Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11053)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Humanities and Social Science
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Psychology
||Other subject area||None
||Please use Learn
||Taught in Gaelic?||No
|Course description||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 takes students from introduction to basic statistics and an introduction to the basics of the R package, to competence in the standard univariate methodology and analysis using R.
R is a language and environment for statistical computing and graphics, based on the S language. R is a flexible and increasingly popular package 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.
* Methods of science and refresher on descriptive
* Introduction to R statistics
* Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares, linear regression
* Introduction to data analysis using R
* Multiple regression
* Multiple regression analysis using R
* The general linear model (GLM) including continuous DV models, including ANOVA, ANCOVA, mixed designs, Defries-Fulker analysis
* General linear models with continuous DVs in R
- the lab practicals are formative.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||No
Course Delivery Information
|Delivery period: 2013/14 Semester 1, Available to all students (SV1)
||Learn enabled: Yes
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 5,
Feedback/Feedforward Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
|No Exam Information
Summary of Intended Learning Outcomes
|- Students should know which statistical analyses are appropriate to the research design of particular studies.
- Understand how a common framework unifies seemingly disparate data analysis methods.
- Use the R statistical package to analyze real data, be able to interpret the outputs, and create any required graphs.
|End of course assignment: a data analysis exercise (take home exam) 70%|
Assignment deadline: Monday 20th January 2014 by 12 noon
Page limit: 6 pages for the write-up (4 pages of text and 2 pages of tables/figures). There is no limit for the code.
We consider the completion of lab work sheets and take home mini assignments to be an essential preparation for the exam. All components must be submitted in order to pass the course. 30%
Monday 7th October, 10.00 am
Monday 14th October, 12 noon
Monday 21st October, 12 noon
Monday 28th October, 12 noon
Monday 4th November, 12 noon
Monday 11th November, 12 noon
Monday 18th November, 12 noon
Monday 25th November, 12 noon
|Course organiser||Dr Adam Moore
Tel: (0131 6)50 3369
|Course secretary||Miss Toni Noble
Tel: (0131 6)51 3188
© Copyright 2013 The University of Edinburgh - 13 January 2014 5:02 am