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University of Exeter Business School

Research Methods I

Module titleResearch Methods I
Module codeBEEM136
Academic year2023/4
Module staff

Dr Damian Clarke ()

Duration: Term123
Duration: Weeks




Module description

This module provides an introduction to the techniques involved in data handling and analysis.


Module aims - intentions of the module

This module aims to provide a thorough introduction to the techniques involved in effective data handling, analysis and visualisation required to undertake PhD level quantitative research. 

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Use statistical tools and software packages
  • 2. Repeat steps required to work with large datasets
  • 3. Transform raw data into meaningful insight

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 4. Read and work with current economic data.
  • 5. Critically analyse and visualize economic data
  • 6. List data sources commonly used to analyse economic models

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 7. Apply quantitative skills and handle logical and structured problem analysis
  • 8. Apply inductive and deductive reasoning involving data
  • 9. Apply essential research skills

Syllabus plan

  • Fundamentals of programming
  • Handling and manipulating vectors and matrices
  • If-then conditional statements
  • For loops and other iterative operations
  • User defined functions and the ability to use built-in functions
  • Handling and manipulating strings
  • Tools to deal with databases and tables
  • Data visualisation

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activities22Lectures
Scheduled learning and teaching activities10Tutorials
Guided independent study118Reading, preparation for classes and assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Practice problemsVaries1-9Oral/Written

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Empirical project802500 words (10-12 sides of A4)1-9Oral/Written
Average of bi-weekly problem sets20Bi-weekly problem sets with at most 3 questions each1-9Oral/Written

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Empirical project (80%)Empirical project 80%1-9August examination period
Average of bi-weekly problem sets Single problem set 1-9August examination period

Re-assessment notes

*Deferral of an individual online test may result in an average being taken of tests that have been taken


Indicative learning resources - Basic reading

  • R Programming for Data Science, Peng RD (2020)
  • Introduction to Data Exploration and Analysis with R, Mahoney (2019)

Indicative learning resources - Web based and electronic resources


Indicative learning resources - Other resources


Key words search

Programming, Data 

Credit value15
Module ECTS


Module pre-requisites

Only available to MRes Economics PhD pathway

Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date