ADVANCED RESEARCH METHODS

Provisional outline syllabus:

• Data collection: Questionnaire design; Lab experiments; Lab-in-the-field experiments; Natural experiments; Scraping the web for data.

• Data analysis: Advanced Stata; Matlab; Julia; Python/Numpy; the UNIX shell; Regular Expressions and inexact text matching; GIS and spatial analysis; Quantitative text analysis; Big data and machine learning.

• Researcher and career development: Reading economics papers; Refereeing economics papers; Replicating economics papers; Writing and publishing economics papers; Career tracks: academic and private sector; Career tracks: government, international organisations and third sector.

Aims: This module is deliberately broad and shallow. It aims to map the landscape of practical tools and techniques for quantitative research, to help students identify the ones that may be of use to them and target their own exploration more effectively. For most of these tools, students can use online tutorials, guides and references to build deeper knowledge. In many cases there are staff members in a student’s department with the expertise to help if and when a student get stuck.

Teaching and learning methods: Teaching is blended, with sessions conducted simultaneously in-person and via Zoom. Sessions will often combine a short lecture with practical exercises.

Contact time: There will be a live 2-hour Zoom session from 1 – 3pm each Wednesday afternoon over the 22 weeks of the module.

Assessment: Students will be asked to review/referee a paper in the first semester, and to replicate a paper’s analysis in the second semester. These exercises are intended to be assessed by the student’s own supervisor. For students at Sussex, these assessments will feed into the upgrade decision.

Administration: Live sessions via Zoom.

Materials and slides on Canvas: https://canvas.sussex.ac.uk/courses/21402

Enrolment information: For University of Sussex students, this module is automatic and compulsory in the first year of registration (or part-time equivalent). For others: ask your PhD programme director to contact george.mackerron@sussex.ac.uk.

Maximum class size: There is a soft cap on this module of 30 students per year.

Step 1 of 2: Application Email. Send an email to the Instructor of this course and to the Academic Officer of the host university: names and contact links are reported above in the section 'Course Details' (right-click on the name to copy their email address, or left-click on the name to create a new email with your default software). Please send your application email using your university email account. Sample email:

To: [INSTRUCTOR]

Cc: [ACADEMIC OFFICER]

Object: Application to your online PhD course (ShOT/SEDOT)

Dear Prof [INSTRUCTOR],

My name is [STUDENT NAME], PhD student at [HOME UNIVERSITY]. I would like to attend online your PhD course in [COURSE TITLE], shared through the ShOT/SEDOT network. Please let me know if I can join the course, I look forward to hearing from you.

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