Research Data Collection and Storytelling Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Officers, Programme Managers and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-ME-156

Before you join: Participants should have practical familiarity with programme monitoring, surveys, assessments, evaluations or donor reporting.

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

Development and humanitarian teams must turn field evidence into decisions while working with limited access, diverse languages, protection risks and demanding reporting schedules. Weak questions, poorly specified indicators, convenience samples or unreliable digital forms can produce data that appears precise but cannot support funding decisions, programme adaptation or accountability. Even sound analysis can be ignored when reports bury the conclusion, charts mislead, or recommendations lack an owner and action. This course addresses the full chain from decision question through collection, analysis, visual communication and use.

Participants learn to design evaluation questions, results chains, indicators and data collection instruments; select defensible samples; manage enumerator-led and digital collection; and protect respondents through informed consent, data minimisation and safeguarding protocols. They use qualitative coding, descriptive statistics, disaggregation and triangulation to assess evidence strength. The course then focuses on converting findings into clear charts, concise data stories, decision briefs, dashboards and recommendation matrices suitable for programme managers, donors, government counterparts and community stakeholders.

Teaching combines short technical briefings with realistic development and humanitarian cases, including household surveys, key informant interviews, service-monitoring data and evaluation findings. Participants work through instrument design, data-quality checks, chart critique and stakeholder communication decisions. They leave with a reusable monitoring and evaluation data-to-storytelling pack: an evaluation matrix, instrument quality checklist, analysis plan, visualisation rules and reporting templates. The five-day instructor-led course is available in classrooms in 50+ cities or live online, with a certificate of completion.

The course is particularly suited to professionals who already contribute to monitoring, evaluation, research, programme management or reporting and need stronger control over how evidence is collected, interpreted and communicated.

What you will be able to do

  • Design an evaluation matrix linking decision questions, indicators, methods and sources
  • Construct questionnaires, interview guides and observation checklists using tested question-design methods
  • Select probability and non-probability sampling approaches appropriate to research objectives
  • Apply fieldwork quality-assurance controls to digital and enumerator-led data collection
  • Assess consent, safeguarding, privacy and disclosure risks using a data protection approach
  • Analyse qualitative and quantitative evidence through coding, disaggregation and triangulation
  • Create decision-focused charts using sound visualisation and accessibility principles
  • Produce evidence-based data stories, recommendations and communication products for defined audiences

Benefits of attending

For you

  • Greater confidence challenging weak indicators, samples and unsupported claims
  • A repeatable method for designing field-ready data collection instruments
  • Stronger judgement about evidence quality, uncertainty and disaggregation
  • Practical chart and briefing techniques for presenting findings to decision-makers
  • Reusable templates for future assessments, evaluations and donor reporting

For your organisation

  • More reliable monitoring and evaluation data for programme and funding decisions
  • Fewer fieldwork errors through clearer instruments, checks and enumerator controls
  • Reduced privacy and safeguarding exposure during respondent data collection
  • Better use of disaggregated evidence to identify underserved groups and delivery gaps
  • Clearer reports and dashboards that convert findings into assigned management actions

Course modules

10 modules over 5 days

Participants can frame decision-focused evaluation questions and connect them to measurable evidence requirements.

  • Decision-focused research questions linked to programme theories and management decisions
  • OECD DAC evaluation criteria translated into answerable enquiry questions
  • Results chains distinguishing activities, outputs, outcomes and intended impacts
  • Logframe assumptions tested against plausible causal pathways and external factors
  • SMART indicators specified with unit, direction, disaggregation and frequency
  • Indicator reference sheets defining numerator, denominator, source and calculation
  • Evaluation matrix connecting questions, indicators, methods, sources and responsibilities

Participants can develop data collection instruments that generate valid, usable and ethically appropriate responses.

  • Cognitive interviewing identifies ambiguous wording, recall burdens and response options
  • DHS-style question wording adapted without changing construct definitions
  • Questionnaire flow uses screening, skip logic and sensitive-question placement
  • Likert scales balanced for direction, anchors and neutral response choices
  • Observation checklists convert technical standards into observable verification criteria
  • Key informant interview guides use probes, funnels and neutral prompts
  • Translation and back-translation preserve meaning across multilingual instruments

Participants can select and document sampling approaches that match research aims, population characteristics and operational constraints.

  • Target populations, sampling frames and eligibility criteria documented explicitly
  • Probability sampling selected through simple random, systematic and cluster designs
  • Stratification protects estimates for sex, age, disability and location groups
  • Sample-size calculations use confidence level, margin of error and design effect
  • Purposive, quota and snowball approaches matched to qualitative research aims
  • Nonresponse bias assessed through call records, substitutions and response rates
  • Fieldwork routing plans balance coverage, cost, safety and interviewer workload

Participants can configure practical controls for reliable digital data collection and daily fieldwork supervision.

  • KoboToolbox forms configured with constraints, relevance rules and required fields
  • Unique identifiers support repeat visits, panel matching and duplicate detection
  • Daily field dashboards monitor completions, duration, GPS and missing values
  • Back-check protocols verify interviews, observations and enumerator conduct
  • Paradata flags straight-lining, speeders, unusual timestamps and coordinate anomalies
  • Codebooks record variable labels, value codes, formats and derivations
  • Version control documents instrument amendments and dataset releases

Participants can identify and manage ethical, safeguarding and privacy risks throughout the data lifecycle.

  • Do no harm assessments identify collection risks and mitigation measures
  • Informed consent scripts cover purpose, voluntariness, risks and withdrawal
  • Data minimisation limits personal data to justified analytical needs
  • Data Protection Impact Assessments document risks, controls and residual exposure
  • Safeguarding referral protocols protect respondents disclosing violence or exploitation
  • De-identification applies suppression, aggregation, pseudonymisation and disclosure review
  • Role-based access controls govern storage, transfer, retention and deletion

Participants can organise qualitative evidence into defensible themes, comparisons and findings.

  • Framework analysis structures coded evidence by questions, cases and themes
  • Codebooks define codes, inclusion rules, exclusions and illustrative quotations
  • Inductive and deductive coding distinguish emergent themes from theory-led categories
  • Inter-coder agreement checks interpretation before full transcript coding
  • Matrix displays compare perspectives by gender, location, stakeholder and period
  • Negative case analysis tests explanations against disconfirming evidence
  • Analytic memos link findings, evidence strength, limitations and implications

Participants can interpret common monitoring and survey data while communicating uncertainty and limitations accurately.

  • Data cleaning logs record recodes, exclusions, imputations and validation decisions
  • Frequency tables expose outliers, impossible values and missing-data patterns
  • Cross-tabulations disaggregate outcomes by sex, age, location and vulnerability
  • Means, medians and proportions selected for appropriate variable types
  • Confidence intervals communicate sampling uncertainty around survey estimates
  • Baseline-endline comparisons separate observed change from causal attribution claims
  • Triangulation compares survey, administrative and qualitative evidence systematically

Participants can select and design charts that communicate findings accurately, accessibly and efficiently.

  • Chart selection matches comparison, distribution, composition, trend and relationship questions
  • Bar charts use sorted categories, zero baselines and direct labels
  • Line charts show time-series intervals, breaks and annotation of shocks
  • Dot plots reduce clutter when comparing many groups or indicators
  • Disaggregated visuals avoid averages that conceal inequity or protection risks
  • Colour palettes meet contrast requirements and avoid misleading semantic cues
  • Tables highlight decision-relevant values through alignment, rounding and hierarchy
  • Titles state the finding rather than merely naming the indicator

Participants can shape evidence into audience-specific narratives that support informed decisions without overstating findings.

  • Message maps connect audience decisions, evidence, recommendation and supporting detail
  • SCQA structure frames situation, complication, question and answer succinctly
  • Data stories distinguish findings, interpretation, limitations and recommendations
  • Claim-evidence-reasoning chains prevent overstatement of weak or partial results
  • Audience segmentation adjusts language, granularity, format and call to action
  • Uncertainty statements explain confidence, caveats, missing data and assumptions
  • Ethical storytelling protects dignity, consent and contextual accuracy

Participants can create reporting products that assign evidence-based actions and support follow-through after dissemination.

  • Evaluation report architecture follows executive summary, methods, findings and recommendations
  • Recommendation matrices assign actions, owners, deadlines, resources and evidence
  • Results brief templates present one decision, three findings and implications
  • Dashboard wireframes prioritise questions, indicators, filters and update cadence
  • Stakeholder review plans establish validation, challenge and sign-off stages
  • Dissemination plans match products to decision forums and information needs
  • Personal application plans schedule instrument, analysis and communication improvements

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

What you take back to work

  1. Evaluation matrix template
  2. Indicator reference sheet
  3. Data collection instrument quality checklist
  4. Fieldwork quality-assurance plan
  5. Evidence analysis and triangulation model
  6. Data story and recommendation matrix template

Who should attend

  • Monitoring and Evaluation Officers — need defensible systems for gathering and communicating programme evidence
  • Programme Managers — need usable findings to adapt delivery, resources and implementation priorities
  • Research Officers — need stronger methods for instruments, sampling, analysis and reporting
  • MEAL Managers — need consistent quality controls across monitoring, accountability and learning processes
  • Humanitarian Information Management Staff — need clear visual products from operational and assessment data
  • Donor Reporting and Grants Staff — need credible evidence and concise results narratives for reports

Skills and tools covered

Research designSampling strategyData quality assuranceEthical data collectionEvidence analysisData storytelling

Tools and standards

KoboToolbox, OECD DAC evaluation criteria, Theory of Change, Data Protection Impact Assessment

How the course runs

Training methodology

The course uses short instructor briefings to establish methods, followed by case work drawn from development programmes and humanitarian responses. Participants review research questions, critique instruments, make sampling decisions, inspect data-quality signals and interpret mixed evidence. Group exercises focus on chart selection, evidence claims and audience-specific communication. Simulations recreate fieldwork and reporting decisions under time, access and protection constraints. Each participant develops an application plan that identifies improvements to an active or upcoming monitoring, assessment or evaluation assignment.

Requirements and prerequisites

Participants should have practical familiarity with programme monitoring, surveys, assessments, evaluations or donor reporting. They should be able to interpret basic tables and percentages and understand the difference between an activity, output and outcome. Experience using spreadsheets is helpful because examples include data cleaning, frequency tables and charts. Participants do not need advanced statistics, coding skills, specialist research software expertise or prior experience with KoboToolbox. The course explains methods sufficiently for professionals who use research findings but are not full-time researchers.

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

You should understand basic monitoring, programme reporting or research processes and be comfortable interpreting simple tables and percentages. Advanced statistics, coding and specialist software experience are not required.

Bring a laptop if possible, along with a current or planned monitoring, assessment, evaluation or reporting assignment. You may use it to shape your application plan and adapt the course templates to your work.

It is designed for M&E, MEAL, research, programme, information management and donor reporting professionals. It is most useful for staff who collect, commission, review or communicate evidence rather than solely manage databases.

This course covers the full evidence chain: questions, instruments, sampling, ethics, quality assurance, analysis, visualisation and decision communication. It focuses on practical judgement for development and humanitarian evidence rather than advanced statistical modelling or a single software platform.

Participants can use the evaluation matrix, instrument checklist, fieldwork controls and recommendation matrix on current assignments. The methods are applicable to needs assessments, routine monitoring, evaluations, beneficiary feedback and donor reporting.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The five days progress from research design and data collection through analysis, visualisation, storytelling and an individual application plan.

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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