Research methodology is one of the most important — and most misunderstood — concepts in academic research. Every PhD student encounters it in the first months of their doctoral programme. Most are told they need a methodology chapter without being given a clear explanation of what research methodology actually is, why it matters, and how to choose the right approach for their specific study.
This guide explains research methodology from the ground up — in plain language, with practical examples drawn from academic research contexts. Whether you are beginning your PhD, writing a research proposal, or trying to understand your methodology chapter for the first time, this guide will give you the conceptual foundation you need.
What Is Research Methodology?
A research methodology is the plan that explains how a study will produce trustworthy answers — not just collect information. It is the systematic framework that guides every decision you make about how your research is designed, how data is collected, how it is analysed, and how findings are interpreted and reported.
Think of research methodology as the architectural blueprint of your study. Just as a building cannot be constructed without a detailed plan that specifies the materials, structure, and construction process, a research study cannot produce credible findings without a clear methodological framework that specifies what kind of data will be collected, how it will be gathered, and how it will be analysed.
At its core, research methodology answers three essential questions: What kind of data will you collect? How will you collect this data? And why are these methods appropriate — that is, how do they align with your research aims, questions, and philosophical stance?
Research methodology vs research methods — the critical distinction:
Many beginners confuse research methodology with research methods. They are related but not the same.
Research methodology is the broader philosophical and strategic framework — it is your overall approach to inquiry, the principles that guide your choices, and the justification for those choices. Research methods are the specific tools and techniques you use within that framework — surveys, interviews, experiments, observations, document analysis.
Your methodology explains why you are using particular methods. Your methods describe what you are actually doing. You cannot describe your methods without first establishing your methodology — because the methods must be justified within the methodological framework.
Why research methodology matters in a PhD thesis:
Your choice of methodology will be shaped by your field of study — in the humanities, methodologies often draw on interpretative, critical, or historical approaches. In sciences they tend toward experimental or empirical frameworks. In social sciences they draw on a wide spectrum from positivist quantitative designs to interpretivist qualitative approaches.
Your methodology chapter is examined rigorously because it determines the credibility of everything that follows. A research question can be brilliant, but if the methodology used to investigate it is inappropriate or poorly justified, the findings cannot be trusted. Examiners and peer reviewers look at methodology first when evaluating whether research conclusions are warranted.
The Research Onion — Understanding the Layers of Methodology
One of the most widely used frameworks for understanding research methodology is the Research Onion, developed by Saunders, Lewis, and Thornhill. It visualises the layers of methodological decision-making that researchers must work through — from the outermost philosophical layer to the innermost data collection layer.
The layers from outside to inside are: research philosophy, research approach, research strategy, research choices, time horizon, and data collection and analysis techniques.
Understanding each layer helps you make coherent methodological decisions — because each layer constrains and informs the choices available in the layers inside it.
Research Philosophy — The Foundation of Your Methodology
Research philosophy is the outermost layer and the most fundamental. It refers to the set of beliefs and assumptions about the nature of knowledge and reality that underpin your research approach.
Most beginning researchers find research philosophy the most abstract and confusing component of methodology. But understanding it — even at a basic level — is essential because it explains why researchers in different disciplines approach the same topic in fundamentally different ways.
Positivism
Positivism holds that reality exists independently of the observer and can be measured objectively. Knowledge is built through empirical observation and measurement — by collecting data that can be quantified, analysed statistically, and used to test hypotheses.
Positivist research assumes that there are discoverable laws and patterns governing social and natural phenomena, and that the role of research is to identify and test these laws through rigorous, replicable methods.
Positivism is the dominant philosophy in natural sciences and is widely used in quantitative social science research. If you are conducting a survey, running an experiment, or performing statistical analysis, your research is most likely positivist or post-positivist in its philosophical orientation.
Example: A study measuring the relationship between supervision frequency and PhD completion rates, using survey data analysed with regression analysis, is positivist in orientation.
Interpretivism
Interpretivism holds that social reality is constructed by the people who experience it — that meaning is not discovered but created through interpretation. Knowledge is built through understanding the subjective meanings, perspectives, and experiences of research participants.
Interpretivist research assumes that human behaviour cannot be understood through the same lens as natural phenomena — that context, culture, history, and individual perspective fundamentally shape what people do and why.
Interpretivism is the dominant philosophy in humanities, and is widely used in qualitative social science research. If you are conducting interviews, ethnographic observations, or analysing texts and documents, your research is most likely interpretivist in orientation.
Example: A study exploring how PhD students experience the supervision relationship — using in-depth interviews and thematic analysis — is interpretivist in orientation.
Pragmatism
Pragmatism holds that the most useful philosophical stance is determined by the research question — not by a prior commitment to either positivism or interpretivism. A pragmatist uses whatever philosophical approach and methods best address the research problem.
Pragmatism is the philosophical foundation most commonly associated with mixed-methods research — studies that combine quantitative and qualitative methods because the research question requires both types of data.
Example: A study that uses a survey to measure the prevalence of PhD writing challenges and interviews to understand how students experience those challenges is pragmatist in orientation.
Research Approaches — Deductive, Inductive, and Abductive
The research approach refers to the logic of your inquiry — how you move between theory and data.
Deductive research approach
A deductive approach begins with theory. You start with an existing theoretical framework, derive specific hypotheses or predictions from it, collect data to test those hypotheses, and use the results to confirm, revise, or reject the theory.
Deductive research moves from the general (theory) to the specific (data). It is associated with positivist philosophy and quantitative methods.
Example: You begin with the theory that supervision quality predicts PhD completion. You derive a hypothesis — students with high-quality supervision will have significantly higher completion rates. You collect survey data from 300 PhD students, measure supervision quality and completion outcomes, and test whether the relationship exists statistically.
Inductive research approach
An inductive approach begins with data. You collect observations or data without a predetermined theoretical framework, look for patterns and themes in the data, and build new theory or conceptual frameworks from what you find.
Inductive research moves from the specific (data) to the general (theory). It is associated with interpretivist philosophy and qualitative methods.
Example: You conduct in-depth interviews with 20 PhD students about their supervision experiences without a prior framework, analyse the transcripts for recurring themes, and develop a new model of how students experience supervisory relationships based on what you found.
Abductive research approach
An abductive approach moves back and forth between theory and data — starting with an incomplete set of observations, seeking the most likely explanation, and iteratively refining the explanation as more data is gathered.
Abductive reasoning is associated with pragmatism and mixed-methods research. It is increasingly recognised as reflecting how research actually proceeds in practice — rarely in a purely deductive or purely inductive way.
Research Design — Quantitative, Qualitative, and Mixed Methods
Quantitative methods accounted for roughly 70% of total global market research expenditure, while qualitative approaches represented about 15% of global spend leading into 2025. That does not mean quantitative research is automatically better — it means numerical methods remain heavily used in settings where scale, measurement, and comparable results are important.
Research design refers to the overall structure of your study — how the major components of your research (aims, data collection, analysis) fit together in a coherent whole.
Quantitative research design
Quantitative research collects and analyses numerical data. It is appropriate when your research questions require measuring variables, testing relationships, establishing frequencies, or making statistical generalisations about a population.
Characteristics of quantitative research:
- Data is expressed in numbers — scores, frequencies, percentages, rankings
- Large samples are typical — enough for statistical analysis
- Research instruments (surveys, tests, scales) are standardised
- Analysis uses statistical methods — descriptive statistics, regression, ANOVA, SEM
- Findings can be generalised to a wider population
When to use quantitative research: When you need to measure how much, how often, or how strong a relationship is. When you need findings that can be statistically tested and replicated. When you have a large enough population to sample meaningfully.
Example studies:
- Measuring the relationship between research self-efficacy and PhD completion rates across 500 doctoral students
- Testing whether a new thesis writing intervention improves student confidence scores
- Analysing survey data on academic writing challenges among 300 international PhD students
Qualitative research design
Qualitative research focuses on meaning, context, experience, and interpretation. Instead of reducing the study to numbers alone, the researcher gathers descriptive data through interviews, observations, texts, artifacts, or open-ended responses. This approach is especially useful when the goal is to understand how people experience a situation, how a process unfolds, or why participants interpret events in different ways.
Characteristics of qualitative research:
- Data is expressed in words, images, or observations — not numbers
- Smaller samples are typical — depth over breadth
- Data collection is flexible and iterative — methods can evolve as the study progresses
- Analysis is interpretive — thematic analysis, grounded theory, discourse analysis
- Findings are transferable rather than generalisable — they apply to similar contexts
When to use qualitative research: When you need to understand the meaning, process, or experience of a phenomenon rather than measure it. When the phenomenon is complex, poorly understood, or cannot be reduced to numbers without losing essential meaning. When your research questions begin with “how” or “why” rather than “how much” or “how many.”
Example studies:
- Exploring how international PhD students experience the supervision relationship at Indian universities
- Understanding why female researchers leave academia at disproportionate rates through interview research
- Analysing how PhD students develop their researcher identity over the course of their doctoral programme
Mixed methods research design
Mixed methods research combines quantitative and qualitative approaches in one study. It is appropriate when a research question is complex enough that neither quantitative nor qualitative data alone can address it adequately.
Common mixed methods designs:
Sequential explanatory — quantitative data is collected and analysed first, then qualitative data is collected to help explain or contextualise the quantitative findings. For example: survey data shows that supervision quality predicts completion rates; interviews then explore why and how supervision quality affects students’ progress.
Sequential exploratory — qualitative data is collected first to explore a poorly understood phenomenon; quantitative data is then collected to test or generalise the qualitative findings. For example: interviews reveal five key barriers to PhD completion; a survey then measures the prevalence and severity of each barrier across a large sample.
Concurrent triangulation — both quantitative and qualitative data are collected simultaneously and compared to validate or cross-check findings. The two datasets provide different perspectives on the same phenomenon.
Research Strategies — How You Will Conduct Your Study
Research strategy refers to the specific approach or design you use to collect and analyse data. Different strategies are appropriate for different types of research questions.
Survey research
A survey collects standardised data from a sample of respondents through questionnaires — either self-administered (online or paper) or interviewer-administered. Surveys are efficient for collecting data from large samples and are the most widely used strategy in social science and management research.
Surveys are appropriate when you need to describe the characteristics of a population, measure attitudes or perceptions at scale, or test relationships between variables across a large sample.
Case study research
A case study is an in-depth investigation of a single case — a person, organisation, event, programme, or phenomenon — within its real-world context. Case studies are used when the boundaries between the phenomenon and its context are blurred, and when you need to understand the complexity of the case rather than isolate variables.
Case studies are particularly appropriate for research questions that ask “how” or “why” about a contemporary phenomenon you cannot manipulate or control.
Experimental research
An experiment manipulates one or more variables (independent variables) and measures their effect on another variable (dependent variable) while controlling for other influences. Experiments are the gold standard for establishing causal relationships.
True experiments require random assignment of participants to conditions. Quasi-experiments use pre-existing groups rather than random assignment. Both are used extensively in psychology, education, and health sciences.
Ethnographic research
Ethnography involves the researcher immersing themselves in the natural setting of the group or community being studied — typically over an extended period — to understand the culture, practices, and meanings of that group from an insider perspective.
Ethnography is appropriate for research questions that require deep contextual understanding of how people live and make meaning in their natural environments.
Grounded theory
Grounded theory is a systematic qualitative strategy for generating theory from data. The researcher collects data — typically through interviews — and simultaneously analyses it, using the emerging analysis to guide further data collection until theoretical saturation is reached.
Grounded theory is appropriate when the phenomenon under investigation is poorly understood and no adequate existing theory exists to explain it.
Action research
Action research involves the researcher working collaboratively with practitioners — teachers, managers, community members — to identify a problem, develop an intervention, implement it, evaluate its effects, and refine the approach based on evaluation. The goal is simultaneously to solve a practical problem and to generate knowledge.
Action research is widely used in education, health, and organisational research.
Systematic review and meta-analysis
A systematic review synthesises the findings of all available studies on a specific research question, using explicit and replicable methods to search, select, and evaluate the evidence. A meta-analysis goes further by statistically combining the quantitative results of multiple studies.
Systematic reviews and meta-analyses are at the top of the evidence hierarchy in medicine and health sciences and are increasingly used in social sciences and education.
Sampling — Who or What Will You Study?
Sampling refers to the process of selecting the participants, cases, documents, or data sources from which you will collect your research data. Since you almost never have access to the entire population you are interested in, you need a strategy for selecting a representative or purposive sample.
Probability sampling
In probability sampling, every member of the population has a known and equal chance of being selected. Probability samples allow statistical generalisation from the sample to the population.
Simple random sampling — every member of the population is assigned a number and participants are selected randomly. The purest form of probability sampling.
Stratified random sampling — the population is divided into subgroups (strata) based on a characteristic (e.g. discipline, gender, year of study) and random samples are drawn from each stratum. Ensures representation of all subgroups.
Cluster sampling — the population is divided into clusters (e.g. universities) and whole clusters are randomly selected. Used when a complete list of individuals is not available but a list of clusters is.
Non-probability sampling
In non-probability sampling, the selection is not random. Non-probability samples do not allow statistical generalisation but are often more practical and appropriate for qualitative and exploratory research.
Purposive sampling — participants are selected deliberately because they possess particular characteristics relevant to the research question. The most common sampling strategy in qualitative research.
Snowball sampling — initial participants are asked to refer other potential participants. Used when the target population is hard to reach — for example, researchers who have withdrawn from their PhD programmes.
Convenience sampling — participants are selected because they are easily accessible. The weakest form of sampling but widely used in exploratory and student research.
How large should your sample be?
Sample size depends on your research design. For quantitative research requiring statistical analysis, sample size should be determined by a power analysis — calculating the minimum sample needed to detect the effect size of interest with adequate statistical power. As a rough guide, regression analyses typically require a minimum of 100 to 150 participants; structural equation modelling requires 200 or more.
For qualitative research, sample size is determined by theoretical saturation — you continue collecting data until no new themes or insights are emerging. In practice, most qualitative studies using in-depth interviews reach saturation with 15 to 30 participants, though this varies significantly by topic and approach.
Data Collection Methods
Data collection methods are the specific techniques used to gather information from your sample. The most commonly used methods in PhD research are surveys, interviews, observations, and document analysis.
Surveys and questionnaires
Surveys collect standardised responses from large numbers of participants through written questions. They are efficient, cost-effective, and produce data that can be analysed statistically.
Surveys are appropriate for measuring attitudes, perceptions, behaviours, and demographic characteristics across a large sample. For detailed guidance on designing a research questionnaire, see our complete guide on How to Develop a Research Questionnaire.
Interviews
Interviews collect data through direct conversation between the researcher and participant. They are the primary data collection method in most qualitative research.
Structured interviews use a fixed set of questions asked in the same order to every participant. They produce standardised data but limit the depth of response.
Semi-structured interviews use a prepared set of topic areas and key questions but allow the researcher to probe interesting responses and follow unexpected lines of inquiry. The most widely used interview format in qualitative PhD research.
Unstructured interviews use no predetermined questions — they are open conversations guided by the research topic. Produce rich, detailed data but are difficult to analyse across participants.
Observations
Observation involves the researcher directly watching and recording behaviour, events, or settings. Observations capture what people actually do rather than what they say they do — addressing the gap between reported and actual behaviour.
Structured observation uses a predetermined coding scheme to record specific behaviours. Used in educational and psychological research.
Participant observation involves the researcher participating in the activities of the group being studied while simultaneously observing and recording. The primary data collection method in ethnographic research.
Document and archival analysis
Document analysis involves systematically examining written or visual materials — government documents, policies, reports, letters, academic publications, media texts, photographs, or social media content. Used in historical research, policy analysis, and discourse analysis.
Secondary data analysis
Secondary data analysis involves analysing existing datasets collected by other researchers or organisations. Examples include national census data, large-scale survey datasets, administrative records, and published research databases. Secondary data is free to access but was collected for a different purpose — you must assess whether it adequately addresses your research questions.
Data Analysis Methods
Data analysis is the process of making sense of the data you have collected — identifying patterns, testing relationships, and drawing conclusions.
Quantitative data analysis
Quantitative analysis applies statistical techniques to numerical data. The choice of statistical technique depends on the nature of your data and your research questions.
Descriptive statistics summarise and describe the basic features of your data — means, standard deviations, frequencies, percentages. They tell you what the data looks like before you test any relationships.
Inferential statistics allow you to draw conclusions about a population from a sample — testing whether relationships observed in the sample are likely to exist in the wider population. Common inferential techniques include t-tests, ANOVA, chi-square tests, correlation, and regression analysis.
Regression analysis examines the relationship between one or more independent variables and a dependent variable — allowing you to predict outcomes and identify which variables are most strongly associated with the outcome of interest.
Structural equation modelling (SEM) tests complex theoretical models with multiple variables and relationships simultaneously. Used in management, psychology, and social science research where theoretical frameworks are multi-dimensional.
The most widely used software for quantitative data analysis in PhD research is SPSS, R, and Stata. Python is increasingly used for complex statistical analysis.
Qualitative data analysis
Qualitative analysis makes sense of non-numerical data — interview transcripts, observation notes, documents, and images — by identifying themes, patterns, and meanings.
Thematic analysis involves systematically coding the data to identify recurring themes and patterns. It is the most widely used qualitative analysis method across disciplines because of its flexibility and accessibility to beginning researchers.
Content analysis involves systematically categorising and counting the frequency of specific content elements — words, phrases, themes — in a body of text. Can be qualitative or quantitative.
Grounded theory analysis involves open coding, axial coding, and selective coding in an iterative process of developing theoretical categories from the data. Used when the goal is to generate new theory.
Discourse analysis examines how language is used in texts or speech — focusing not just on what is said but on how it is said, what assumptions it carries, and what power relations it reflects.
Interpretative phenomenological analysis (IPA) examines how individuals make sense of their lived experiences. Used in psychology, health, and education research.
The most widely used software for qualitative data analysis is NVivo, Atlas.ti, and MAXQDA.
Validity and Reliability in Research Methodology
Two concepts central to evaluating the quality of any research study are validity and reliability. Understanding both is essential for PhD researchers regardless of their methodological approach.
Validity
Validity refers to the extent to which your research measures what it claims to measure and your conclusions are warranted by your data.
Internal validity — are the conclusions you draw about relationships and causation justified by your data? Is the observed relationship due to the variables you measured, or could it be explained by other factors you did not control for?
External validity — can your findings be generalised beyond your specific sample and context? Do your conclusions apply to other populations, settings, and time periods?
Construct validity — does your measurement instrument accurately measure the theoretical construct you intend to measure? Does your measure of “research self-efficacy” actually capture what researchers mean by that concept?
Content validity — does your measurement instrument cover all the important dimensions of the construct it is measuring? Does your questionnaire on PhD writing challenges address all the major challenges researchers face?
Reliability
Reliability refers to the consistency of your measurement — whether the same measurement produces the same results across different times, raters, or conditions.
Test-retest reliability — does your instrument produce consistent results when administered to the same people on two different occasions?
Inter-rater reliability — when two researchers independently code the same data, do they reach the same conclusions? Particularly important in qualitative research and observational studies.
Internal consistency — do the items in a scale or questionnaire all measure the same underlying construct? Measured using Cronbach’s alpha — an acceptable threshold is 0.7 or above.
In qualitative research, validity and reliability are assessed through different criteria — credibility, transferability, dependability, and confirmability — which serve the same function as validity and reliability in quantitative research but acknowledge the different epistemological assumptions of interpretivist inquiry.
Ethics in Research Methodology
Ethical compliance is a mandatory requirement of all PhD research. Before collecting any data — from any source — you must obtain ethical approval from your institution’s Institutional Review Board (IRB) or Research Ethics Committee.
Informed consent — every participant must be informed about the study’s purpose, their role, how their data will be used, and their right to withdraw at any time. Consent must be given voluntarily and documented.
Anonymity and confidentiality — protect participants’ identities and personal information. Anonymity means you cannot identify who gave which response. Confidentiality means you can identify participants but protect their identity in all publications.
Avoidance of harm — no participant should suffer physical, psychological, social, or professional harm as a result of participating in your research. This includes protecting participants from distress caused by sensitive research topics.
Data protection — store all research data securely, limit access to authorised researchers, and follow your country’s data protection legislation.
Research integrity — report your findings honestly and accurately. Do not fabricate, falsify, or selectively report data. Do not plagiarise others’ work or misrepresent others’ findings.
How to Write the Research Methodology Chapter
The methodology chapter of your PhD thesis must do three things — describe what you did, justify why you did it, and demonstrate that you understand the epistemological basis for your approach.
A methodology chapter that only describes is insufficient at doctoral level. You must justify every major decision — why this research philosophy, why this design, why this sample, why these analysis techniques — by explaining how each choice is appropriate for your research questions and consistent with the broader methodological framework.
Structure of a PhD methodology chapter:
The chapter typically covers research philosophy and paradigm, research approach (deductive, inductive, abductive), research design (quantitative, qualitative, mixed), research strategy (survey, case study, experiment etc), sampling strategy and sample size, data collection methods and instruments, data analysis methods, validity and reliability, ethical considerations, and limitations of the methodology.
Each section should both describe and justify. Not “I used semi-structured interviews” but “Semi-structured interviews were selected because the research question required understanding how participants make meaning of their experiences — a goal that standardised surveys cannot achieve — while the topic guide ensured sufficient consistency across interviews for thematic comparison.”
Common Research Methodology Mistakes Beginners Make
Confusing methodology with methods. Your methodology is the philosophical and strategic framework. Your methods are the specific tools. Always establish the framework before describing the tools.
Describing without justifying. Listing what you did without explaining why you made each choice is insufficient at PhD level. Every methodological decision requires justification.
Choosing methods before the research question is clear. The research question should determine the methodology — not the other way around. Choosing a survey because “everyone uses surveys” without considering whether your research question requires quantitative data is a methodological error.
Ignoring the literature on methodology. Your methodology chapter should cite methodological literature — Creswell, Bryman, Saunders, Yin, or discipline-specific methodology texts. This demonstrates that your methodological choices are grounded in established scholarly practice.
Claiming more generalisability than the sample supports. A convenience sample of 50 students at one university cannot support claims about PhD students globally. Be accurate about what your sample allows you to conclude.
Neglecting ethical considerations. Ethics is not a box to tick — it is an integral part of the methodological design. Discuss ethical considerations substantively, not as a brief afterthought.
Frequently Asked Questions
What is the difference between research methodology and research methods? Research methodology is the broader philosophical and strategic framework that guides your research — it encompasses your research philosophy, approach, design, and the justification for all your choices. Research methods are the specific tools and techniques you use to collect and analyse data within that framework — surveys, interviews, experiments, statistical analysis. Methodology explains why; methods describe what.
Which is better — quantitative or qualitative research? Neither is inherently better — the appropriate approach depends entirely on your research question. Quantitative methods are appropriate when you need to measure, test, or compare variables across a large sample. Qualitative methods are appropriate when you need to understand meaning, experience, or process in depth. Many PhD studies use both through a mixed-methods design. Choose the approach that best addresses your specific research question, not the one most common in your field or most convenient for your data access.
How do I choose the right research methodology for my PhD? Start with your research question — it determines everything. Ask: does my question require measuring and testing (quantitative) or understanding and exploring (qualitative)? Does it require both? Then consider your research paradigm — what philosophical assumptions about knowledge underlie your field? Then consider practical constraints — what data can you realistically access? What skills do you have or can develop? Discuss your choices with your supervisor before finalising your methodology.
What is a research paradigm? A research paradigm is the overarching philosophical framework — the set of shared assumptions about the nature of reality and knowledge — within which research is conducted. The major paradigms in social science research are positivism, interpretivism, and pragmatism. Your paradigm shapes your research philosophy, approach, design, and methods — it is the lens through which you understand what counts as valid knowledge and how it can be produced.
How long should the methodology chapter be in a PhD thesis? A PhD methodology chapter is typically 8,000 to 15,000 words, representing approximately 10 to 15% of the total thesis word count. This varies by discipline — science theses tend to have shorter methodology chapters (3,000 to 6,000 words) because the methods are often highly standardised; social science and management theses tend to have longer ones because the philosophical justification requires more detailed explanation. Always check your institution’s guidelines and discuss expected length with your supervisor.
What is triangulation in research methodology? Triangulation is the use of multiple methods, data sources, researchers, or theoretical perspectives to cross-check and validate research findings. Methodological triangulation — using both quantitative and qualitative methods — is the most common form. The idea is that if different methods produce consistent findings, confidence in those findings is increased. If they produce inconsistent findings, this inconsistency itself becomes a finding worth exploring and explaining.
Conclusion
Research methodology is not an abstract philosophical exercise — it is the practical foundation of credible research. Every decision you make about how to conduct your study either strengthens or weakens the validity of your findings. Understanding the philosophical assumptions behind your methodological choices, aligning those choices coherently with your research questions, and justifying every decision with reference to the scholarly literature on methodology are the hallmarks of doctoral-level methodological thinking.
The concepts covered in this guide — research philosophy, approaches, designs, strategies, sampling, data collection, analysis, validity, reliability, and ethics — provide the complete framework for understanding and writing your research methodology. Return to specific sections as you work through your methodology chapter and use them as a reference throughout your doctoral programme.
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