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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Analysing Qualitative Data (PGSP11110)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis practical and hands-on course provides an introduction to the main issues involved in the management and analysis of qualitative data and the use of Computer Assisted Qualitative Data Analysis software (CAQDAS).

The course is made up of lectures, workshops and lab-based classes. Students are expected to work through exercises and readings in their own time.

The course is designed to complement the existing core research skills courses, to provide a foundation for students wishing to pursue intermediate and advanced qualitative skills training and to provide students with an introductory course in the management and analysis of qualitative data and the use of qualitative data analysis software.
Course description The aim of the first part of the course is to introduce students to qualitative data and the principles of data analysis, including data selection, transcription and data preparation.
Students will be expected to identify a suitable dataset of qualitative data which they will work on during the course. Staff will assist students to identify data suitable for the course requirements. Coding workshops will introduce students to the principles of coding in qualitative data analysis.

The second part of the course will introduce students to qualitative data analysis software. Online self-learning materials will be provided, supported by drop-in lab sessions with support from a tutor. Students will use software to analyse a small qualitative dataset, in discussion with staff.

The third part of the course will introduce students to different forms of qualitative data analysis. Students will develop their own approach to qualitative analysis.

This is a mix of lectures, workshops and lab-based classes. Students are expected to work through exercises and readings in their own time. Class discussions and exercises will enable students to reflect on their own approach to analysis and enable them to consider options for analysing data in future post-graduate or professional research.

The course is cross-disciplinary and open to students with backgrounds in social sciences, natural sciences and the
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  40
Course Start Semester 2
Course Start Date 16/01/2023
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework, in two parts.
Part A - A reflective coding exercise. All students will work from the same dataset and will conduct an initial coding of the data with reflections on the process (750 words) 20%
Part B ¿ A report on the process of analysing a small qualitative dataset (3250 words) 80%
Feedback Self-tests and practical exercises which students will work through during the course will build towards the final assessment.
Feedback on Part A of the assessment will enable students to develop their analysis skills for Part B.
Feedback on Part B will enable students to develop skills in analysing qualitative data for future Masters dissertations, PhD theses and professional research.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate knowledge of the principles of qualitative data analysis
  2. Demonstrate critical understanding of the main approaches to analysing qualitative data
  3. Demonstrate knowledge of the processes of qualitative analysis, including preparing data for analysis, managing data, carrying out analysis, writing and critical reflection
  4. Understand and demonstrate the principles of the use of computer software in carrying out qualitative data analysis
  5. Reflect critically on their own approach to analysis and options for analysing data in future post-graduate or professional research
Reading List
Bazeley, P., & Jackson, K. (2019). Qualitative data analysis with NVivo (3rd ed.).
Miles, M., Huberman, A., & Saldana, J. (2020). Qualitative data analysis : A methods sourcebook (4th ed.).
Richards, L. (2020). Handling qualitative data : A practical guide (4th ed.).
Saldana, J. (2021). The coding manual for qualitative researchers (4th ed.)
Additional Information
Graduate Attributes and Skills Application of critical analysis to complex information
Communicating complex information to cross-disciplinary audiences
IT skills in using qualitative data analysis software and its application
Additional Class Delivery Information The course consists of eight 2-hour lecture/seminars and two 2-hour lab-based sessions
This is combined with online delivery and directed learning of 180 hours

100% coursework, in two parts.
Part A - A reflective coding exercise. All students will work from the same dataset and will conduct an initial coding of the data with reflections on the process (750 words) 20%
Part B ¿ A report on the process of analysing a small qualitative dataset (3250 words) 80%

KeywordsQualitative Data Analysis,CAQDAS software
Contacts
Course organiserDr Jackie Gulland
Tel: (0131 6)50 8256
Email: Jackie.Gulland@ed.ac.uk
Course secretaryMrs Beth Richardson-Mills
Tel: (0131 6)51 1659
Email: brichar3@ed.ac.uk
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