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Advanced Research Methods and Analysis

Module description

This unit builds on the research methods courses (both quantitative and qualitative) by exposing students to more advanced methodological and statistical skills needed to understand journal articles and conduct one’s own research at a masters level. This unit aims to familiarise students with some of the main quantitative and qualitative research methods and statistical analysis techniques in order for the students to be able to read output presented in journal articles, be able to generate results using SPSS or other statistical packages such as R and correctly interpret the results.

Full module specification

Module title:Advanced Research Methods and Analysis
Module code:BEMM216
Module level:M
Academic year:2023/4
Module lecturers:
  • Dr Justin Tumlinson - Convenor
  • Dr Greg Molecke - Convenor
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 2:


Module aims

The aim of this module is to introduce students to advanced quantitative and qualitative research methods such as multiple regression analysis, structural equation modelling, multilevel analysis, longitudinal analysis, social network analysis, and text analysis. 

ILO: Module-specific skills

  • 1. Demonstrate an advanced understanding of qualitative research methods avaliable for management research
  • 2. Demonstrate an advanced understanding of quantitative research methods avaliable for management research

ILO: Discipline-specific skills

  • 3. Identify which statistical analysis to use for a specific research question.
  • 4. Critically evaluate statistical methods used in top management journals and judge to what extend the conclusions drawn are valid

ILO: Personal and key skills

  • 5. Independently master statistical software in order to analyse qualitative and quantitative data and draw conclusions.
  • 6. Demonstrate the appreciation of the limitations of specific methodological approaches for own research.

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
Workshops22The lecture will involve a tutor-led but not dominated discussion of key methods to develop an in-depth understanding of each research method.
Online learning20Preparatory video lessons on topics regarding statistics and using software
Pre-reading of articles20Read all assigned articles prior to each session
Assessment preparation88Reading and writing related to the two assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Review of individual performance on exercisesRegular feedback in lecture1,2,3,4,5,6Verbal feedback to individual students and groups

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
Assignment: statistical analysis and using SPSS or R50During week 6 (1.5 hours)1,2,3,4,5,6Written feedback
Assignment: statistical analysis and using qualitative software50During week 10 (1.5 hours)1,2,3,4,5,6Written feedback

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Assignment: statistical analysis and using SPSSAssignment: statistical analysis and using SPSS1,2,3,4,5,6Within 6 weeks
Assignment: statistical analysis and using qualitative softwareAssignment: statistical analysis and using qualitative software1,2,3,4,5,6Within 6 weeks

Syllabus plan

Sessions will include specific focus on research methods such as

Multiple regression analysis

Structural equation modelling

Multilevel analysis

Longitudinal analysis

social network analysis

Interpretation of Semi-structured interviews

Text analysis

Focus groups

Each session will include practical examples and opportunities to run analyses and ask questions. This will involve dealing with how to run the models with specialised software (SPSS or other software such as R) to ensure that students are able to run the models

Indicative learning resources - Basic reading

Basic reading: Weekly reading provided by lecturer (academic journal articles) 

Indicative articles:

Shaun McQuitty & Marco Wolf (2013) Structural Equation Modeling: A Practical Introduction, Journal of African Business, 14:1, 58-69.

Krotov, V., A Quick Introduction to R and RStudio, unpublished manuscript.

Hox, J. J., & Bechger, T. M. (1998). An introduction to structural equation modeling.Family Science Review, 11, 354-373.

Sullivan, L. M., Dukes, K. A., & Losina, E. (1999). An introduction to hierarchical linear modelling. Statistics in medicine, 18(7), 855-888.

Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications.  

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