Analysis problems often begin before any test or code is run. A readiness review protects the connection between the research questions, collected evidence, coding decisions, and claims the final report can support.
1. Start with the question and analysis plan
List each research question or hypothesis and identify the exact evidence needed to answer it. For quantitative work, map constructs, variables, levels of measurement, and planned tests. For qualitative work, clarify the analytical approach, role of theory, unit of meaning, and expected audit trail.
- Every question has an evidence source
- The proposed technique fits the design
- Decision rules are written before results are interpreted
2. Protect data meaning
A clean spreadsheet can still be analytically wrong. Check codebooks, value labels, reverse-scored items, units, date formats, skip patterns, derived variables, duplicate records, and the relationship among files.
- Raw data is preserved separately
- Coding rules are documented
- Personally identifying fields are removed where possible
3. Review quality and missingness
Quantitative data requires checks for missing values, impossible values, outliers, distributional assumptions, scale construction, and sample limitations. Qualitative material requires complete transcripts, anonymisation, contextual notes, and a transparent record of exclusions or corrections.
- Corrections never overwrite the raw source
- Missingness or exclusions are reported
- Quality problems are distinguished from inconvenient results
4. Keep interpretation within the design
Statistical significance does not establish importance, causation, or generalisability by itself. A qualitative theme is not credible merely because it sounds persuasive. Interpret effect size, uncertainty, contrary evidence, context, researcher decisions, and limitations.
- Claims match the sampling and design
- Tables or themes answer the research questions
- Alternative explanations and limitations remain visible
5. Prepare reproducible outputs
Save syntax, scripts, project files, coding frameworks, output files, version notes, and decision logs. The final thesis should make the analysis understandable without requiring the reader to reverse-engineer undocumented steps.
- Output names and versions are clear
- Reported values can be traced to analysis output
- The discussion distinguishes results from interpretation
Working audit
Usable data-analysis preparation checklist
Use this audit before choosing a statistical test, importing a file into software, or starting formal coding. Mark an item complete only when another person could verify it from the files or documentation—not from memory.
Study alignment
- Each research question or hypothesis maps to a named evidence source and intended output.
- The sampling or recruitment route, inclusion criteria, exclusions, and final analysis unit are documented.
- The final instrument, interview guide, observation schedule, or secondary-data specification is available with its version date.
- Consent, ethics, data-use, and institutional restrictions that affect storage, sharing, or reporting are recorded.
File and documentation control
- A read-only raw master is preserved; cleaning and coding occur in a dated working copy.
- Anonymous, stable IDs connect permitted files without using names, email addresses, phone numbers, or student numbers in the analysis copy.
- A codebook or README explains each file, variable or field, unit, label, category code, missing-value code, and derived measure.
- Every correction, exclusion, merge, recode, and scoring decision is logged so it can be reproduced.
Quantitative handoff
- Rows and columns match the intended unit of analysis; repeated observations or multi-level structures are explicitly identified.
- Ranges, units, category labels, reverse-scored items, scale rules, skip patterns, and derived-variable formulas are confirmed.
- Missing values are explicit and distinct from true zeros; duplicate IDs, impossible values, and inconsistent dates have been reviewed.
- The draft analysis plan maps questions to variables, comparisons or models, assumptions, and the tables or figures needed.
Qualitative handoff
- Transcripts are complete enough for the agreed analysis, with consistent speaker labels and useful page, line, or timestamp references.
- Direct and relevant indirect identifiers are reviewed under the approved consent and ethics conditions before files are shared.
- A metadata sheet records anonymous ID, source type, collection round or date, and only the contextual fields permitted for analysis.
- Corrections, exclusions, translations, transcription conventions, initial coding framework, and reflexive decisions are documented.
Minimum handoff bundle
Keep these items together before you request analysis or transfer the project to another researcher. Share only the files and contextual details permitted by the applicable consent, ethics approval, data-use agreement, and institutional rules.
- Preserved raw source plus a separate, dated analysis-ready copy
- Codebook, data dictionary, or qualitative metadata and transcription-convention sheet
- Final questionnaire, interview guide, coding frame, or data-source specification
- Short analysis plan linking each question to evidence, method, and intended output
- Decision log covering cleaning, recoding, exclusions, translations, scoring, and unresolved issues
- A written note describing the consent, ethics, privacy, and institutional limits relevant to the files
Choose the right next step
Match the remaining problem to relevant support
Readiness comes before software. If a blocker remains, resolve the design, documentation, or privacy decision first. If the files are ready, choose the route that matches the evidence and research question.
Use when variables, scoring rules, design and questions are sufficiently defined for statistical planning in SPSS, AMOS or SmartPLS.
Review quantitative analysis support Qualitative data analysisUse when transcripts or other qualitative material, context, analytical approach and audit-trail requirements are clear.
Review qualitative analysis support Methodology supportStart here when the research question, sampling logic, measures, coding approach or analysis route still needs to be resolved.
Review methodology support Analysis and software coursesCompare the SPSS, AMOS, SmartPLS and thematic-analysis courses when you need a structured self-study route.
Compare analysis and software coursesBefore sharing research material: remove unnecessary identifiers, confirm what your participants and institution permit, and send only the minimum files needed. Review the institute's academic-integrity boundaries before requesting project-specific work.
Apply the checklist to your project.
Download the printable resource, review the related service, or send a structured enquiry with your programme, current stage, exact issue, and deadline.