Course Catalogue

Applied Data Analysis MGT5513

  • Academic Session: 2026-27
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

Building on the foundational skills of Semester 1, this course focuses on the advanced application of specialist analysis techniques. Students will gain hands-on experience in both quantitative inferential statistics and qualitative analytic methods (such as narrative or content analysis) using industry-standard software. The course emphasizes the interpretation of complex datasets and the communication of findings to diverse audiences, fostering the disciplinary depth required for global competitiveness in management research.

Timetable

10 x 1 hour tutorials

10 x 1 hour practical classes and workshops

Requirements of Entry

Students must be registered on one of the associated programmes listed in this course specification.

Excluded Courses

None

Co-requisites

None

Assessment

1. Written assignment, including essay; Individual; 1400 words; 40%; ILOs 1,4.

2. Practical skills assessment; Individual; 1000 words; 30%; ILOs 1,3.

3. Set exercise; Individual; 1000 words; 30%; ILOs 1-2.

Course Aims

This course aims to:

 

■ Develop students' ability to apply analysis techniques to management research problems.

 

■ Enhance practical competence in industry‑standard analytical software for both numerical and textual data analysis.

 

■ Strengthen students' ability to communicate analytical findings effectively to academic and non‑specialist audiences.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

 

1. Evaluate different approaches to analysing data, including both qualitative and quantitative methods.

 

2. Apply appropriate inferential statistical tests (e.g., ANOVA, correlation, regression) to address management research hypotheses.

 

3. Execute qualitative analytic methods for textual, aural, or visual data using computer-assisted data analysis software (e.g., NVivo or MAXQDA).

 

4. Communicate complex research findings effectively to non-specialist stakeholders such as industry and/or policy audiences.

Minimum Requirement for Award of Credits

No exceptions