Data Analysis and Reporting

Turn raw data into understandable findings through cleaning, derivation, weighting, analysis, verification, visualization, and writing.

• Data require cleaning or coding.
• A project needs an analysis plan, tables, or tests.
• Findings need responsible interpretation and writing.
• An organization needs a dashboard or visual presentation.
• Figures and reports require independent review.

• Inspect structure, completeness, and impossible values.
• Prepare dictionaries and code open-ended responses.
• Derive indicators and weights and merge files.
• Conduct descriptive, comparative, inferential, or qualitative analysis.
• Create tables, charts, and summaries.
• Review consistency among narrative, tables, and figures.

• Survey analysis accounting for sample design.
• Regression, time-series, or clustering where appropriate.
• Thematic qualitative analysis or coding framework.
• Triangulation of quantitative, qualitative, and operational data.
• Sensitivity analysis and alternative definitions.

The study population, unit of analysis, and access frame are defined before implementation. Sampling is selected according to purpose, inference needs, resources, and risk. “Representative” is used only where the design supports it, and exclusions, nonresponse, and adjustments are documented.

• Review the question and instrument before fieldwork.
• Pilot or pretest where the assignment permits.
• Train researchers and standardize instructions.
• Monitor completion and apply consistency, duration, and location checks where appropriate.
• Use clear consent, voluntary participation, and harm minimization.
• Separate identifying information and restrict access.
• Document deviations, problems, and corrective action.

• Clean data and processing log.
• Analysis plan, tables, and reproducible outputs.
• Technical report, executive brief, or presentation.
• Dashboard or visualization package.
• Methodology appendix and codebook.
• Analysis code where agreed.

• Analysis cannot repair every design or data weakness.
• Statistical significance does not alone establish practical importance.
• Models or categories are not selected merely for preferred results.
• Missing values are distinguished from zero and not applicable.
• Data description is separated from causal inference.

Work begins with the question, intended audience, decision to be supported, relevant areas and groups, schedule, and budget. The Center then proposes a feasible scope, methodology, and deliverables.

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• How should data be cleaned and rules documented?
• Which indicators and tables answer the questions?
• How should weights and uncertainty be calculated?
• How should quantitative and qualitative evidence be integrated?
• How should findings become an understandable report, presentation, and visual output?

An inception discussion confirms purpose, questions, population, geography, users, deliverables, and constraints. An inception note or protocol may define design, roles, schedule, quality plan, risk, and change rules before implementation.

• Variable dictionary and cleaning and coding rules.
• Analysis plan and output specification table.
• Reproducible code or processing record where the assignment permits.
• Cross-checks among tables, narrative, and charts.
• Distinction among data findings, interpretation, and recommendation.

• Review concepts and definitions before item wording.
• Use language appropriate to context and collection mode.
• Review translation for meaning rather than literal equivalence alone.
• Conduct logical and technical testing and, where needed, a pilot or cognitive interviews.
• Freeze the approved version and document subsequent changes.

Tables, indicators, comparisons, quotations, and qualitative themes are defined before collection closes where possible. Descriptive, exploratory, and explanatory analysis are distinguished, and small cells, missing values, weights, multiple tests, and uncertainty are reviewed.

The commissioner may review factual accuracy, contextual understanding, and compliance with scope. It may not select which valid findings appear or require a correct figure or material limitation to be removed. Comments and changes affecting method or meaning are recorded.

The agreement defines ownership of instruments, data, code, reports, and publication rights. Identifiable or restricted data are shared only for a defined purpose under clear authority and safeguards. Delivery of a report does not by itself authorize redistribution of the original dataset.

Data Collection and Fieldwork Management · Data Analysis and Reporting · Methodological Advisory