Research Methods and Professional Practice
🎯 Module Overview
This module enabled me to:
- Acquire the ability to study and reflect on key principles and methods in research based on the scientific method, applicable across multiple disciplines.
- Examine various research strategies and designs suitable for different project contexts.
- Develop research competencies, particularly in collecting and analysing diverse data types to design, conduct, and evaluate independent research critically.
- Take a reflective and independent approach to the learning process, enhancing professional and academic growth.
📚 Table of Contents
- Unit 1: Introduction to Research Methods — The Scientific Investigation and Ethics in Computing
- Unit 2: Research Questions, the Literature Review and the Research Proposal
- Unit 3: Methodology and Research Methods
- Unit 4: Case Studies, Focus Groups and Observations
- Unit 5: Interviews, Survey Methods, and Questionnaire Design
- Unit 6: Quantitative Methods — Descriptive and Inferential Statistics
- Unit 7: Inferential Statistics and Hypothesis Testing
- Unit 8: Data Analysis and Visualisation
- Unit 9: Validity and Generalisability in Research
- Unit 10: Research Writing
- Unit 11: Going Forward — Professional Development and Your e-Portfolio
- Unit 12: Project Management and Managing Risk
Unit 1: Introduction to Research Methods. The Scientific Investigation and Ethics in Computing
Key Concepts:
- The purpose and objectives of research (exploration, description, explanation)
- The scientific method: observation, hypothesis, reasoning, testing
- Deductive and inductive reasoning
- Ethics and professionalism in research
- The Menlo Report principles and BCS Code of Conduct
Reflections: This unit introduced the foundations of research methods and their importance in computing. I learned that research serves three main purposes, to explore, to describe, and to explain (QuestionPro, 2021). The scientific method, which involves observation, hypothesis, reasoning, and testing, remains the cornerstone of credible investigation (Anderson and Hepburn, 2020).
Through practical examples, I developed a clearer understanding of deductive and inductive reasoning. Deduction moves from theory to observation, whereas induction starts from observation and builds toward theory. Recognising when to apply each approach enhances both logical problem-solving and analytical thinking.
Ethics emerged as a vital theme in this unit. The Menlo Report outlines four principles — Respect, Beneficence, Justice, and Respect for Law and Public Interest — that guide ethical research (Finn and Shilton, 2023). These principles are directly relevant to computing, especially in the age of artificial intelligence and data-driven systems, where privacy, consent, and accountability are key (Deckard, 2023). Understanding and applying ethical frameworks, including the BCS Code of Conduct, ensures professionalism and integrity in research practice.
This unit laid the groundwork for ethical and methodical research design. It has reinforced my appreciation of the balance between innovation, responsibility, and professional accountability in the computing field.
Related Work:
- 📄 Unit 1 Forum Post - Codes of Ethics and Professional Conduct
- 📄 Reflective Activity 1 – Ethics in Computing in the age of Generative AI
Unit 2: Research Questions, the Literature Review and the Research Proposal
Key Concepts:
- Formulating and refining research questions
- Developing a research proposal
- Understanding and conducting a literature review
- Evaluating existing research to identify gaps and opportunities
Reflections: This week’s unit focused on developing effective research questions and understanding how these form the foundation of a strong research proposal. I learned that defining a research topic can be a challenging and iterative process, often requiring multiple revisions to achieve clarity and focus (Golash-Boza, 2011). However, this stage is crucial for ensuring that the research remains purposeful and achievable.
A key takeaway was that a well-crafted research question drives every aspect of a project, from methodology to analysis, by narrowing the scope of investigation and providing a clear direction. The unit also introduced the structure and purpose of a research proposal, which acts as both a planning document and a rationale for why the research should be undertaken.
Another important element discussed was the literature review. This process involves surveying academic sources to identify existing theories, frameworks, and knowledge gaps within a chosen area of study. Conducting a literature review not only strengthens one’s understanding of the topic but also helps position new research within the broader academic conversation. I found that it also supports critical thinking, as it encourages comparing and evaluating diverse viewpoints rather than merely summarising them.
Overall, this unit provided me with the essential tools to conceptualise a research topic, transform ideas into well-defined questions, and justify a study through structured academic review. These skills are fundamental to producing a high-quality postgraduate research project.
Related Work:
Unit 3: Methodology and Research Methods
Key Concepts:
- Research design and philosophical assumptions (ontology, epistemology, axiology)
- Exploratory vs. descriptive research designs
- Qualitative, quantitative, and mixed methods research
- Primary and secondary data collection
- Data collection tools: surveys, interviews, case studies, observations
Reflections: This unit emphasised that methodology choices reflect underlying philosophical assumptions about reality, knowledge, and values. Understanding the difference between exploratory and descriptive research designs clarified when each approach is appropriate based on problem definition. Learning about qualitative, quantitative, and mixed methods revealed how inductive and deductive approaches serve different research purposes. Qualitative for exploring “how” and “why,” quantitative for answering “how many” and “to what extent”.
Related Work:
Unit 4: Case Studies, Focus Groups and Observations
Key Concepts:
- Case studies: in-depth research for hypothesis generation, limited generalisability
- Focus groups: 6-10 participants with common backgrounds exploring “why,” “what,” and “how”
- Quantitative observation: collecting numerical data through structured methods
- Qualitative observation: monitoring natural behaviors and characteristics
- Multiple methods can be combined for comprehensive data collection
Reflections: This unit explored data collection methods used primarily for qualitative research but applicable to quantitative approaches. Case studies provide deep insights but have limitations in establishing causality and generalisability due to potential bias. Focus groups reveal group dynamics that individual methods cannot capture, requiring careful participant selection with common backgrounds. Understanding the distinction between quantitative observation (numerical, structured) and qualitative observation (contextual, natural) clarified how observation serves different research purposes. The flexibility to combine multiple methods reinforces the importance of selecting appropriate tools based on research questions.
Related Work:
Unit 5: Interviews, Survey Methods, and Questionnaire Design
Key Concepts:
- Interviews: conversational qualitative method providing in-depth details from individual respondents
- Survey vs. questionnaire: surveys are comprehensive data gathering methods; questionnaires are sets of questions used within surveys
- Open vs. closed questions: closed questions for quantitative data, open questions for qualitative insights
- Pre-testing and post-testing: “before-and-after” methods to evaluate process or system implementation
- Survey distribution methods: online, phone, or face-to-face approaches
Reflections: This unit clarified the distinction between interviews, surveys, and questionnaires—terms often used interchangeably but serving different purposes. Interviews provide qualitative depth through conversation, while surveys offer quantitative breadth through structured questions. Understanding that closed questions generate numerical data for statistical analysis while open questions capture detailed experiences emphasised the importance of aligning question design with research objectives. The cyclic survey process—from questionnaire design through distribution and analytics—highlighted the need for careful planning at each stage.
Related Work:
Unit 6: Quantitative Methods - Descriptive and Inferential Statistics
Key Concepts:
- Quantitative methods: examining relationships between numerically measured variables
- Data sets: organised tables with columns (variables) and rows (observations)
- Descriptive statistics: summarising data through measures of central tendency and dispersion
- Measures of location: mean, median, mode representing typical observations
- Measures of dispersion: range, variance, standard deviation showing data spread
Reflections: This unit introduced the foundation of quantitative analysis, emphasising that meaningful insights require appropriate methodology for the level of measurement. Descriptive statistics provide tools to summarise and describe data through graphical representations and numerical measures. Understanding measures of location (central tendency) helps identify typical values, while measures of dispersion reveal data variability—both essential for comprehensive data understanding. The distinction between descriptive statistics (summarising known data) and inferential statistics (making predictions about populations) clarified their complementary roles in research.
Related Work:
Unit 7: Inferential Statistics and Hypothesis Testing
Key Concepts:
- Inferential statistics: making inferences about populations from sample data
- Statistical inference: extracting meaningful information from data with quantified uncertainty using probability
- Hypothesis testing: using data to test assumptions about population values
- Null and alternative hypotheses: null hypothesis assumes no effect; alternative proposes a specific relationship
- Type I and Type II errors: false positives and false negatives in hypothesis testing
Reflections: This unit built on descriptive statistics by introducing inferential methods that allow researchers to draw conclusions about populations from sample data. Understanding that all inferences carry inherent uncertainty quantified through probability emphasised the importance of rigorous statistical methods. Hypothesis testing provides a formal framework for comparing populations or assessing relationships, using statistical tests to determine whether evidence supports rejecting the null hypothesis. Learning about Type I errors (false positives) and Type II errors (false negatives) highlighted the balance between significance levels and statistical power in research design.
Related Work:
- 📄 Literatur Review - E-Learning - Grade 80% (Distinction)
- 🌐 Hypothesis Testing and Summary Measures
- 📄 Unit 7 Forum Post - Accuracy of information
Unit 8: Data Analysis and Visualisation
Key Concepts:
- Qualitative data analysis: coding and categorising non-numerical data like interviews, observations, and images
- Qualitative analysis methods: content analysis, thematic analysis, narrative analysis, grounded theory, discourse analysis
- Quantitative vs. qualitative analysis: qualitative analysis begins during data collection; quantitative has distinct preparation and analysis stages
- Data visualisation: using charts, graphs, and dashboards to present findings and communicate insights
Reflections: This unit highlighted fundamental differences between qualitative and quantitative data analysis approaches. Qualitative data is inherently open to interpretation, requiring coding to categorise responses into meaningful themes and patterns. Understanding methods like thematic analysis, content analysis, and grounded theory provided frameworks for systematically analysing non-numerical data while acknowledging researcher bias. The iterative nature of qualitative analysis—beginning during data collection rather than after—contrasts with quantitative methods’ sequential structure. Data visualisation emerged as crucial for transforming both data types into accessible insights through charts, graphs, and dashboards that connect KPIs and metrics for business intelligence.
Related Work:
Unit 9: Validity and Generalisability in Research
Key Concepts:
- Reliability: consistency of the measurement process
- Validity: accuracy of the measurement in targeting the correct concept
- Generalisability: extent to which findings apply to broader populations
- Reliability as a prerequisite for validity
- Data cleansing and validation processes in quantitative research
Reflections: This unit clarified the critical distinction between reliability and validity, concepts often confused but fundamentally different. Reliability ensures consistency in measurement, while validity ensures the measurement actually targets what it claims to measuring. The insight that a process must be reliable before it can even be assessed for validity was particularly valuable. The unit also emphasised that data analysis and presentation methods must be determined before data collection begins. This proactive approach ensures that the data collected is actually capable of answering the research question and that necessary steps like data cleansing are planned for.
Related Work:
Unit 10: Research Writing
Key Concepts:
- Research writing as a skill for communicating technical knowledge
- The structure and sections of a dissertation
- Writing research proposals by synthesising previous work
- Preparing research for peer-reviewed journals and books
- Bridging technical work and effective communication
Reflections: This unit focused on research writing as a practical skill for communicating technical knowledge. The key insight was learning how to structure a large document like a dissertation, which brings together all previous work (from topic selection and literature review to methodology) into a cohesive research proposal. This process highlighted that clear writing is essential not only for academic success but also for presenting proposals and papers in professional and peer reviewed contexts.
Related Work:
- 📹 Research Proposal - Presentation - Grade 67% (Merit)
Unit 11: Going Forward: Professional Development and Your e-Portfolio
Key Concepts:
- e-Portfolio as a tool for student and professional development
- Continuous Professional Development (CPD) and industry certifications
- Professional Skills Matrix for assessing goals and outcomes
- The Learning Loop as a holistic review and feedback tool
- Reflection on learning processes and professional practice
Reflections: This unit emphasised the value of the e-Portfolio not just as an assessment tool but as a lifelong asset for professional growth. Reviewing the Professional Skills Matrix helped identify gaps between current competencies and future career goals, directly informing a concrete action plan for Continuous Professional Development. The concept of the Learning Loop provided a structured way to synthesise the entire degree programme, linking academic achievements with practical industry requirements. This culmination of professional practice discussions reinforced that recording and reflecting on processes is as vital as the outcomes themselves.
Related Work:
Unit 12: Project Management and Managing Risk
Key Concepts:
- Project management methodologies and life cycle phases
- Risk management planning including identification and mitigation strategies
- Change control processes to manage scope, cost and quality
- Performance metrics for monitoring project success
- Relationship between risks, assumptions, constraints, threats and opportunities
Reflections: This unit highlighted that effective project management requires adapting methodologies to specific project environments rather than applying a single universal approach. The inevitable nature of risk means that successful delivery relies on proactive identification and management strategies rather than attempting to eliminate risk entirely. Understanding the formal change control process revealed how to handle necessary adjustments without compromising project quality or budget. These skills in managing uncertainty and performance metrics are essential preparation for the individual capstone project and professional practice in computing.
Related Work:
- 📄 End of Module - Reflective Piece - Grade 71% (Distinction)
🏁 Summary of Achievements
✅ Completed module units demonstrating an understanding of cloud principles
💬 Contributed actively to student forums
🧠 Gained practical and theoretical understanding of cloud architecture, security, and emerging technologies
🗂 Built and maintained a structured ePortfolio with critical reflections and artefacts
📚 References
Anderson, H. and Hepburn, B. (2020) ‘Scientific Method’, in Zalta, E. (ed.) The Stanford Encyclopedia of Philosophy (Winter 2020 edition). Metaphysics Research Lab, Stanford University.
Golash-Boza, T. (2011) Writing a Literature Review: Six Steps to Get You from Start to Finish. Available at: https://getalifephd.blogspot.com/2011/10/writing-literature-review-six-steps-to.html (Accessed: 27 October 2025).
Deckard, R. (2023) What Are Ethics in AI. Available at: https://www.bcs.org/articles-opinion-and-research/what-are-ethics-in-ai/ (Accessed: 27 October 2025)
Finn, M. and Shilton, K. (2023) ‘Ethics governance development: The case of the Menlo Report’, Social Studies of Science, 53(3), pp. 315–340. Available at: https://doi.org/10.1177/03063127231151708
QuestionPro (2021) What is research? Available at: https://www.questionpro.com/ (Accessed: 27 October 2025).