Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.
The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).
Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.
Course planning information
Analysis in this course is supported primarily by SAS software. Access to a Windows PC is required for analysis of data.
Students must attend block courses.
The courses listed above have similar content to this one meaning you can only enrol in this course or one of the listed courses. Only one of the courses can be credited towards your qualification.
General progression requirementsYou may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.
What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.
- 1 Explain the properties of multivariate data and distinguish a multivariate analysis from a univariate analysis in a research setting.
- 2 Explain the problems and opportunities that big data bring to standard analytical methods.
- 3 Explain and demonstrate how to use methods of cluster analysis and ordination of multivariate data, including principal components analysis (PCA), factor analysis, or multi-dimensional scaling (MDS), by virtue of their conceptual properties, and using statistical software.
- 4 Explain and demonstrate how to perform latent variable analyses using statistical software.
- 5 Perform analyses using statistical software to discriminate a priori groups using multivariate data, and to assess classification error of such models.
- 6 Apply and interpret appropriate cluster analysis, ordination methods and/or latent variable methods to analyse new multivariate data and write up the results in a structured, statistically sound research report.
Learning outcomes can change before the start of the semester you are studying the course in.
|Assessment||Learning outcomes assessed||Weighting|
|Computer programmes||1 2||10%|
|Written Assignment||1 2||15%|
|Computer programmes||3 4 5||10%|
|Written Assignment||3 4 5||15%|
|Written Assignment||1 2 3 4 5 6||50%|
Assessment weightings can change up to the start of the semester the course is delivered in.
You may need to take more assessments depending on where, how, and when you choose to take this course.
Explanation of assessment types
- Computer programmes
- Computer animation and screening, design, programming, models and other computer work.
- Creative compositions
- Animations, films, models, textiles, websites, and other compositions.
- Exam College or GRS-based (not centrally scheduled)
- An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
- Exam (centrally scheduled)
- An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
- Oral or performance or presentation
- Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
- You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
- Creative, learning, online, narrative, photographic, written, and other portfolios.
- Practical or placement
- Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
- Technology-based or experience-based simulations.
- Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
- Written assignment
- Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.