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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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

Undergraduate Course: Research Methods & Statistics 3 (PSYL10127)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course will cover a number of quantitative topics and their relation to the generalized linear model as well as covering qualitative methodology. The first 5 weeks of the course will focus on topics of scale construction and data reduction. Emphasis will be placed on how and when these different models should be applied to psychological data and how to run such models using R. In the second 5 weeks, a variety of methodologies for qualitative analysis will be presented.
Course description This course is split into two halves covering quantitative methods for data reduction and scale development, and qualitative methodologies. In the quantitative aspect of the course, students will be introduced to a range of data reduction methods including cluster analysis, principal components analysis and factor analysis. We will discuss the use of these methods, factor analysis in particular, for scale development and discuss related issues of reliability and validity. In the qualitative section of the course, consideration will be given to differing approaches to psychological research, with a focus on qualitative study design and data, discursive analysis, and interpretative phenomenological analysis.
The course is taught via a mix of large group lectures, smaller group labs, problem sets and homework. Students will be encouraged to participate in group discussions in all aspects of the course, but are especially encouraged to make use of office hours. The 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: Research Methods and Statistics (PPLS08001) AND Research Methods and Statistics 2 (PSYL10126)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesThis course teaches using the R statistical package. If you have no experience with R, please contact the course organizer to arrange access to tutorial materials to be completed before the start of the course.
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Homework: 10%
Reports: 40% (2x 20%)
Exam: 50%
Feedback Weekly marked homework exercises delivered via LEARN.
Weekly office hours with lecturers.
Weekly problem sets with answers.
Online Q&A sessions.
Weekly lab.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Research Methods & Statistics 32:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand concepts in scale construction and assessment.
  2. Understand concepts of data reduction methods including interpretation, model assumptions and methods to assess them.
  3. Be able to run the above statistical tests in R and evaluate applications of these methods in published research.
  4. Understand the rationale underlying qualitative methodologies and know about various means of collecting and analyzing qualitative data.
  5. Be able to conduct qualitative analyses and evaluate applications of these methods in published research.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Sue Widdicombe
Tel: (0131 6)50 3411
Email: S.Widdicombe@ed.ac.uk
Course secretaryMiss Susan Richards
Tel: (0131 6)51 3733
Email: sue.richards@ed.ac.uk
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