Undergraduate Course: Research Methods and Statistics (PPLS08001)

Course Outline
School	School of Philosophy, Psychology and Language Sciences	College	College of Humanities and Social Science
Credit level (Normal year taken)	SCQF Level 8 (Year 2 Undergraduate)	Availability	Not available to visiting students
SCQF Credits	20	ECTS Credits	10
Summary	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.
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 studentżs 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 2017/18, 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 Resit		2:00

Learning Outcomes
On completion of this course, the student will be able to: Understand basic probability, the use of probability distributions, sampling and the fundamentals of hypothesis testing Understand, apply and interpret the different forms of t-tests and correlation coefficients. Understand the basics of the linear model with a single binary or continuous predictor. Understand the statistical assumptions of the above referenced statistical tests, and when each test should be used to answer different types of research questions. 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.