C8552 Discovering Statistics. Students who are not enrolled on the BSc Psychology course at Sussex are expected to be familiar with the material covered in these modules.

Teaching

33%: Lecture
67%: Practical (Laboratory)

Assessment

60%: Coursework (Computer-based examination, Problem Set, Professional log, Report)
40%: Examination (Unseen examination)

Contact hours and workload

This module is 150 hours of work. This breaks down into 33 hours of contact time and 117 hours of independent study.

This module is running in the academic year 2019/20. We also plan to offer it in future academic years. It may become unavailable due to staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of such changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses: