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monitoringevaluationstudio.com/resources/reference/topics/sampling

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  1. M&E Library
  2. Topics
  3. Sampling

Sampling

How many people to survey, how to select them, and how to avoid the mistakes that invalidate your data. Sample size calculation, sampling methods, design effect, and practical guidance.

How Do I Choose?

Common Sampling Mistakes in M&EThe eight sampling mistakes that undermine M&E data quality: wrong frames, ignored design effect, field substitution errors, and analysis overclaims.How to Choose Sample Size for M&E: The Five Factors, with Worked ExamplesA practical guide to sample size for program evaluations, with rules of thumb, worked examples, and budget-statistics tradeoffs.Probability vs Non-Probability Sampling: When to Use EachProbability vs non-probability sampling in M&E: when each approach is valid, which method fits your context, and five common mistakes that invalidate findings.Cluster Sampling vs Stratified SamplingCluster sampling saves money when populations are spread out. Stratified sampling ensures subgroup comparisons. When to use each.

Overviews (3)

Baseline DesignA structured approach to collecting initial condition data that directly informs project decisions, minimizes burden, and enables valid comparison with endline measurements.DisaggregationThe breakdown of aggregate data by sub-group characteristics, such as sex, age, location, or vulnerability status, to reveal inequities and differences in program reach and outcomes.Sampling MethodsSystematic approaches for selecting a subset of a population to represent the whole, balancing statistical validity with practical constraints.

Quick Reference (4)

Census vs Sample: When to Use Each in M&ECluster SamplingPurposive SamplingRandom Sampling

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