Advanced Research Methods and Analysis
| Module title | Advanced Research Methods and Analysis |
|---|---|
| Module code | BEMM216 |
| Academic year | 2023/4 |
| Credits | 15 |
| Module staff | Dr Justin Tumlinson (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 10 |
|---|
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.
Module aims - intentions of the module
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.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 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
On successfully completing the module you will be able to...
- 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
On successfully completing the module you will be able to...
- 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.
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
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Workshops | 22 | The lecture will involve a tutor-led but not dominated discussion of key methods to develop an in-depth understanding of each research method. |
| Online learning | 20 | Preparatory video lessons on topics regarding statistics and using software |
| Pre-reading of articles | 20 | Read all assigned articles prior to each session |
| Assessment preparation | 88 | Reading and writing related to the two assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Review of individual performance on exercises | Regular feedback in lecture | 1,2,3,4,5,6 | Verbal feedback to individual students and groups |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 0 | 0 | 100 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Assignment: statistical analysis and using SPSS or R | 50 | During week 6 (1.5 hours) | 1,2,3,4,5,6 | Written feedback |
| Assignment: statistical analysis and using qualitative software | 50 | During week 10 (1.5 hours) | 1,2,3,4,5,6 | Written feedback |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Assignment: statistical analysis and using SPSS | Assignment: statistical analysis and using SPSS | 1,2,3,4,5,6 | Within 6 weeks |
| Assignment: statistical analysis and using qualitative software | Assignment: statistical analysis and using qualitative software | 1,2,3,4,5,6 | Within 6 weeks |
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.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | none |
| Module co-requisites | none |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 25/02/2019 |
| Last revision date | 28/07/2022 |
