Module code: MANM169

Module Overview

The Research Methods module aims to help students to develop an understanding of the research process and to undertake research leading to successful completion of their dissertation. It enables students to conduct research independently and also provides them with the knowledge, skills and understanding required to develop their proposal for their dissertation. The module covers ideas, techniques, and methods relevant to different stages of the research process, stressing the interdependence of each stage in conducting effective, coherent and rigorous research. By covering the fundamentals of research methods and research methodologies, this module will enable students to conduct research independently and provide them with the knowledge and understanding needed to do a dissertation.

Module provider

Surrey Business School

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 113

Lecture Hours: 33

Laboratory Hours: 4

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content includes:

  • Introduction to research

  • Formulating research aims and objectives

  • Developing a conceptual framework

  • Developing a research proposal

  • Writing and presenting the research project

  • Critically reviewing the literature

  • Sampling strategies

  • Data collection methods

  • Measurement and scaling (questionnaires)

  • Analysing quantitative data

  • Obtaining secondary data

  • Data collection instruments (interviews and observation)

  • Analysing qualitative data

  • Negotiating access and ethical issues in research

Assessment pattern

Assessment type Unit of assessment Weighting

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to give the students the opportunity to demonstrate their breadth and depth of understanding of both the theory of research methods and its application to research problems. The assessment strategy has 3 elements (two formative and one summative). Only the summative assessment counts towards the student's final grade for the module:

Formative assessment:

  1. Weekly formative online multiple choice tests with immediate online personal explanatory feedback for selected answers (via SurreyLearn). 

  2. Mid semester (formative timed) online formative examination with multiple choice questions in the same format as the end of module examination, individual marks will be provided online, generic feedback, including a statistical breakdown of marks, being provided in the lecture and via SurreyLearn. 

Through the strategy outlined above students will already be familiar with the assessment process and have had ample opportunity to practice prior to the summative exam.  This will allow them to improve their performance.  Prior to the examination, students lecture time will be spent discussing the examination. 

Summative assessment:

       3. End of module summative multiple choice examination conducted under exam conditions. Some of the exam questions will test understanding of the stages of research and ability to distinguish between different research approaches, methods, and procedures. Additionally, the exam will include questions using one (or more) new case studies to test the students’ ability to apply their knowledge and anticipate issues associated with a particular research scenario, as well as questions related to quantitative/qualitative data analysis.

By combining the different types of questions, the examination will test all the learning outcomes of the module and it will equally cover different elements of the module.

Marking of the multiple choice examination will be undertaken electronically. The module leader will be responsible for second marking which will be undertaken to ensure there are no problems with the electronic marking. 

Marks will be returned to students within 3 weeks for the mid semester formative test, and after the exam board for the summative examination.

In addition students experience and discuss questions related to a range of research scenarios within the lectures.

Module aims

  • Introduce the fundamentals of the research process.
  • Enable students to effectively define their research problem
  • Highlight the main research paradigms and approaches that are relevant to research in management disciplines.
  • Provide students with an introduction to qualitative and quantitative research methods
  • Explain the differences between quantitative and qualitative data collection techniques and analysis procedures.
  • Provide students with hands-on experience with basic data collection and data analysis.
  • Stress the ethical aspects of research and how to embed them into research practice.

Learning outcomes

Attributes Developed
1 Understand the stages of the research process and demonstrate an ability to perform them. CPT
2 Identify the attributes of a good research topic and turn research ideas into research problems. CPT
3 Conduct a review of appropriate literature relevant to a stated research topic. CPT
4 Distinguish between main research approaches and epistemological positions KCPT
5 Understand a range of data collection tools in order to design an effective research method KCPT
6 Use quantitative and qualitative data analysis procedures to serve the purpose of a research project. KCPT
7 Anticipate ethical issues at each stage of the research process, and be aware of a range of strategies to deal with them. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The teaching and learning strategy for this module is designed to encourage students to think critically about the different stages of the research process and to engage in evaluating the different research methods and techniques.

  • The principal teaching and learning method is a three-hour weekly session including several elements the achieve to module learning outcomes:

    • Lectures delivered by the module tutors designed to develop an understanding of theory. 

    • Student-led class discussions of mini case studies and research scenarios to put theory into application and transform the acquired knowledge into practice.  

    • The use of the Electronic Voting System as appropriate to enhance proactive student engagement given the constraints of large class sizes.

  • Students are expected to support the work undertaken during each lecture by undertaking a number of independent learning activities:

    • Preparatory reading with questions to guide their note taking. 

    • Follow up exercises to consolidate their learning, where appropriate applying this to new situations. 

    • Multiple choice self-test questions to assess their achievement of each week's learning outcomes. 

    • Guided further reading to enable them to extend their knowledge and understanding.

  • To support student learning of statistics, computer lab tutorials will run, in which students will be divided into smaller groups and will be trained on the basics of conducting quantitative data analysis using SPSS. Students are expected to support their learning by practising in their own time using the exercises and data sets provided by the lecturers.

  • The University’s virtual learning environment (SurreyLearn) will be used to support student learning by providing them with additional resources and links to useful websites. SurreyLearn will also be used for further discussion of the module topics between the students and the lecturers as well as the students and their colleagues. Additionally, PowerPoint presentations and case studies used by the lecturers will be placed on SurreyLearn before the lecture so that students may print off copies in time for the lecture.

The ethos of this module is that students will learn best when they become active participants in the learning process and this is reflected in all elements of the module design. For example: 

  • Students will be expected to participate in lectures through electronic voting and undertaking a range of other tasks.

  • Students will be expected to actively engage in all statistics practical classes held in the computer labs.

  • Not all elements of the curricula will be covered in detail in the lecture programme, students will be expected to find things out for themselves.

  • Students will be expected to prepare for all lectures by undertaking the pre-reading.

  • In order to gain high marks in the exam, students will have to participate fully. In particular students should undertake the pre work and at least one follow up activity each week. They should also read beyond lecture notes and the recommended textbook. A list of specific reading is provided but the expectation is that this represents a starting point for reading and not an inclusive list. 

The weekly formative multiple choice tests are designed such that students who engage and participate will gain the most.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: MANM169

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Management (Entrepreneurship) MBus 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Business Management (HRM) MBus 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
International Business Management MBus 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Business and Retail Management MBus 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.