Dissertation Methodology Checklist

The research problem is specific enough to guide method decisions.

The methodology explains how evidence will answer the problem.

The chapter avoids generic textbook descriptions that are not connected to the study.

Each major method choice is linked to the aim, objectives, or questions.

The chapter explains what the method can and cannot show.

The aim states the main purpose of the dissertation in one clear direction.

Objectives are specific, measurable or reviewable, and aligned with the scope.

Objectives are not so broad that the chosen method cannot address them.

Each objective can be matched to a data source or analysis activity.

The methodology chapter uses the same wording and logic as the introduction.

Each research question is answerable using the proposed data.

Questions are not merely topic headings or yes/no claims without analysis depth.

Qualitative questions invite explanation, interpretation, experience, or meaning.

Quantitative questions identify measurable variables, relationships, differences, or patterns.

Mixed-methods questions explain what each method contributes.

The chapter names the design, such as case study, survey, experiment, interview study, comparative study, or design science project.

The design choice is justified using the research questions and practical constraints.

The design explains units of analysis, context, time frame, and evidence boundaries.

Alternative designs are briefly considered when useful.

The chosen design is feasible within the deadline, access, ethics, and skill constraints.

The chapter identifies qualitative, quantitative, mixed-methods, or practice-based orientation.

Inductive, deductive, abductive, or design-led logic is explained where relevant.

The philosophical position is included only if required and connected to method decisions.

The approach avoids claiming certainty beyond the method's evidence.

The reporting style matches the selected approach.

Interviews, surveys, experiments, observations, document analysis, simulations, or secondary datasets are chosen for clear reasons.

The method is practical for the researcher's access, time, tools, and ethics approval route.

The method can produce data at the depth or scale needed.

The method does not require unavailable participants, instruments, software, or permissions.

The method is explained in enough detail for review.

The chapter explains why the method fits the research questions.

The method is justified with methodological literature or discipline norms where needed.

Limitations of the chosen method are acknowledged honestly.

Rejected alternatives are mentioned if they clarify the decision.

The justification avoids claiming that the method is automatically superior.

The sampling frame or recruitment pool is identified.

Inclusion and exclusion criteria are stated.

Recruitment method and access route are realistic and ethical.

The sample aligns with the research question, not convenience alone.

Participant burden, consent, and withdrawal rights are considered.

The population is defined by role, setting, geography, institution, dataset, time period, or case boundary.

The dissertation distinguishes target population from accessible sample.

The population definition supports the scope of conclusions.

The chapter avoids making claims about groups not represented in the data.

Context details are included where they affect interpretation.

Probability or non-probability sampling is named correctly.

Purposive, convenience, snowball, stratified, random, or criterion sampling is justified where applicable.

Sample recruitment steps are described in order.

Bias and access limitations are acknowledged.

The strategy is feasible within ethics and deadline constraints.

The proposed sample size matches the data collection method.

Quantitative sample size considers statistical power, expected response rate, or practical constraints where relevant.

Qualitative sample size considers depth, saturation, information power, or case richness.

The chapter explains what will happen if recruitment falls short.

The sample size does not support overgeneralized claims.

The chapter states what data will be collected, from whom or where, and in what order.

Tools, platforms, locations, time frames, and permissions are identified.

Consent, privacy, storage, and withdrawal procedures are included.

Data collection risks and backups are documented.

The plan can be explained clearly to a supervisor or ethics committee.

Primary and secondary data sources are distinguished clearly.

Dataset, archive, participant, document, software, or platform sources are named where possible.

Access permissions and licensing requirements are checked.

Data quality, completeness, and relevance are considered.

The chapter avoids relying on sources that cannot be verified.

Interview, questionnaire, observation, experiment, scraping, archive review, or system logging steps are explained.

Questionnaire or interview flow matches the research questions.

Pilot testing is planned where practical.

The method reduces leading questions, unclear wording, and avoidable participant pressure.

Data storage and naming conventions are planned before collection starts.

Questionnaires, interview guides, coding sheets, observation schedules, rubrics, or test protocols are included or summarized.

Instrument sources are cited if adapted from prior studies.

Questions or measures align with objectives and variables.

Reliability, validity, pilot feedback, or expert review is addressed where needed.

Appendices include instruments when institutional rules require them.

The chapter maps each research question to an analysis method.

Software, coding procedure, statistical test, model, or framework is identified where relevant.

Data cleaning, transformation, coding, or preparation steps are described.

The plan explains how results will be interpreted and reported.

The analysis avoids tests or claims the data cannot support.

Thematic, content, discourse, narrative, grounded theory, case, or framework analysis is named correctly.

Coding stages and theme development are explained.

Researcher reflexivity, audit trail, or triangulation is considered where appropriate.

Illustrative evidence such as quotes, cases, or documents is planned ethically.

The analysis avoids treating isolated comments as universal findings.

Descriptive and inferential analysis steps are separated.

Tests, models, or metrics are suitable for the data type and sample size.

Assumptions, missing data, outliers, and reliability checks are considered.

Effect size, confidence intervals, or practical significance are included where useful.

The chapter avoids overclaiming causality from non-causal designs.

The design names sequential, concurrent, explanatory, exploratory, or embedded logic where relevant.

Each method has its own data collection and analysis path.

Integration points are planned, such as connecting, merging, comparing, or explaining results.

The chapter explains what each strand adds that the other cannot provide alone.

Contradictory findings are treated as evidence to interpret, not as errors to hide.

Validity and reliability are addressed for quantitative instruments, measures, and analysis.

Credibility, transferability, dependability, and confirmability are addressed for qualitative work where appropriate.

Triangulation, pilot testing, member checking, expert review, or audit trail is used only when suitable.

Threats to quality are identified honestly.

The chapter explains how quality checks improve confidence without guaranteeing perfection.

The chapter states whether ethics approval is required and how it will be obtained.

Informed consent, withdrawal, anonymity, and confidentiality are explained.

Sensitive data, vulnerable participants, or institutional permissions are handled carefully.

Data storage, access, retention, and deletion procedures are described.

No participant, dataset, result, citation, or approval is invented.

Limitations explain constraints that may affect evidence or interpretation.

Delimitations explain choices made to keep scope manageable.

Sampling, access, time, measurement, researcher role, and data quality limits are included where relevant.

The chapter explains how limitations will be managed.

The final claims stay within the stated boundaries.

Headings follow the institution's expected methodology structure.

Definitions are concise and linked to the actual dissertation.

Tables or matrices connect questions, objectives, data, and analysis where helpful.

The chapter uses future tense before data collection and past tense after completion consistently.

The methodology can be defended in a viva using the student's own understanding.