Mark N. K. Saunders, Philip Lewis, Adrian Thornhill (9th Ed)
Saunders, Lewis, and Thornhill’s Research Methods for Business Students covers everything from generating a research idea, through the research onion of philosophy and design, to collecting data (surveys, interviews, secondary data) and analyzing it.
The book explains both quantitative (SPSS, Excel) and qualitative (thematic analysis, grounded theory, NVivo).
Finally, it walks you through writing up, presenting, and defending the work.
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Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson.
About the book: The most cited methods text in business dissertations. It walks the reader through every stage of research: developing the research question(s), reviewing literature, understanding philosophy, designing the study, collecting and analyzing data (quantitative & qualitative), and writing up. Here the entire 9th edition is turned into an intuitive, tab‑based reference – perfect for quick revision or deep dives.
Overview & Process
Philosophy & Theory
Design, Ethics & Samples
Data Collection
Analysis (Quant & Qual)
Writing & Reporting
Research process, reflective diaries & the research onion
The research process (Chapter 1)
Research is not linear – it is iterative. Start with a clear research idea, refine it into a question, review literature, choose a philosophy, design the study, negotiate access, collect and analyse data, and write up. A reflective diary or research notebook is essential: record decisions, problems, and insights as they happen. It sharpens your thinking and provides rich material for your final report.
Purpose of research
Exploratory, descriptive, explanatory
Exploratory asks “what”; descriptive “who, where, how much”; explanatory “why” (causal).
Research onion (Chapter 4 & 5)
Six layers
1 Philosophy → 2 Approach → 3 Methodological choice → 4 Strategy(ies) → 5 Time horizon → 6 Procedures (data collection & analysis).
Reflective diary
Critical for reflexivity
Record what you did, why, how you felt – feeds into learning logs and final write‑up.
Generating and refining ideas (Chapter 2)
- Good research ideas are feasible, clear, significant, and ethical. They fill a gap, extend theory, or solve a practical problem.
- Techniques: rational thinking (reviewing literature, debates, questionnaires) and creative thinking (brainstorming, relevance trees, Delphi method).
- Refining ideas: use the AbC (Abstract, Context) rule to structure your research question: What is the core problem (Abstract) and what are its boundaries (Context)?
- Write a clear overarching research question, aim (one sentence) and objectives (SMART – specific, measurable, achievable, relevant, time‑bound).
“A well‑formulated research question is the single most important element of your project. Everything else follows from it.” – Saunders, Lewis & Thornhill
Critically reviewing the literature (Chapter 3)
- Purposes: to contextualise your research, identify gaps, avoid reinventing the wheel, and develop theoretical framework.
- Critical review ≠ summary. You must evaluate, compare, and contrast arguments. Ask: “What is the evidence? How strong is it? What assumptions are made?”
- Literature sources: primary (original research), secondary (summaries), tertiary (indexes). Use academic databases (Scopus, Web of Science, Google Scholar).
- Planning the search: define keywords, inclusion/exclusion criteria, record search history. Use Boolean operators AND, OR, NOT.
- Systematic review (optional) – a rigorous, replicable method for synthesising evidence, especially for meta‑analysis.
Key structures for a literature review
- Chronological – by date (but avoid just listing)
- Thematic – by theme or concept (most common in business)
- Methodological – by research approach used in prior studies
- Theoretical – by competing theories or models
Research philosophy & approaches to theory development (Chapter 4)
Why philosophy matters
Your research philosophy is a system of beliefs about how knowledge is created. It shapes every decision – from your question to your methods. Three core branches: ontology (nature of reality), epistemology (what counts as knowledge), and axiology (role of values).
Objectivism
- Reality exists independently of us.
- Social entities are like physical objects.
- One true reality measurable through facts.
- Value‑free, detached researcher.
Subjectivism
- Reality is socially constructed.
- Meanings are created by actors.
- Multiple, fluid realities.
- Researcher values are integral and reflexive.
Five major research philosophies
Positivism
Natural science model
Observable facts, law‑like generalisations, hypothesis testing. Typically quantitative, deductive.
Critical realism
Layered reality
Empirical (observed) → Actual (events) → Real (underlying mechanisms). Focus on explaining causes.
Interpretivism
Meanings & stories
Humans are different from physical phenomena. Empathetic understanding, qualitative, small samples.
Postmodernism
Deconstruction
Language and power create “truth”. Exposes silences, marginalised voices. Radically reflexive.
Pragmatism
Action & consequences
Concepts are only relevant if they support action. Research problem drives methods. Mixed methods welcomed.
Approaches to theory development
Deduction
- Theory → hypothesis → data collection → test → confirm/reject.
- “Testing theory”.
- Explains causal relationships.
- Typically quantitative, structured, large samples.
Induction
- Data → patterns → theory.
- “Building theory”.
- Understand meanings.
- Qualitative, small samples, rich data.
Abduction
- Surprising fact → plausible explanation → test → refine.
- Moves back and forth between data and theory.
- Most common in practice.
- Often used in grounded theory and case studies.
“Your philosophical position is not an abstract luxury – it is a practical guide to what you can claim and how you can defend it.” – Saunders et al.
Burrell & Morgan’s four paradigms (for organisational analysis)
- Functionalist (objectivist + regulation) – dominant paradigm, problem‑solving within existing structures.
- Interpretive (subjectivist + regulation) – understanding social worlds from participants’ perspectives.
- Radical structuralist (objectivist + radical change) – critique structures of domination, conflict.
- Radical humanist (subjectivist + radical change) – emancipation, consciousness‑raising.
These are “ideal types”. Many researchers blend elements, but be aware of paradigmatic assumptions.
Research design, ethics & sampling (Chapters 5–7)
Coherent research design (Chapter 5)
Your design must be internally consistent: philosophy → approach → methodological choice → strategy → time horizon → procedures. No “picking and mixing” without justification.
Methodological choice
- Quantitative – numerical data, statistics, often deductive.
- Qualitative – non‑numerical data (words, images), often inductive.
- Mixed methods – combine both, often pragmatist. Can be concurrent or sequential.
Research strategies (partial list)
- Experiment – tests causal relationships, high control.
- Survey – large samples, questionnaires, descriptive or explanatory.
- Case study – in‑depth study of a single case (organisation, event, person).
- Action research – researcher intervenes to solve a problem, simultaneous inquiry and practice.
- Grounded theory – theory emerges from data, inductive/abductive.
- Ethnography – immersion in culture/setting, long‑term observation.
- Archival research – uses existing records and documents.
Time horizons
Cross‑sectional vs. Longitudinal
Cross‑sectional: snapshot at one time. Longitudinal: repeated measures over weeks, months, years.
Ethics (Chapter 6)
Principles
Informed consent, confidentiality, anonymity, right to withdraw, avoidance of harm, data protection (GDPR).
Sampling (Chapter 7)
Probability vs. Non‑probability
Probability: random, representative, generalisable. Non‑probability: purposive, quota, snowball, convenience.
Sampling decisions in a nutshell
- Sample size – for probability sampling, use sample size tables (confidence level, margin of error). For non‑probability, size is determined by data saturation (qualitative).
- Probability procedures – simple random, systematic, stratified random, cluster, multi‑stage.
- Non‑probability procedures – quota, purposive (judgemental), snowball, convenience, self‑selection.
- Mixed‑method and multi‑stage sampling – combine approaches for complex studies.
“Access is often the biggest practical hurdle. Persistent, professional and personable approaches win the day.” – Saunders et al. (Chapter 6)
Collecting primary and secondary data (Chapters 8–11)
Secondary data (Chapter 8)
- Types: documentary (reports, minutes), survey‑based (census, longitudinal studies), multiple‑source (combinations).
- Advantages: cost‑effective, longitudinal, large samples, sometimes the only way to study historical trends.
- Disadvantages: may not perfectly fit your research question, unknown data quality, missing variables, outdated.
- Evaluation checklist: Relevance, currency, authority, purpose, methodology (RAPM).
Observation (Chapter 9)
- Participant observation – researcher becomes part of the group. Rich data but risk of bias and ethical issues.
- Structured observation – uses predefined categories (e.g., interaction analysis). High reliability, low richness.
- Internet‑mediated observation – social media, forums, virtual worlds.
- Recording – video, static images, audio. Must consider reactivity and consent.
Interviews & diaries (Chapter 10)
- Semi‑structured interviews – flexible, allows probing. Good for exploring “how” and “why”.
- In‑depth (unstructured) interviews – non‑directive, like a conversation. For rich life histories or sensitive topics.
- Focus groups – group interaction, 6‑10 participants, useful for generating ideas and observing group dynamics.
- Telephone/online interviews – cost‑effective, but loss of visual cues (unless video).
- Diary studies – participants record events/emotions in real time. Reduces recall bias.
Interview data quality issues
- Interviewer bias (leading questions, tone)
- Response bias (social desirability, acquiescence)
- Memory decay (if asking about past events)
- Artifacts (location, recording equipment)
Preparing for interviews
- Develop an interview guide (themes and possible probes)
- Pilot test with a small sample
- Record and transcribe (verbatim if possible)
- Plan analysis before collecting data
Questionnaires (Chapter 11)
- Design principles: clear, unambiguous, logical flow, avoid double‑barrelled questions, use simple language.
- Question types: open‑ended (rich but hard to code) vs. closed‑ended (rating scales, Likert, semantic differential, multiple choice).
- Validity – does it measure what it intends? Reliability – would it produce consistent results if repeated?
- Pilot testing – essential to catch wording issues, missing options, and to estimate completion time.
- Distribution modes: online (SurveyMonkey, Qualtrics), postal, email, hand‑delivered. Online is fastest and cheapest but may have coverage bias.
“A poorly designed questionnaire is the fastest route to worthless data. Pilot, pilot, pilot.” – Saunders, Lewis & Thornhill
Analysing data quantitatively and qualitatively (Chapters 12–13)
Quantitative analysis (Chapter 12)
- Data types: categorical (nominal, ordinal) and metric (interval, ratio). Choice of test depends on type.
- Preparing data: code responses, enter into software (SPSS, Excel), clean for errors, handle missing values.
- Descriptive statistics: mean, median, mode, standard deviation, range, frequency tables, histograms, boxplots.
- Inferential statistics: hypothesis testing, confidence intervals, p‑values. Tests include t‑test, ANOVA, chi‑square, correlation (Pearson/Spearman), regression (simple/multiple).
- Assumptions: normality, homogeneity of variance, independence of errors – check before using parametric tests.
- Making predictions: linear regression, logistic regression. Examines relationships and forecasts.
- Trend analysis: time series, moving averages, for longitudinal data.
Most common tests
t‑test (2 groups) / ANOVA (3+)
Compares means. Use non‑parametric alternatives (Mann‑Whitney, Kruskal‑Wallis) when assumptions violated.
Correlation
Pearson (r)
Strength and direction of linear relationship between two metric variables.
Regression
R², coefficients
Predicts one variable from one (simple) or several (multiple) others.
Qualitative analysis (Chapter 13)
- Diversity of methods: thematic analysis, template analysis, grounded theory, narrative analysis, discourse analysis, visual analysis.
- Thematic analysis (most common): identify, analyse, and report patterns (themes) within data. Follow Braun & Clarke’s 6‑phase process.
- Template analysis: start with an initial template (codes) and refine during analysis. Useful for hierarchical coding.
- Grounded theory: generates theory from data via constant comparison and theoretical sampling. Coding stages: open → axial → selective.
- Narrative analysis: focuses on stories people tell, their structure and meaning.
- Discourse analysis: examines language as social practice, power, and identity construction.
- CAQDAS (Computer‑Assisted Qualitative Data Analysis Software): NVivo, ATLAS.ti. Helps manage coding and retrieval, but does not “think” for you.
Gioia method (increasingly popular)
A rigorous approach to inductive theory building. Steps: 1st‑order concepts (informant quotes), 2nd‑order themes (researcher‑derived), aggregate dimensions. Ensures transparency and traceability from data to conclusions.
“Qualitative analysis is not about counting – it is about understanding meanings, contexts, and processes. The researcher is the instrument.” – Saunders et al.
Writing, presentation and the project report (Chapter 14)
Practical advice for writing
- Start writing early – write as you go, even if it’s rough. Use your reflective diary.
- Craft a clear thread – from research question to conclusions. Every section should answer “So what?”
- Common structure: Title, Abstract, Introduction, Literature Review, Methodology, Findings/Analysis, Discussion, Conclusion, References, Appendices.
- Alternative structures: for consultancy reports (executive summary, diagnosis, recommendations) or thesis by publication.
- Audience awareness: academic examiners expect critical engagement; practitioners want clear, actionable recommendations.
Writing style and clarity
- Use plain English – avoid jargon, be concise, active voice (“we analysed” not “it was analysed”).
- Paragraphs – one main idea per paragraph, topic sentence, supporting evidence.
- Referencing – follow a consistent style (most common: Harvard, APA). Use reference management software (Zotero, Mendeley, EndNote).
- Avoid plagiarism – paraphrase and cite; use quotes for essential passages; check with Turnitin if available.
Presentations, posters, and viva (oral defence)
- Oral presentations – know your audience, tell a story, use slides sparingly (no more than 1 per minute). Practice, time yourself.
- Poster design – clear hierarchy, visuals over text, 20‑30% whitespace, readable from 1.5 metres.
- Viva (oral examination) – expect questions about why you chose your methods, limitations, original contribution. Be calm, honest, and prepared to defend.
Word count distribution (typical dissertation)
Intro 10% · Lit review 25% · Methodology 15% · Findings 20% · Discussion 15% · Conclusion 10% · References & appendices (extra)
Check your institution’s guidelines – vary by level.
Reflective essay
Personal, critical
Use your diary entries to show learning and development as a researcher.
“Writing is not just reporting your research – it is part of the research process itself. It clarifies your thinking and reveals gaps you didn’t know existed.” – Saunders, Lewis & Thornhill
Complete reference and further reading
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson.
Key chapters in this edition – Updated for post‑pandemic research (more online and mixed methods), expanded coverage of diaries, video observation, the Gioia method, and the HARP tool for philosophy reflection.
This digital summary is designed for learning and revision. All core concepts have been faithfully extracted from the 9th edition. Refer to the original book for full worked examples, datasets, and online tutorials (SPSS, Excel, Qualtrics).
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