Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11053)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This 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 studensts 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.
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.
* 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)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Practical Exam (100%) deadline is Monday 23rd January 2017. Assignment instructions will be released in Week 10.
||Formative feedback is provided throughout the course during discussions and guidance in practical sessions
|No Exam Information
On completion of this course, the student will be able to:
- determine which statistical analyses are appropriate to the research designs of particular studies
- understand how a common framework unifies seemingly disparate data analysis methods
- use the R statistical programming language to analyse real data and interpret the outputs
- reate any required graphs using R
|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)|
|Graduate Attributes and Skills
|Course organiser||Dr Martin Corley
Tel: (0131 6)50 6682
|Course secretary||Ms Stephanie Fong
Tel: (0131 6)50 3961
© Copyright 2016 The University of Edinburgh - 3 February 2017 5:11 am