
How to write a research proposal: structure and methods
Plan a research proposal with a clear problem, literature gap, objectives, methods, analysis and timeline. Use a practical review checklist.
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Plan a research proposal with a clear problem, literature gap, objectives, methods, analysis and timeline. Use a practical review checklist.
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Review thesis chapters, align questions with methods and findings, track revisions and prepare a defense under your institution’s requirements.
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Compare SPSS, R, Python, Stata, MATLAB and NVivo for research, with educational code examples and guidance on tool selection and analytical reporting.
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Choose research programming tools, organize projects, record dependencies, use version control and validate simulations for reproducible results.
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Review educational citation templates, reconcile in-text citations and reference lists, check DOI records and follow your institution’s style guidance.
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Understand textual matches, quotations, citations and report settings. Learn the limits of similarity scores and a responsible approach to revision.
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Review report components, document versions and tool settings, and use a revision-action table. A similarity percentage alone cannot determine plagiarism.
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Understand original research, review articles and other formats, plan a manuscript and prepare submission. Distinguish journal indexing from article type.
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Plan a review protocol, reproducible search, screening, evidence extraction and PRISMA reporting. Keep reporting quality distinct from study validity.
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Check journal fit, prepare submission files and declarations, respond to each reviewer comment and inspect the final article proof.
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Build a data dictionary, document cleaning and changes, protect confidential information and preserve code and analysis environments for review.
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Use AI responsibly in research: verify sources and calculations, protect confidential information and disclose use under institution and journal policies.
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Build a research search using concepts, synonyms, Boolean logic and suitable databases; preserve a clear search log and manage records without losing provenance.
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Align descriptive, predictive or causal aims with study design, measurement and the unit of analysis; document feasibility and bias before collecting data.
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Preregister questions, outcomes, sampling and confirmatory analyses; disclose prior data access, changes and exploratory work with a transparent decision record.
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Justify sample size using the primary outcome, study design, plausible effect, power or precision; document attrition, clustering and sensitivity scenarios.
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Report magnitude, direction, units and uncertainty alongside significance tests; separate exploratory analyses and avoid treating nonsignificance as proof of no effect.
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Inspect missingness patterns, distinguish assumptions from evidence and justify complete-case or imputation methods; report analytical samples and sensitivity transparently.
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Connect qualitative questions to sampling, researcher reflexivity and documented interpretation; choose reporting guidance that fits the specific methodology.
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Translate constructs into clear questions and answer scales; test wording, order, burden and language equivalence before running the final questionnaire.
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Separate association, prediction and causal effects; define the intervention, timing, confounders and identification assumptions before interpreting a model causally.
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Combine scientific explanation with code; control hidden state, dependencies, data paths and outputs so a colleague can rerun the analytical workflow.
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Document data with metadata, a README, persistent identifiers and access conditions; distinguish FAIR practice from unrestricted sharing or simply uploading a file.
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Check open-access routes, article versions, embargoes, licenses and costs in official policies; free reading does not imply unrestricted reuse or universal repository eligibility.
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Distinguish researcher identifiers from output identifiers; reconcile names, authors, versions and affiliations, and avoid treating an identifier as proof of research quality.
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Choose reporting guidance by study design; check PRISMA 2020, CONSORT and SPIRIT 2025, qualitative guidance and journal-specific requirements.
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Match the idea to the current funding call and eligibility rules; prepare coherent aims, resources, justified budgets, data plans and internal submission deadlines.
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Evaluate questions, design, sampling, measurement and analysis; distinguish reporting quality, risk of bias, relevance and the publication’s correction or withdrawal status.
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Plan Stage 1, in-principle acceptance, protocol execution and Stage 2 reporting with a hypothetical thesis case and an editable workbook.
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Document prior knowledge, sample rules, models and amendments when data already exist; work through a secondary-data example and decision log.
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Record qualitative sampling, researcher position, analysis and justified changes without imposing an unsuitable fixed statistical hypothesis.
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Map study records, protocol, analysis, materials, data, code and reporting to TOP 2025 disclosure, sharing and citation, and certification levels.
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Define defensible choices and dependencies, compare analytical sensitivity and rerun a six-path synthetic example with data, Python and outputs.
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Connect manuscript numbers to computation, preserve clean execution and data releases, and review RTL, Word and PDF formatting with a sample project.
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Build a coherent thesis around published work, connecting a central question, linking chapters, author contributions and an integrated discussion.
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Compare preprints, editorial selection, public assessments, revisions and final versions, with an original reviewer-response example.
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Document data generation, dictionaries, validation, access conditions and a reuse example for a data-focused paper.
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Assess research purpose, public development history, tests, documentation, licensing and AI disclosure for research software.
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Distinguish the accepted manuscript from the publisher version and check funder, institution, journal and deposit permissions before signing.
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Why a large p-value does not prove no effect: interpret equivalence bounds, standard errors and confidence intervals with executable examples.
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Separate populations, settings, stimuli, time and mechanisms and write an explicit scope-of-generalization statement.
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Design surveillance, continuation criteria, versioning and update triggers using a worked monitoring and stopping log.
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Specify CHART scope, model versions, prompts, variable outputs, human assessment and harm controls in health-advice chatbot research.
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Distinguish SPIRIT protocols from CONSORT randomized-trial reports and work with current versions and an evidence-location matrix.
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Distinguish a familiar name, a published proposal and official endorsement using a documented 2026 case and a provenance-check workflow.
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