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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2016/2017

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

Undergraduate Course: Research Methods and Statistics (PPLS08001)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course will provide an introduction to statistical methodology, with a focus on teaching the fundamental principles of probability and statistical inference. After providing this grounding, the course will move on to teach basic statistical procedures (chi-square, t-tests, correlation and simple linear models), whilst introducing students to the open source statistical program R.
Course description This course will provide an introduction to statistical methodology, with a focus on teaching the fundamental principles of probability and statistical inference. After providing this grounding, the course will move on to teach basic statistical procedures (chi-square, t-tests, correlation and simple linear models), whilst introducing students to the open source statistical program R.

This course provides a thorough grounding in the basics of probability and statistical data analysis for psychologists. Importantly, the course focusses on statistical methods as a tool to answering research questions. The course combines large group lectures, practical lab sessions and small group tutorials to provide both the theoretical background to statistical procedures, and the practical skills to run and interpret analyses.

The course will cover the basics of probability and probability distributions; fundamentals of statistical hypothesis testing; and core statistical tools including chi-square, t-tests, correlation and simple linear regression models. Practically, the course will develop student┐s ability to use Excel as a database manager, and introduce the basics of programming through the use of R statistical software. Thus, students will develop a suite of highly transferable knowledge and skills.

Students will be encouraged to participate in group discussions in all aspects of the course, but especially in seminar group tasks. A primary aim of these sessions is to begin to build the requisite skills for independent problem solving. Regular homework exercises will provide a means of tracking student development and be a source of regular formative feedback.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Psychology 1 (PSYL08001)
Co-requisites
Prohibited Combinations Other requirements This course is compulsory for students wishing to enter Honours in Psychology. Other interested students wishing to enrol on this course without having met the pre requisite, particularly those on the MA and BSc Cognitive Science degrees, can seek approval from the course organiser.
Course Delivery Information
Academic year 2016/17, Not available to visiting students (SS1) Quota:  226
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 40, Supervised Practical/Workshop/Studio Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 136 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Homework: 25% - Average of the best 14 out of 18 homework quiz scores
Report: 25%
Exam: 50%
Feedback Weekly marked assessments across the 2 semesters.
Weekly office hours with lecturers.
Weekly online Q&A sessions.
Weekly lab sessions
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Resit Exam Diet (August)Research Methods and Statistics Resit2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand basic probability, the use of probability distributions, sampling and the fundamentals of hypothesis testing
  2. Understand, apply and interpret the different forms of t-tests and correlation coefficients.
  3. Understand the basics of the linear model with a single binary or continuous predictor.
  4. Understand the statistical assumptions of the above referenced statistical tests, and when each test should be used to answer different types of research questions.
  5. Develop practical analytic skills in excel (database management) and of the R statistical package to be able to describe and plot different types of data (e.g. categorical, continuous etc.), and to run and interpret the above statistical tests in R.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements Quota - 180 maximum
KeywordsNot entered
Contacts
Course organiserDr Alex Doumas
Tel: (0131 6)51 1328
Email: Alex.Doumas@ed.ac.uk
Course secretaryMiss Susan Richards
Tel: (0131 6)51 3733
Email: sue.richards@ed.ac.uk
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