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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

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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 Arts, Humanities and Social Sciences
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 instruction 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 R, and will give students 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 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.

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 generalized linear model (GLM).
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 22, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 54 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Quizzes 20%
Written Assessment 80%
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. create any required graphs using R
Reading List
The course textbook is Learning Statistics with R (version 0.6), by Danielle Navarro. This book is available to download for free (or you can purchase a printed copy).
Additional Information
Graduate Attributes and Skills Not entered
Keywordsr,statistics
Contacts
Course organiserDr Martin Corley
Tel: (0131 6)50 6682
Email: Martin.Corley@ed.ac.uk
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk
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