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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 starts with an introduction to basic statistics and the basics of the R programme, and will give student competence in the standard univariate methodology and analysis using R.

Due to resource constraints this course is not currently available to students outside PPLS, or auditors.
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.

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 2017/18, Available to all students (SV1) Quota:  170
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) Practical Exam (100%). Assignment instructions will be released in Week 10.
Feedback Formative feedback is provided throughout the course during discussions and guidance in practical sessions
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. determine which statistical analyses are appropriate to the research designs of particular studies
  2. understand how a common framework unifies seemingly disparate data analysis methods
  3. use the R statistical programming language to analyse real data and interpret the outputs
  4. reate any required graphs using R
Reading List
The course textbook is Learning Statistics with R (version 0.5), 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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