Module ASB-4601:
Research Methods

Module Facts

Run by Bangor Business School

15 Credits or 7.5 ECTS Credits

Semester 1

Organiser: Prof Kostas Nikolopoulos

Overall aims and purpose

To provide the student with knowledge of intermediate and advanced qualitative and quantitative research methods, and provide a basis in research methodology.

Course content

Describing and summarising data; Probability and probability distributions; Principles of statistical inference; Correlation and regression analysis; Regression models for panel data.

Assessment Criteria

threshold

No major omissions or inaccuracies in the deployment of information/skills; Some grasp of theoretical/conceptual/practical elements; Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives.

good

Much of the relevant information and skills mostly accurately deployed; Adequate grasp of theoretical/conceptual/practical elements; Fair integration of theory/practice/information in the pursuit of the assessed work's objectives; Some evidence of the use of creative and reflective skills. High Standard: Very good performance; Most of the relevant information accurately deployed; Good grasp of theoretical/conceptual/practical elements; Good integration of theory/practice/information in pursuit of the assessed work's objectives; Evidence of the use of creative and reflective skills.

excellent

An outstanding performance, exceptionally able; The relevant information accurately deployed; Excellent grasp of theoretical/conceptual/practice elements; Good integration of theory/practice/information in pursuit of the assessed work's objectives; Strong evidence of the use of creative and reflective skills.

Learning outcomes

  1. Demonstrate an understanding of basic statistical concepts, basic ideas of sampling and statistical inference.
  2. Demonstrate a good foundation in research methods and techniques that can be applied in writing reports.
  3. Obtain data from primary and secondary sources, and carry out investigations using summary statistics, graphs, techniques of exploratory data analysis, correlation and regression.
  4. Formulate feasible research questions and strategies; and assemble, select and present the results of data analysis and data modelling.
  5. Demonstrate computer skills for data analysis and modelling, and use statistical computer packages.

Assessment Methods

Type Name Description Weight
Assignment 40
2-hour written examination at the end of Semester 1 60

Teaching and Learning Strategy

Hours
  A weekly 2-hour lecture.  
  One 1-hour practical session per week.  

Pre- and Co-requisite Modules

Courses including this module

Compulsory in courses: