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DRPS : Course Catalogue : Edinburgh College of Art : Architecture and Landscape Architecture

Postgraduate Course: Data Acquisition and Analysis for Person-Environment Studies (ARCH11277)

Course Outline
SchoolEdinburgh College of Art CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryData Acquisition and Analysis for Person-Environment Studies provides a systematic and critical introduction to a range of specific research techniques for acquiring and analysing both quantitative and qualitative data used across different disciplines in a context where the research concerns the interactions of a person and their environment, particularly in the context of their health and wellbeing. It is aimed at students with little or no previous experience with such methods for data acquisition and analysis. Students will have the opportunity to explore a number of techniques and to apply them to their own interests and research plans using 'real' data.
Course description Data Acquisition and Analysis for Person-Environment Studies aims to introduce a limited and focussed set of data acquisition and analysis techniques for quantitative, qualitative, and mixed methods research. This course focusses mainly on the person-environment context, and involves examples and case studies from a range of disciplines involved in such research.

It aims to introduce a number of key techniques, including behaviour observation, site assessment, quantitative interviews, qualitative interviews and the means of setting up and acquiring data, database construction and management, use of specific software for spatio-statistical, statistical and qualitative analysis. Issues of sample size, questionnaire design and application, GIS spatial analysis, data reliability and validity, among other important aspects related to research quality. Furthermore, it also introduces mixed methods research, which is increasing in popularity, and how to acquire and analyse data from different methods in a linked and integrated way, at least at the point of interpretation.

The course will allow students to put this knowledge into practice during workshops with key tools such as standardised and validated survey instruments as well as software programmes (QGIS, SOPARC, SPSS, R statistical software and NVivo) used by researchers in the field.

By the end of the course, students should feel confident in undertaking their own research project (likely to be their dissertation) and to criticise other researchers' projects from different disciplines in terms of how the research was conducted and analysed, the quality of the data, and the reliability and significance of the results. They should be able to undertake data analysis for both quantitative and qualitative data, and effectively communicate and present their findings.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  46
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Formative Assessment Hours 1, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 165 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative assessments:
Students will have to submit two summative assessments as part of this course. The first assessment counts for 30% of their total mark for the course and the second for 70% of their total mark.
- The first assessment, counting for 30% of their grade, covers identification of a research gap and appropriate methods and data to meet it. This links with second assignment. 1000 words. Due in week 7
Addresses LO1, LO2 and LO3
- The second assessment, submitted at the end of the course, counting for 70% of their grade, requires students to apply data analysis methods on 'real' data of their choice. 3000 words. Due two weeks after final teaching in week 11
Addresses LO1, LO2, LO3 and LO4

Formative assessments:
Students will submit abstracts for both their summative assessments in weeks 4 and 8, allowing for the provision of feedback before the assessment takes place. These should include information on what the student plans to do for their assessment.
Feedback Formative feedback will be given in class and followed up by written feedback based on abstracts of both assignments presented in one seminar.
Written feedback for both summative and formative assessments submitted will be provided to all students. This feedback will be released within 15 working days from submission.
Feedback regarding the weekly exercises will be given during tutorials/workshops. During these, students will have the opportunity to ask questions, clarifying any issues they might have.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Effectively devise but also criticise techniques for data acquisition in relation to specific research questions, strategies and methodologies.
  2. Demonstrate a critical awareness and understanding of the data acquisition and analysis techniques for quantitative, qualitative and mixed methods research.
  3. Make informed choices regarding what data to acquire and why, how to acquire it and how to analyse it effectively to address specific research questions.
  4. Effectively undertake data analysis for both quantitative and qualitative data using data analysis software and effectively communicate and present findings.
Reading List
Core Textbook:
Bryman, A., 2015. Social Research Methods. Oxford: Oxford University Press. Fielding J. and Gilbert N., 2006. Understanding Social Statistics, London: Sage. Elliot J. and Marsh C., 2008. Exploring Data, Cambridge: Polity.
Bazeley, P., and Kackson, K., 2019. Qualitative Data Analysis with NVivo, London: Sage
Miles, M., Huberman, A., and Saldana, J., 2014. Qualitative data analysis: A methods sourcebook. London: Sage
Additional Information
Graduate Attributes and Skills 1. Knowledge and Understanding
- To demonstrate knowledge regarding the different practical issues and practices in data acquisition and analysis.

2. Practice: Applied knowledge, skills and understanding
- To be able to use and apply range of data acquisition and analysis techniques associated with person-environment studies.

3. Generic cognitive skills:
- To be able to critically evaluate practical issues and practices in person-environment research through the critical evaluation of evidence.

4. Communication, ICT and numeracy skills:
- To be able to use a wide range of skills, techniques and software, appropriate for research practices.

5. Autonomy, accountability and working with others:
- To be able to exercise substantial autonomy and initiative in the identification and execution of their intended learning activities and be committed to continuous reflection, self-evaluation and self-improvement.

Additional attributes:
- To be intellectually curious and able to sustain intellectual interest.
- To seek and value open feedback to inform genuine self-awareness (e.g. during workshops and tutorials).
Special Arrangements No special arrangements, other than the maximum number of students taking the course being 45
KeywordsResearch Methods,Research Design,Data Acquisition,Data Analysis,Statistics,Qualitative,Quantitative
Course organiserDr Simon Bell
Tel: (0131 6)51 5828
Course secretaryMr Daniel Jackson
Tel: (0131 6)50 2309
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