Research Methods and Professional Practice July 2025

Research Methods and Professional Practice

• 1-3 Collaborative Discussion 1: Codes of Ethics and Professional Conduct
• 1 Unit 1: Introduction to Research Methods - The Scientific Investigation and Ethics in Computing
• 2 Unit 2: Research Questions, the Literature Review and the Research Proposal
• 3 Unit 3: Methodology and Research Methods
• 4 Unit 4: Case Studies, Focus Groups and Observations
• 5 Unit 5: Interviews, Survey Methods, and Questionnaire Design
• 6 Unit 6: Quantitative Methods - Descriptive and Inferential Statistics
• 7-9 Collaborative Discussion 2: Case Study on Accuracy of Information
• 7 Unit 7: Inferential Statistics and Hypothesis Testing
• 8 Unit 8: Data Analysis and Visualisation
• 9 Unit 9: Validity and Generalisability in Research
• 10 Unit 10: Research Writing
• 11 Unit 11: Going Forward: Professional Development and Your e-Portfolio
• 12 Unit 12: Project Management and Managing Risk

Research Methods and Professional Practice Module

This section contains all my work for the Research Methods and Professional Practice module from July 2025. The module covers fundamental research methodologies, statistical analysis, professional ethics, and practical skills for conducting rigorous research in computing and related fields.

1-3

Collaborative Discussion 1: Codes of Ethics and Professional Conduct

Three-week discussion analyzing ACM case studies through professional ethical frameworks and comparing with BCS Code of Conduct

1-3.1 Key Concepts

• Professional Codes: ACM Code of Ethics and BCS Code of Conduct application
• Case Study Analysis: Real-world ethical scenarios from ACM examples
• Legal and Social Impact: Jurisdictional and professional implications
• Comparative Ethics: ACM versus BCS code frameworks

1-3.2 Learning Outcomes

• Appraise the professional, legal, social, cultural and ethical issues that affect computing professionals

1-3.3 Reflection Notes

The discussion examined AllTogether's accessibility failure case study, revealing tensions between delivery pressure and ethical compliance. Peer feedback emphasized integrating preventive measures into development processes, highlighting accessibility as fundamental professional competence (Gotterbarn et al., 2018). The analysis demonstrated how ACM and BCS codes address professional competence differently, with BCS emphasizing jurisdictional legal compliance while ACM focuses on broader public welfare principles (Wong, 2021).

References

1. Gotterbarn, D., Wolf, M.J. and Flick, C. (2018) 'ACM Code of Ethics', Communications of the ACM, 61(1), pp. 121–128
2. Wong, R.Y. (2021) 'Tactics of soft resistance in user experience professionals' values work', Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), pp. 1-28
3. Gautam, A. et al. (2024) 'Socially responsible computing in an introductory course', Proceedings of the 55th ACM Technical Symposium on Computer Science Education, pp. 373-379

Artifacts

• Initial Post and Peer Responses Ethical analysis of accessibility failures with peer feedback and professional code application

1

Unit 1: Introduction to Research Methods - The Scientific Investigation and Ethics in Computing

Scientific method, deductive/inductive reasoning, and ethical frameworks for computing research and professional practice

1.1 Key Concepts

• Scientific Method: Observation, hypothesis, reasoning and testing approaches
• Deductive Reasoning: Theory to observation to conclusion methodology
• Inductive Reasoning: Observation to analysis to theory development
• Research Ethics: Menlo Report principles (Respect, Beneficence, Justice, Law and Public Interest)
• Professional Conduct: BCS and ACM codes for computing professionals

1.2 Learning Outcomes

• Differentiate between inductive and deductive reasoning
• Understand why ethics are important and how they relate to research and professional practice

1.3 Reflection Notes

The scientific method's combination of observation, hypothesis, reasoning and testing varies significantly across disciplines, with deductive and inductive reasoning serving as core problem-solving approaches (Anderson & Hepburn, 2020). Deductive reasoning provides perfect conclusions when assumptions are correct, while inductive reasoning proves more practical for everyday problems involving partial information.

Research ethics in computing extends beyond traditional frameworks through the Menlo Report's four principles, particularly relevant in the generative AI age where additional moral considerations emerge (Finn & Shilton, 2023; Deckard, 2023). The distinction between ethics and morals becomes crucial for computing professionals navigating complex technological impacts on society.

References

1. Anderson, H. and Hepburn, B. (2020) 'Scientific Method', The Stanford Encyclopedia of Philosophy (Winter 2020 edition)
2. Finn, M. and Shilton, K. (2023) 'Ethics governance development: The case of the Menlo Report', Social Studies of Science, 53(3), pp. 315-340
3. Deckard, R. (2023) What are Ethics in AI
4. Dawson, C. (2015) Projects in Computing and Information Systems: A Student's Guide, Chapters 2
5. Correa, N. et al. (2023) Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance

Artifacts

• Reflective Activity 1: Ethics in Computing in the Age of Generative AI Global AI Ethics Landscape: Challenges and Recommendations

2

Unit 2: Research Questions, the Literature Review and the Research Proposal

Research question formulation, literature review methodology, and research proposal development

2.1 Key Concepts

• Formulating and revising research questions
• Research proposal structure and components
• Literature review process and presentation
• Critical evaluation of literature

2.2 Learning Outcomes

• Examine characteristics that make up suitable research topics
• Explore rational and creative methods for formulating research ideas
• Identify means of transforming research ideas into crafted research questions and proposals
• Conduct literature searches, critique literature and present literature reviews

2.3 Reflection Notes

The process of defining research topics proved "daunting" as Boza (2022) notes, requiring careful question formulation and revision. My chosen topic "Evaluating the Impact of Large Language Models on Authorised Push Payment Fraud Detection and Prevention in the UK" emerged from my financial crime background. The literature review process involved systematic searching and critical evaluation of scholarly sources to identify gaps in existing research (Dawson, 2015).

References

1. Dawson, C. (2015) Projects in Computing and Information Systems: A Student's Guide, Chapters 1-3, 5
2. Boza, T. (2022) How to Write a Literature Review: Six Steps to Get You from Start to Finish
3. Healey, M., Matthews, K. and Cook-Sather, A. (2020) Writing about learning and teaching in higher education, Chapter 14
4. Kuziemsky, C. and Lau, F. (2016) Handbook of eHealth Evaluation An Evidence-based Approach, Chapter 9

3

Unit 3: Methodology and Research Methods

Introduction to research methodology, design types, and data collection methods

3.1 Key Concepts

• Exploratory vs Descriptive research designs
• Qualitative, Quantitative, and Mixed Methods research
• Primary and Secondary research approaches
• Interview and survey design techniques

3.2 Learning Outcomes

• Understand the different research methods
• Know which data collection methods are related to each method of research
• Have some idea which of these would be suited to your area of research

3.3 Reflection Notes

The distinction between exploratory and descriptive research designs provided clarity for project planning. Understanding qualitative, quantitative, and mixed methods approaches helped determine appropriate data collection strategies. The lecturecast on interview and survey design proved particularly valuable, demonstrating how methodological choices impact research validity (Saunders et al., 2012).

References

1. Saunders, M., Lewis, P. and Thornhill, A. (2012) Research Methods for Business Students, 6th edn, Chapter 5
2. Dawson, C. (2015) Projects in Computing and Information Systems: A Student's Guide, Chapter 2
3. British Research Methodology (BRM) (2018) Research Design
4. Sage (2021) Sage Research Methods: Methods Map

Artifacts

• e-Portfolio Activity: Research Proposal Review Methodology selection and data collection planning for chosen research area

4

Unit 4: Case Studies, Focus Groups and Observations

Data collection methods for qualitative research including case studies, focus groups, and observation techniques

4.1 Key Concepts

• Case study methodology and applications
• Focus group design and facilitation
• Quantitative vs Qualitative observation methods
• Observer roles and participant interaction

4.2 Learning Outcomes

• Understand how to carry out each of these data collection methods
• Know which method would be suitable, if any, for your investigation
• Consider the type of data you would obtain

4.3 Reflection Notes

Learning about case studies, focus groups, and observation methods expanded my understanding of qualitative data collection. The distinction between quantitative observation (numerical values) and qualitative observation (monitoring characteristics) proved particularly useful. Understanding observer roles from complete observer to full participant highlighted the methodological considerations needed for valid research design (Dawson, 2015).

References

1. Dawson, C. (2015) Projects in Computing and Information Systems: A Student's Guide, Chapter 2
2. Priya, A. (2021) 'Case Study Methodology of Qualitative Research: Key Attributes and Navigating the Conundrums in Its Application', Sociological bulletin, 70(1), pp. 94–110
3. Kaplan, B. and Maxwell, J. A. Qualitative Research Methods for Evaluating Computer Information Systems
4. Wohlin, C. (2021) Case Study Research in Software Engineering, Information and Software Technology

5

Unit 5: Interviews, Survey Methods, and Questionnaire Design

Interview techniques, survey methodology, questionnaire design, and pre-/post-testing methods

5.1 Key Concepts

• Interview methods and techniques
• Survey vs questionnaire distinctions
• Questionnaire design process and question types
• Pre-testing and post-testing methods

5.2 Learning Outcomes

• Understand how interviews and surveys fit into research and whether either or both of these might be useful for your investigation
• Understand how pre- and post-testing can be used in research
• Design a questionnaire to obtain the responses you will need for your investigation
• Understand how the data obtained can be analysed

5.3 Reflection Notes

The questionnaire design process from deciding information required through pretesting proved invaluable for research planning. Analyzing inappropriate survey use cases like Cambridge Analytica highlighted ethical implications of data collection methods. Understanding the distinction between surveys and questionnaires, plus opening/closing question techniques, enhanced methodological awareness (Dawson, 2015).

References

1. Dawson, C. (2015) Projects in Computing and Information Systems: A Student's Guide, Chapter 2
2. Hoepfl, M. C. (1997) 'Choosing Qualitative Research: A Primer for Technology Education Researchers', Journal of Technology Education, 9(1), pp. 47-63
3. Allsop, D. B. et al. (2022) 'Qualitative Methods with Nvivo Software: A Practical Guide for Analyzing Qualitative Data', Psych, 4(2), pp. 142–159
4. Brace, I. (2013) Questionnaire Design: How to Plan, Structure and Write a Survey, Kogan Page

Artifacts

• Reflective Activity 2: Case Study: Inappropriate Use of Surveys Analysis of Cambridge Analytica, TikTok, and Amazon Ring cases examining ethical implications of data collection violations

6

Unit 6: Quantitative Methods - Descriptive and Inferential Statistics

Introduction to quantitative analysis, descriptive statistics, and measures of location and spread

6.1 Key Concepts

• Quantitative data and levels of measurement
• Descriptive statistics and data summarization
• Measures of location (central tendency)
• Measures of dispersion (spread and variability)

6.2 Learning Outcomes

• Understand and apply descriptive statistics
• Identify the different levels of measurement
• Produce measures of location and spread

6.3 Reflection Notes

Understanding quantitative methods proved fundamental for meaningful data analysis. Learning to distinguish between levels of measurement—nominal, ordinal, interval, and ratio—guides appropriate statistical technique selection. The progression from raw datasets to meaningful summary measures through descriptive statistics revealed how location and dispersion measures work together to characterize data distributions (Berenson et al., 2019).

References

1. Berenson, L., Levine, D., and Szabat, K. (2019) Basic Business Statistics: Concepts and Applications, 14th edn, Pearson, Chapters 2-3

7-9

Collaborative Discussion 2: Case Study on Accuracy of Information

Discussion on information accuracy, validation, and quality assurance

Artifacts

• Initial Post and Peer Responses Analysis of information accuracy challenges and solutions

7

Unit 7: Inferential Statistics and Hypothesis Testing

Inferential statistics, probability principles, and hypothesis testing for population inferences

7.1 Key Concepts

• Inferential statistics and population inferences
• Basic principles of probability
• Hypothesis testing methodology
• Statistical significance and p-values

7.2 Learning Outcomes

• Apply inferential statistics to data analysis
• Identify the correct probability distributions
• Perform appropriate hypothesis tests

7.3 Reflection Notes

Learning inferential statistics transformed abstract probability concepts into practical analytical tools. Understanding how to make valid population inferences from sample data proved essential for evidence-based decision-making. The progression from descriptive statistics to hypothesis testing using LibreOffice functions like `=TTEST()` and `=QUARTILE()` provided hands-on experience distinguishing statistical from practical significance (Berenson et al., 2019).

References

1. Berenson, L., Levine, D., and Szabat, K. (2019) Basic Business Statistics: Concepts and Applications, 14th edn, Pearson, Chapters 4, 9-10
2. Purdue University (2023) Basic Inferential Statistics: Theory and Application

Artifacts

• Hypothesis Testing Worksheet Conduct hypothesis tests to compare groups and validate statistical claims using LibreOffice functions
• Summary Measures Worksheet Calculate descriptive statistics using LibreOffice Calc for diet effectiveness and brand preference analysis
• Literature Review Evaluating the Impact of Large Language Models on Authorised Push Payment Fraud Detection and Prevention in the UK

8

Unit 8: Data Analysis and Visualisation

Statistical data analysis techniques and visualization methods for research communication

8.1 Key Concepts

• Descriptive Statistics: Summary measures for data characterization
• Statistical Inference: Making population inferences from sample data
• Data Visualization: Charts, graphs, and visual communication techniques
• LibreOffice Analysis: Statistical functions and data analysis tools

8.2 Learning Outcomes

• Apply descriptive and inferential statistical techniques
• Create effective data visualizations for research communication
• Interpret statistical results and their practical significance
• Use LibreOffice statistical functions for data analysis

8.3 Reflection Notes

This unit bridged the gap between theoretical statistics and practical application through data visualization and analysis. Creating confidence intervals and conducting power analysis revealed the importance of sample size planning in research design. The progression from basic descriptive statistics to complex inferential procedures using LibreOffice enhanced analytical capabilities for evidence-based decision-making in professional contexts.

References

1. Berenson, L., Levine, D., and Szabat, K. (2019) Basic Business Statistics: Concepts and Applications, 14th edn, Pearson
2. Saunders, M., Lewis, P. and Thornhill, A. (2023) Research Methods for Business Students, Chapter 12

9

Unit 9: Validity and Generalisability in Research

Validity, generalisability, and reliability considerations in research design and data presentation

9.1 Key Concepts

• Validity: Measurement targeting the correct construct
• Reliability: Consistency of measurement processes
• Generalisability: Applicability of findings to broader populations
• Data Cleansing: Validation processes before analysis
• Qualitative vs Quantitative: Analysis and presentation differences

9.2 Learning Outcomes

• Understand how validity, generalisability, and reliability affect research design
• Distinguish between qualitative and quantitative data analysis approaches
• Apply data cleansing and validation processes before analysis
• Consider how results enable answering research questions

9.3 Reflection Notes

I initially underestimated how reliability must be established before validity can even be assessed. Working through the concepts, I realized that my previous research approaches often jumped to validity concerns without ensuring measurement consistency first. The lecturecast clarified how qualitative data interpretation differs fundamentally from quantitative analysis - something I hadn't fully appreciated when designing my research proposal on RL explainability. This unit made me reconsider my data collection timeline, as Saunders et al. (2023) emphasize that cleansing and validation processes need planning before any data gathering begins.

References

1. Schneider, C. (2018) Making the case: A Qualitative Approach to Studying Social Media Documents, in Bryman, A. & Buchanan, D. (eds.) Unconventional Methodology in Organization and Management Research. Oxford Scholarship Online
2. Saunders, M., Lewis, P. and Thornhill, A. (2023) Research Methods for Business Students, 9th edn, Pearson, Chapter 11.4

Artifacts

• e-Portfolio Activity: Charts Worksheet Data visualization exercises with solutions for bar charts, histograms, and clustered columns

10

Unit 10: Research Writing

Research writing skills for dissertations, proposals, and technical communication

10.1 Key Concepts

• Research Writing: Technical communication for academic and professional contexts
• Dissertation Structure: Standard sections and organization principles
• Research Proposals: Structure and presentation of research plans
• Academic Publishing: Peer-reviewed journals and publication standards

10.2 Learning Outcomes

• Structure dissertations and research documents effectively
• Present research proposals professionally
• Apply academic writing conventions
• Communicate technical knowledge clearly

10.3 Reflection Notes

Research writing is essential for technical professionals, providing a structured approach to communicating complex ideas. This unit culminates previous work on literature reviews, research methods, and proposal development into a comprehensive presentation format.

References

1. Dawson, C. (2015) Projects in Computing and Information Systems
2. Heard, S. (2016) The Scientist's Guide to Writing
3. Academic writing and research presentation standards

Artifacts

• Research Proposal Presentation Interpretable Verifier-Reinforcement Learning for Financial Crime Detection via Sparse Autoencoders

11

Unit 11: Going Forward: Professional Development and Your e-Portfolio

Professional development planning based on e-Portfolio reflection, skills matrix completion, and CPD action planning

11.1 Key Concepts

• e-Portfolio Review: Summative and formative assessment throughout the programme
• Professional Skills Matrix: Assessment of current competencies and development needs
• Action Planning: Structured approach to continued professional development (CPD)
• Industry Certifications: Technical credentials for maintaining current knowledge
• Learning Loop: Holistic review tool for degree programme and future planning

11.2 Learning Outcomes

• Review learning approach based on reflections throughout the programme
• Complete Professional Skills matrix considering professional goals and module outcomes
• Provide necessary reflection for Learning Loop completion
• Develop comprehensive action plan for continued professional development

11.3 Reflection Notes

Week 11 represents a culmination of professional practice discussions throughout the module. The e-Portfolio serves as a comprehensive record of the learning journey, enabling creation of a Showcase e-Portfolio for sharing with peers and employers. Reflections on learning processes provide insight into personal learning approaches and their impact on professional practice.

The Professional Skills matrix and action plan extend beyond the degree programme, forming the foundation for lifelong learning and career development. Industry certifications play a crucial role in maintaining technical relevance and developing essential professional practice aspects.

References

1. UoEO. Short Guide to Reflective Writing
2. Mindtools. (no date) Personal Swot Analysis
3. O' Mara, M. (2023) The future of Computing, MIT
4. Jisc. (2019) Digital storytelling for the future
5. Jisc. (2019) Building digital and employability capabilities into the curriculum
6. Jisc. (2012) Stories of e-Portfolio Implementation - Thanet College, Kent
7. Alyoussef, I.Y. (2023) Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model
8. Ariningsih, N.K.D., Artini, L.P. and Marsakawati, N.P.E. (2021) 'The effect of e-portfolio in project-based learning toward learner autonomy and writing competency', Journal of Education Research and Evaluation, 5(1)
9. Pospíšilová, L. and Rohlíková, L. (2023) Reforming higher education with ePortfolio implementation, enhanced by learning analytics
10. Janssens, O. et al. (2022) The role of ePortfolios in supporting learning in eight healthcare disciplines: A scoping review. Nurse Education in Practice

12

Unit 12: Project Management and Managing Risk

Project management methodologies, risk assessment, and change management processes for computing projects

12.1 Key Concepts

• Project Management: Controlling processes and activities related to computing infrastructure and information systems projects
• Project Life Cycles: Structured phases and methodologies for project implementation
• Risk Management: Identifying, assessing, and mitigating project risks and uncertainties
• Change Control: Managing inevitable project changes while maintaining scope, quality, and timelines
• Remote Collaboration: Technologies and software supporting distributed project teams

12.2 Learning Outcomes

• Define and explain project management concepts and their application to computing projects
• Demonstrate appreciation of project life cycles and methodologies
• Understand how projects are impacted by risk and uncertainty
• Prepare comprehensive risk management plans
• Appreciate technologies supporting remote collaboration
• Control risk and manage project change effectively

12.3 Reflection Notes

The complexity of computing projects necessitates structured project management approaches, as no 'one size fits all' methodology exists (Nicholas & Steyn, 2020). The PMBOK Guide's emphasis on project uniqueness aligns with the diverse environments and objectives encountered in computing infrastructure projects. Risk-free projects are non-existent, making proactive risk identification crucial for project success.

Chapman's (2019) maturity model highlights that effective risk management enhances project capability through systematic deployment of assessment frameworks. The integration of Agile methodologies (Mircea, 2019) with traditional project management provides flexibility essential for managing software development uncertainties. McKinsey's (2022) AI status report demonstrates how technological advancement creates both opportunities and risks requiring adaptive project management approaches.

References

1. Nicholas, J.M. and Steyn, H., (2020) Project management for engineering, business and technology. Routledge. Chapters 1, 3, 5, and 10
2. Project Management Institute. (2020) A Guide to the Project Management Body of Knowledge PMBOK Guide, 7th Edition. Newtown Square, PA: Project Management Institute
3. Mircea, E. (2019) Project Management using Agile Frameworks
4. McManus, J. (2004) Risk Management in Software Development and Software Engineering Projects
5. The Association for Project Management (APM). (2023) What is a Gantt Chart?
6. McKinsey. (2023) The Status of AI in 2022
7. Successful projects. (no date) Project Management Methodologies
8. Chapman, R. (2019) 'Exploring the Value of Risk Management for Projects: Improving Capability Through the Deployment of a Maturity Model', IEEE Engineering Management Review, 47(1), pp. 126-143

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