Research Design and Data Analysis Training Course

Duration 10 days 18 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Officers, Programme Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-ME-125

Before you join: Participants should understand the purpose of monitoring and evaluation and have experience reading programme documents, indicator tables or research reports.

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 programmes require evidence that is credible enough to guide funding, improve delivery and account to affected populations. Weak research design can produce misleading findings: indicators may not measure intended change, samples may exclude priority groups, tools may introduce bias, and poorly managed datasets can undermine analysis. Teams need practical methods to define answerable questions, collect defensible data and communicate findings that support decisions.

This course develops the full workflow from programme theory and evaluation questions through sampling, instrument design, fieldwork planning, data processing, statistical analysis and reporting. Participants work with mixed-methods designs, probability and non-probability sampling, questionnaires, key informant interviews, focus group discussions, KoboToolbox forms, Excel-based data preparation and introductory R analysis. They apply OECD DAC evaluation criteria, ethical protocols, data-quality checks and clear visual reporting to development and humanitarian cases.

Instruction combines short technical briefings with worked cases, group design tasks, dataset reviews, analysis simulations and structured peer critique. Participants progressively build a research and evaluation package for a realistic programme scenario, then refine it using feedback on feasibility, validity, ethics and data use. They leave with a completed research design portfolio containing a theory of change, evaluation matrix, sampling approach, data-collection instruments, data-management plan and analysis plan.

The course suits monitoring and evaluation staff, programme managers, researchers, assessment teams and technical specialists who need to commission, conduct or review applied studies. It is particularly relevant to teams working on livelihoods, health, education, protection, food security, governance and humanitarian response programmes.

What you will be able to do

  • Develop a theory of change and evaluation matrix linking questions, indicators, methods and decision uses
  • Formulate research questions, hypotheses and measurable indicators using SMART and OECD DAC criteria
  • Select probability and non-probability sampling methods and calculate defensible sample sizes
  • Design questionnaires, interview guides and focus group protocols that reduce measurement bias
  • Configure mobile data-collection forms and fieldwork controls using KoboToolbox
  • Prepare, clean, document and protect quantitative and qualitative research datasets
  • Conduct descriptive, cross-tabulation, basic inferential and qualitative thematic analyses
  • Produce and present a complete research design and data analysis plan for programme decisions

Benefits of attending

For you

  • Design evaluation studies that stand up to technical and donor review
  • Write clearer indicators, research questions and analysis plans
  • Reduce fieldwork errors through structured data-quality controls
  • Interpret quantitative and qualitative findings with appropriate caution
  • Present evidence-based recommendations with traceable supporting data

For your organisation

  • More credible baseline, endline, needs assessment and evaluation studies
  • Stronger evidence for programme adaptation, donor reporting and resource allocation
  • Reduced data loss, duplication and preventable fieldwork quality failures
  • Greater consistency across teams using shared research design templates
  • Improved protection of participant data and ethical research practice

Course modules

18 modules over 10 days

Participants can distinguish research, monitoring and evaluation purposes and define an appropriate evidence approach.

  • Research, monitoring and evaluation purposes distinguished through decision-use requirements
  • Programme theory linking inputs, activities, outputs, outcomes and intended impact
  • Results chains tested for assumptions, risks and causal plausibility
  • Evaluation timing aligned with baseline, midline, endline and post-project decisions
  • Formative, process, outcome and impact evaluations compared against intended uses
  • Stakeholder analysis identifying decision-makers, implementers, communities, donors and data users

Participants can formulate focused research questions and criteria that direct data collection and analysis.

  • Research questions narrowed by population, intervention, outcome and decision
  • SMART evaluation questions converted into measurable evidence requirements
  • OECD DAC criteria applied to relevance, coherence, effectiveness and impact
  • Hypotheses specified with expected relationships between intervention and outcome variables
  • Assumptions and rival explanations documented before selecting research methods
  • Question prioritisation matrix balancing usefulness, feasibility, ethics and available resources

Participants can build a theory of change and specify indicators with usable measurement definitions.

  • Theory of change diagrams connecting activities to intermediate and final outcomes
  • Outcome pathways tested against contextual assumptions and external influencing factors
  • Indicator reference sheets defining numerators, denominators, disaggregation and frequency
  • Direct and proxy indicators assessed for validity, reliability and sensitivity
  • Disaggregation plans covering sex, age, disability, location and displacement status
  • Targets established from baselines, programme standards and realistic delivery assumptions

Participants can select a research design that fits the question, context and evidence standard.

  • Cross-sectional, longitudinal and panel designs matched to evaluation questions
  • Experimental and quasi-experimental designs assessed for attribution claims
  • Pretest-posttest comparisons examined for history, maturation and selection threats
  • Mixed-methods sequencing combining quantitative breadth with qualitative explanatory evidence
  • Case study design defining bounded cases, evidence sources and comparison logic
  • Method selection justified through validity, feasibility, cost and safeguarding considerations

Participants can develop a sampling strategy and explain its implications for inference and bias.

  • Target populations, study populations and sampling frames clearly differentiated
  • Simple random, systematic and stratified sampling selected for survey designs
  • Cluster sampling planned for dispersed populations and fieldwork constraints
  • Purposive, quota and snowball sampling used with stated limitations
  • Sample size calculations incorporating confidence level, precision and design effect
  • Non-response adjustments planned through replacement rules and response-rate monitoring

Participants can design survey questionnaires that produce valid, reliable and analysable data.

  • Question wording avoids double-barrelled, leading and socially desirable responses
  • Response scales selected using nominal, ordinal, interval and ratio measurement
  • Recall periods set to reduce memory error and reporting bias
  • Skip logic and validation rules specified before digital form configuration
  • Translation and back-translation procedures preserve item meaning across languages
  • Cognitive testing identifies misunderstood terms, concepts and response categories

Participants can prepare qualitative protocols that generate credible and ethically obtained evidence.

  • Semi-structured interview guides organised around research questions and probes
  • Focus group discussion protocols define participant composition and facilitation roles
  • Key informant selection criteria document expertise, perspective and potential bias
  • Observation checklists capture behaviours, conditions, interactions and service quality
  • Probing techniques clarify examples, meanings, contradictions and causal explanations
  • Saturation decisions recorded using emerging themes and information redundancy

Participants can apply ethical safeguards to research involving programme participants and affected populations.

  • Informed consent scripts explain purpose, procedures, risks and voluntary participation
  • Assent and caregiver consent procedures protect children and adolescents
  • Do-no-harm assessment identifies distress, retaliation and disclosure risks
  • Referral pathways prepared for protection, health and psychosocial support concerns
  • Confidentiality protocols separate identifiers from research responses and recordings
  • Ethics review documentation records approvals, amendments and incident reporting

Participants can prepare an operational fieldwork plan with clear roles, controls and contingencies.

  • Fieldwork plans schedule sites, teams, transport, permissions and daily targets
  • Enumerator selection criteria assess language, local knowledge and safeguarding suitability
  • Training agendas cover protocols, role boundaries, consent and tool administration
  • Inter-observer reliability checks compare enumerator ratings against standard scenarios
  • Supervisor checklists monitor interviews, spot checks and daily team debriefs
  • Security and access contingencies address movement restrictions and communication failures

Participants can configure KoboToolbox forms and controls for accurate, secure field data collection.

  • XLSForm structure uses survey, choices and settings worksheets correctly
  • KoboToolbox questions configured with labels, hints, constraints and required fields
  • Relevance expressions control skip patterns and respondent-specific question paths
  • Calculation fields derive ages, totals, scores and consistency checks
  • Metadata captured for timestamps, enumerators, locations and device identifiers
  • Form testing checks validation, translations, submissions and offline collection behaviour

Participants can prepare a documented, analysis-ready dataset while protecting sensitive information.

  • Data-management plans assign ownership, access rights, retention and disposal rules
  • Unique identifiers link records while limiting exposure of personal information
  • Codebooks document variable names, labels, values, units and missing-data codes
  • Range, logic and duplicate checks identify implausible or inconsistent records
  • Missing data patterns assessed before deletion, imputation or sensitivity analysis
  • Version control records raw files, cleaned datasets, scripts and change logs

Participants can summarise survey data and create decision-focused tables and charts.

  • Frequency tables summarise categorical responses, missing values and denominator choices
  • Measures of central tendency compared using mean, median and mode
  • Spread described through range, interquartile range, variance and standard deviation
  • Cross-tabulations examine differences by sex, age, location and programme group
  • Excel pivot tables produce reproducible summaries from cleaned survey datasets
  • Chart selection matches comparison, distribution, composition and trend questions

Participants can conduct and interpret basic inferential tests without overstating conclusions.

  • Confidence intervals express uncertainty around proportions, means and estimated differences
  • Chi-square tests assess association between categorical variables and groups
  • T-tests compare group means under stated assumptions and limitations
  • Correlation coefficients distinguish linear association from causal explanation
  • P-values interpreted alongside effect sizes, confidence intervals and study design
  • R scripts import data, generate summaries and reproduce selected analyses

Participants can code qualitative material and develop evidence-based themes and explanations.

  • Transcription standards preserve relevant language, pauses, context and anonymisation
  • Initial coding applies descriptive, in-vivo and process codes consistently
  • Codebooks define code meanings, inclusion criteria, exclusions and examples
  • Thematic analysis groups codes into patterns, mechanisms and divergent cases
  • Framework matrices compare themes across locations, groups and respondent categories
  • Analytic memos record interpretations, evidence gaps and researcher reflexivity

Participants can assess evidence quality and integrate findings from multiple sources responsibly.

  • Triangulation compares survey, interview, document and observation evidence
  • Construct validity tested against indicator definitions and programme theory
  • Internal validity threats assessed through confounding, selection and measurement error
  • Reliability checks examine consistency across enumerators, coders and repeated measures
  • Data-quality assessment rates completeness, timeliness, accuracy, integrity and confidentiality
  • Contradictory findings analysed through disaggregation, source appraisal and follow-up questions

Participants can prepare clear findings and recommendations linked directly to evidence and decisions.

  • Finding statements distinguish observed evidence, interpretation and recommendation
  • Evidence tables link questions, methods, results, limitations and confidence
  • Recommendations specify actions, owners, timing, resources and implementation conditions
  • Executive summaries present priority findings for senior decision-makers concisely
  • Data visualisations use titles, denominators, source notes and accessible labels
  • Management response matrices record acceptance, actions and follow-up responsibilities

Participants can prepare terms of reference and assess the quality of commissioned research.

  • Terms of reference specify purpose, questions, scope, deliverables and governance
  • Technical proposals assessed against design quality, ethics, staffing and feasibility
  • Inception reports reviewed for methodology, sampling, tools and workplan coherence
  • Fieldwork monitoring checks protocol adherence, consent and emerging data quality
  • Draft reports assessed for evidence traceability, limitations and recommendation usefulness
  • Data handover requirements cover ownership, documentation, storage and reusable files

Participants can build, review and present a complete research design and data analysis package for a programme decision.

  • Programme scenario translated into decision-focused research and evaluation questions
  • Theory of change and evaluation matrix aligned with priority evidence gaps
  • Sampling strategy justified using population, precision, feasibility and ethical constraints
  • Data-collection instruments reviewed for measurement quality, consent and safeguarding
  • Data-management and analysis plans specify cleaning, coding, tests and triangulation
  • Peer review tests feasibility, validity, reporting logic and decision usefulness

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

What you take back to work

  1. Theory of change template
  2. Evaluation matrix template
  3. Sampling strategy worksheet
  4. Questionnaire and interview guide pack
  5. Data management and quality assurance plan
  6. Quantitative and qualitative analysis plan

Who should attend

  • Monitoring and Evaluation Officers — need defensible designs, indicators and analysis plans
  • Programme Managers — need evidence to adapt delivery and report results
  • Research Officers — need stronger sampling, instrument design and data-processing methods
  • Humanitarian Assessment Staff — need rapid but credible methods for affected-population data
  • Project Coordinators — need to manage fieldwork, data quality and reporting requirements
  • Donor and Grant Managers — need to review research quality and use findings in decisions

Skills and tools covered

Research designSampling methodsData collectionData quality assuranceQuantitative analysisQualitative thematic analysis

Tools and standards

OECD DAC evaluation criteria, KoboToolbox, Microsoft Excel, R

How the course runs

Training methodology

The course uses short instructor briefings to establish concepts, followed by development and humanitarian case work, group design tasks, dataset reviews and analysis simulations. Participants examine programme documents, build research components, inspect common data errors and interpret findings for management decisions. Facilitated peer critique tests whether designs are feasible, ethical and fit for purpose. Individual application planning helps participants adapt the methods, templates and quality controls to a current or upcoming study.

Requirements and prerequisites

Participants should understand the purpose of monitoring and evaluation and have experience reading programme documents, indicator tables or research reports. Familiarity with basic spreadsheets, including sorting, filtering and simple formulas, is helpful. Participants should be comfortable interpreting percentages, averages and charts. Prior experience with statistical software, KoboToolbox, R, formal research methods or advanced mathematics is not required. The course introduces the required analytical concepts before applying them to development and humanitarian research cases.

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 should have basic familiarity with monitoring and evaluation concepts and be comfortable using spreadsheets. No prior experience with KoboToolbox, R, advanced statistics or formal research training is required.

Participants should bring a laptop with spreadsheet software and, where permitted, a current programme document, indicator framework or draft study idea. Course materials and case datasets are provided.

The course is designed for M&E officers, programme managers, research staff, assessment teams and staff who commission external evaluations. It is most useful for professionals responsible for generating, reviewing or using programme evidence.

Statistics and visualisation are taught as parts of an applied research workflow rather than as isolated technical subjects. The course starts with programme theory, questions, indicators, sampling, ethics and fieldwork, then connects analysis to reporting and management decisions.

Participants can use the templates to prepare a baseline, needs assessment, evaluation or operational study. They can also use the review criteria to strengthen terms of reference, research tools, datasets and consultant reports.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. Early modules establish design, sampling and instrument methods; later modules focus on data processing, analysis, reporting and the final capstone package.

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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