Analysis and Evaluation 50
Evaluation designs, matrices, terms of reference, and analyzing what came back.
Evaluation designs, matrices, terms of reference, and analyzing what came back.
Build an evaluation matrix linking evaluation questions to criteria, indicators, data sources, and methods.
Write terms of reference for commissioning an external evaluation, including scope, questions, and methodology.
Get feedback on your evaluation methodology, questions, sampling, and analysis plan before fieldwork.
Get help calculating frequencies, means, cross-tabulations, and interpreting patterns in your survey results.
Get help identifying themes, extracting key quotes, and summarizing findings from interview or FGD transcripts.
Create a codebook and systematically code interview or focus group data into themes and sub-themes.
Pull out the main findings, data points, and recommendations from a report or evaluation.
Analyze changes between baseline and endline data, identify significant shifts, and interpret what they mean.
Find trends, outliers, and patterns across multiple data sources or reporting periods.
Plan a mid-term evaluation to check program progress, relevance, and early outcomes.
Understand the differences between evaluation methodologies and when to use each one.
Get step-by-step guidance on how to analyze your specific type of M&E data.
Create a Most Significant Change story collection and selection guide with interview protocols, selection criteria, and analysis methods.
Analyze program monitoring data through a conflict sensitivity and Do No Harm lens, identifying how the program may be interacting with conflict dynamics and recommending adjustments.
Create a Value for Money (VfM) assessment framework using the 4Es model (Economy, Efficiency, Effectiveness, Equity), with indicators, benchmarks, and reporting templates.
Analyze program cost-effectiveness by comparing unit costs across interventions, sites, or time periods, with benchmarking against sector standards and actionable efficiency recommendations.
Build a contribution analysis framework with contribution claims, evidence assessment criteria, and systematic testing of alternative explanations for observed outcomes.
Build a complete Most Significant Change (MSC) data collection and selection protocol, including story collection guides, selection criteria, and domain definitions.
Build an Outcome Harvesting protocol with outcome description templates, substantiation questions, verification procedures, and analysis frameworks.
Design a quasi-experimental evaluation with matching strategy, comparison group selection, difference-in-differences analysis plan, and threats to validity assessment.
Analyze qualitative data using a structured coding framework that combines deductive codes from your theory of change with inductive codes emerging from the data, culminating in thematic analysis.
Design a participatory evaluation approach that engages beneficiaries and stakeholders as co-evaluators, with inclusive methods, power analysis, and capacity building components.
Analyze program data using a realist evaluation approach to develop and refine Context-Mechanism-Outcome (CMO) configurations that explain what works, for whom, and under what circumstances.
Create a process tracing protocol for causal inference in single-case or small-n evaluations, with hypothesis formulation, evidence tests, and Bayesian confidence updating.