Survey Design and Sampling Techniques Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Officer, M&E Manager and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-ME-095

Before you join: Participants should have practical familiarity with programme indicators, basic data collection processes and simple descriptive statistics such as percentages, averages and cross-tabulations.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Poorly designed surveys produce figures that cannot support funding decisions, programme adjustments or credible reporting. Common problems include unclear indicators, biased respondent selection, inadequate sample sizes, weak questionnaire flow, incomplete sampling frames and field teams replacing unavailable respondents. In development and humanitarian programmes, these errors can distort estimates for service coverage, protection risks, livelihoods, food security, beneficiary satisfaction and outcome indicators.

This course teaches participants to design decision-focused surveys and select defensible samples for quantitative and mixed-method monitoring and evaluation. Participants translate results frameworks into measurable questions, define target populations and units of analysis, build sampling frames, choose probability or non-probability methods, calculate sample sizes and document field selection procedures. They also examine questionnaire construction, measurement quality, non-response, weighting, response-rate calculation and reporting of sampling limitations.

Instruction combines short technical briefings with worked cases drawn from household, facility, beneficiary and community surveys. Participants assess sample designs, identify sources of bias, calculate sample requirements, review questionnaire items and make fieldwork decisions under realistic operational constraints. Each participant leaves with a reusable survey design pack containing a sampling plan, sample-size calculation workbook, questionnaire review checklist, field selection protocol and quality-control plan.

The course is suited to M&E personnel, programme staff, researchers and managers who commission, supervise, analyse or use survey findings. It is particularly relevant where teams need to defend survey methods to donors, government counterparts, technical reviewers or internal decision-makers.

What you will be able to do

  • Formulate decision-focused survey questions from indicators, theories of change and reporting requirements
  • Design questionnaires using valid question formats, response categories, skip logic and translation protocols
  • Assess sampling frames for coverage, eligibility, duplication and frame error
  • Select probability and non-probability sampling methods appropriate to survey purposes
  • Calculate sample sizes using precision, confidence, design effect and non-response assumptions
  • Write field selection procedures that prevent substitution and document contact attempts
  • Apply response-rate, weighting and data-quality methods to survey datasets
  • Produce a documented survey design and sampling plan for a live programme question

Benefits of attending

For you

  • Design surveys that answer specific programme and management questions
  • Defend sample choices during donor, partner and technical review meetings
  • Identify biased sampling and weak questionnaire items before field deployment
  • Calculate and explain sample sizes without relying solely on external consultants
  • Strengthen credibility when presenting survey findings and limitations

For your organisation

  • Improves comparability of baseline, monitoring and endline survey results
  • Reduces wasted fieldwork caused by unclear eligibility and respondent selection
  • Produces sample designs that match available budgets, timelines and precision needs
  • Strengthens donor reporting through documented methods and transparent limitations
  • Improves confidence in decisions based on beneficiary, household and facility data

Course modules

10 modules over 5 days

Participants can define a survey purpose, population and measurable questions linked to decisions.

  • Decision-focused survey questions linked to indicators, assumptions and reporting audiences
  • Results framework review identifies measures requiring primary data collection
  • Theory of change pathways converted into measurable survey constructs
  • Defining target populations, units of analysis and survey reference periods
  • Research questions prioritised using decision value, feasibility and respondent burden
  • Indicator definitions aligned with disaggregation requirements and baseline comparability

Participants can construct a questionnaire that produces interpretable and usable responses.

  • Cognitive interviewing detects ambiguous terms, recall errors and socially desirable responses
  • Question formats selected: binary, categorical, Likert, ranking and open-ended
  • Mutually exclusive response categories and exhaustive coding frames
  • Skip logic, filters and validation rules mapped before programming
  • Sensitive questions sequenced using rapport, privacy and indirect questioning
  • Translation and back-translation protocols protect conceptual equivalence across languages

Participants can assess whether survey questions measure intended constructs consistently.

  • Nominal, ordinal, interval and ratio scales matched to analysis plans
  • Operational definitions distinguish constructs, proxies, measures and thresholds
  • Content validity assessed against indicator definitions and subject-matter evidence
  • Reliability checked through internal consistency, test-retest and inter-rater methods
  • Composite indices constructed with transparent scoring, weighting and missing-data rules
  • Pilot findings used to revise wording, order and response options

Participants can prepare and assess a sampling frame for a defined survey population.

  • Sampling frames assessed for coverage, duplication, eligibility and update dates
  • Population boundaries documented for households, individuals, facilities and enterprises
  • Listing procedures create field-ready frames where administrative lists are incomplete
  • Eligibility screening separates sampled units from interviewed respondents
  • Household definitions address compounds, shared dwellings and displaced populations
  • Frame error quantified as undercoverage, overcoverage and duplicate records

Participants can select and document an appropriate probability sampling design.

  • Simple random sampling implemented from numbered, cleaned sampling frames
  • Systematic sampling uses random starts and fixed selection intervals
  • Stratified sampling allocates cases by subgroup size, variance and priority
  • Cluster sampling selects enumeration areas before households or respondents
  • Multistage designs document primary, secondary and ultimate sampling units
  • Probability proportional to size sampling handles unequal cluster populations

Participants can use non-probability methods appropriately and state their limitations clearly.

  • Purposive sampling recruits information-rich cases against explicit selection criteria
  • Quota sampling controls key characteristics without known selection probabilities
  • Convenience and snowball methods assessed for bias and appropriate use
  • Key informant selection distinguishes expertise, representation and gatekeeper influence
  • Qualitative sample adequacy considered through saturation, diversity and analytic purpose
  • Mixed-method designs explain when probability and purposive samples can coexist

Participants can calculate a practical sample size and explain the assumptions used.

  • Cochran's formula estimates initial sample sizes for proportions and precision
  • Finite population correction adjusts samples when populations are small
  • Design effects inflate cluster samples for intracluster correlation
  • Power analysis links detectable differences, significance levels and statistical power
  • Non-response inflation protects achieved sample sizes against refusals and inaccessibility
  • Disproportionate stratification balances subgroup precision, cost and reporting commitments

Participants can establish field procedures that preserve the intended sample design.

  • Selection protocols prevent interviewer substitution and unauthorised respondent replacement
  • Kish grid procedures randomly select one eligible household member
  • Call-back schedules document contact attempts, refusals and unavailable respondents
  • Enumerator briefing covers consent, neutrality, probing and confidentiality
  • Paradata and GPS checks identify implausible duration, location and interview patterns
  • Fieldwork monitoring dashboards track completion, quotas, errors and response rates

Participants can assess survey quality and apply core adjustments to survey estimates.

  • Response rates calculated using AAPOR disposition codes and outcome categories
  • Item nonresponse diagnosed by question, subgroup and interviewer
  • Weighting adjusts unequal selection probabilities, nonresponse and population benchmarks
  • Post-stratification aligns weighted estimates with credible population control totals
  • Data cleaning rules flag duplicates, range violations and logical inconsistencies
  • Variance estimation accounts for strata, clusters and survey weights

Participants can communicate survey findings, uncertainty and methodological limitations responsibly.

  • Confidence intervals communicate sampling uncertainty around estimated proportions and means
  • Cross-tabulations apply denominators, weighting and subgroup suppression rules
  • Disaggregation plans protect confidentiality while meeting equity reporting needs
  • Findings distinguish statistical significance, practical importance and programme relevance
  • Survey methods sections document frames, selection, response and limitations
  • Field-to-report application plan assigns decisions, owners, dates and quality checks

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Survey purpose and indicator specification template
  2. Questionnaire design and review checklist
  3. Sampling frame assessment tool
  4. Sample-size calculation workbook
  5. Field selection and call-back protocol
  6. Survey quality and reporting plan

Who should attend

  • Monitoring and Evaluation Officer — designs and manages programme data collection
  • M&E Manager — assures methodological quality in baselines, endlines and outcome surveys
  • Programme Manager — needs credible evidence for operational and funding decisions
  • Research Officer — develops questionnaires, sampling plans and fieldwork protocols
  • Humanitarian Assessment Specialist — plans rapid and representative population assessments
  • Donor or Government M&E Focal Point — reviews survey methods and reported findings

Skills and tools covered

Survey questionnaire designProbability samplingSample-size calculationFieldwork quality controlSurvey weightingResults interpretation

Tools and standards

AAPOR Standard Definitions, Cochran sample size formula, Kish grid, ODK Collect

How the course runs

Training methodology

The course uses short instructor briefings to introduce survey design principles, followed by case work based on programme monitoring and humanitarian assessment scenarios. Participants work in groups to compare sample designs, review questionnaire items, calculate sample requirements and diagnose fieldwork risks. Simulations address household selection, non-response and quality-control decisions. Facilitated discussion connects each method to participants' own surveys, and application planning converts learning into a practical survey design pack for use after the course.

Requirements and prerequisites

Participants should have practical familiarity with programme indicators, basic data collection processes and simple descriptive statistics such as percentages, averages and cross-tabulations. Experience supporting a baseline, endline, needs assessment, beneficiary survey or routine monitoring process is helpful. Participants should be comfortable working with tables and basic calculations. Prior training in statistics, advanced research methods, statistical software, survey programming or coding is not required. The course explains formulas and design choices in applied terms, while expecting participants to engage with numerical assumptions and methodological trade-offs.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants need familiarity with programme indicators and basic numerical concepts such as percentages and averages. No advanced statistics, coding or specialist survey software experience is required.

Participants should bring a laptop if possible, plus a current questionnaire, indicator list or planned survey question. Course materials include worked examples and calculation templates, so participants without a live project can still participate fully.

It suits M&E officers, programme managers, research staff, assessment specialists and technical managers involved in survey planning or use. It is designed for professionals who need to make or review methodological decisions rather than only collect data.

This course concentrates on questionnaire design, sampling, field selection and the quality of survey estimates. It does not focus on advanced statistical modelling, qualitative interviewing techniques or dashboard development.

Participants can use the templates to plan a baseline, endline, beneficiary feedback survey, needs assessment or facility survey. The methods also help them review consultant proposals and identify risks before data collection begins.

The course provides approximately 32.5 contact hours, with training days running from 08:30 to 16:30. Time is distributed across ten modules, combining instruction, case analysis, calculations, group work and application planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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