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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11053)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis semester long course is taught using a combination of lab and lecture sessions and is suitable for students following Masters programmes in Psychology and Linguistics It takes students from introduction to basic statistics and an introduction to the basics of the R programme, to competence in the standard univariate methodology and analysis using R.
Course description R is a language and environment for statistical computing and graphics that is highly flexible and increasingly popular for statistical analysis. It provides a wide variety of statistical and graphical techniques, including facilities to produce well-designed publication-quality plots.

Design and analysis are taught under a unifying framework which shows a) how research problems and design should inform which statistical method to use and b) that all statistical methods are special cases of a more general model. The course concentrates on research designs and analysis for problems in which there is a single outcome variable and would be taught using the general linear model as a framework to design and analysis.

The course is co-taught between Dr Tom Booth and Dr Martin Corley.

Typical Syllabus:
* Introduction to the use of statistical methods in research.
* Introduction to R for statistics
* Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares.
* Linear regression: including diagnostics, transformation, different families of models.
* Multiple regression: extending linear regression to multiple IVs and including interactions, effects coding.
* The general linear model (GLM) as an inclusive framework (including ANOVA, ANCOVA, mixed designs).
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  131
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 22, Supervised Practical/Workshop/Studio Hours 20, Feedback/Feedforward Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 24 )
Assessment (Further Info) Written Exam 0 %, Coursework 0 %, Practical Exam 100 %
Additional Information (Assessment) End of course assignment: a data analysis exercise (take home exam) 100%

Assignment deadline: Monday 18th January 2016, 12 noon
Page limit: 6 pages for the write-up (4 pages of text and 2 pages of tables/figures). The report should be written in a standard font, size 12, with standard 1 inch (2.54cm) margins on all sides. There is no limit for the R-code which will be submitted alongside the report.
Return Date: 9th February 2016

Throughout the course a series of homework exercises will be set. These do not form part of the final grade but we strongly advised that these are completed as part of progress monitoring.
Feedback The lab practicals will provide direct feedback on exercises and queries. Q&A sessions held once a week with course Teaching Assistant's. Model answers will be provided for all lab and homework exercises.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Students should know which statistical analyses are appropriate to the research design of particular studies
  2. Understand how a common framework unifies seemingly disparate data analysis methods
  3. Use the R statistical package to analyze real data, be able to interpret the outputs, and create any required graphs
Reading List
The course textbook is Learning Statistics with R (version 0.4), by Dan Navarro. This book is available to download for free (or you can purchase a printed copy).
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Martin Corley
Tel: (0131 6)50 6682
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
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