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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