Randomised Controlled Trials for Monitoring and Evaluation Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Monitoring and Evaluation Managers, Impact Evaluation Specialists 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-123

Before you join: Participants should have practical familiarity with monitoring and evaluation concepts, including indicators, baselines, endlines, sampling and survey or administrative data.

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 are frequently asked whether a cash transfer, teacher coaching programme, livelihoods package or health outreach model caused observed results. Routine monitoring can show reach, expenditure and trends, but it cannot reliably establish the counterfactual: what would have happened without the intervention. A well-designed randomised controlled trial (RCT) can provide credible evidence on causal effects, while also exposing delivery failures, inequitable access, spillovers and risks that affect programme decisions.

This course equips participants to design, commission, manage and interpret RCTs for monitoring and evaluation. Participants formulate evaluable causal questions, build Theory of Change pathways, define estimands and select individual, cluster or stepped-wedge designs. They work through randomisation procedures, sample-size and power calculations, baseline measurement, field protocols, implementation fidelity, intention-to-treat analysis, missing data and reporting standards. The course also addresses ethical review, informed consent, safeguarding, data protection and research governance in fragile, low-resource and humanitarian settings.

Teaching combines focused instructor briefings with realistic cases drawn from cash assistance, education, public health and livelihood programmes. Participants assess trial design choices, examine allocation records and participant-flow data, interpret regression outputs, identify threats to validity and prepare evidence for programme and funding decisions. Each participant leaves with an RCT design pack containing a causal question, Theory of Change, randomisation approach, measurement framework, sample planning assumptions, analysis plan and stakeholder reporting outline. The five-day instructor-led course is available in classrooms in more than 50 cities or live online, with 6.5 contact hours each day from 08:30 to 16:30 and a certificate of completion.

What you will be able to do

  • Formulate causal evaluation questions, estimands and decision criteria for an RCT
  • Develop a Theory of Change and measurement framework for randomised evaluation
  • Select individual, cluster or stepped-wedge randomisation designs using operational constraints
  • Calculate sample-size requirements using power, effect size, attrition and intracluster correlation assumptions
  • Specify auditable allocation, consent, data collection and implementation monitoring procedures
  • Monitor treatment fidelity, contamination, attrition and protocol deviations during trial delivery
  • Analyse intention-to-treat effects using regression adjustment and cluster-robust standard errors
  • Produce an evidence brief that reports RCT findings, limitations and programme implications

Benefits of attending

For you

  • Design RCT terms of reference that specify credible causal questions and deliverables
  • Challenge weak randomisation, sampling and analysis proposals from consultants or partners
  • Interpret treatment effects, confidence intervals and subgroup findings with appropriate caution
  • Manage fieldwork risks affecting trial validity, ethics and participant protection
  • Present causal evidence clearly to donors, senior leaders and programme teams

For your organisation

  • Stronger evidence on whether priority interventions caused measurable beneficiary outcomes
  • Better evaluation procurement through clear RCT scopes, protocols and acceptance criteria
  • Reduced risk of invalid findings caused by contamination, attrition or allocation failures
  • Earlier identification of delivery weaknesses through fidelity and process monitoring
  • More defensible funding, scale-up, adaptation and discontinuation decisions

Course modules

10 modules over 5 days

Participants can distinguish causal attribution questions from routine performance monitoring questions.

  • RCT logic: counterfactuals, potential outcomes, treatment effects and attribution
  • Distinguishing random assignment from random sampling and observational comparison designs
  • Internal, construct, statistical conclusion and external validity threats
  • Intention-to-treat estimands versus treatment-on-the-treated estimands and policy questions
  • Superiority, equivalence and non-inferiority questions matched to programme decisions
  • Ethical equipoise, beneficiary rights and duty-of-care in field experiments

Participants can translate a programme model into testable RCT questions and measurable outcomes.

  • Theory of Change assumptions translated into testable causal pathways
  • Defining intervention components, delivery intensity and eligible participant populations
  • Primary outcomes, secondary outcomes and pre-specified subgroup hypotheses
  • Indicator operational definitions, measurement scales and minimum detectable change
  • Developing a pre-analysis plan before outcome data inspection
  • Results chains aligned with research questions, estimands and decision thresholds

Participants can select and document a randomisation design suited to programme delivery.

  • Individual, cluster and stepped-wedge randomisation designs compared
  • Choosing randomisation units from contamination risks and delivery logistics
  • Stratified and blocked allocation for balance on prognostic covariates
  • Allocation concealment procedures that prevent recruiter influence and subversion
  • Random number generation, reproducible seeds and auditable allocation records
  • Cluster spillovers, interference and buffer zones in community programmes

Participants can plan a feasible sample that detects meaningful programme effects.

  • Sampling frames, eligibility screening and consent procedures for trial recruitment
  • Power calculations using effect size, variance, alpha and power
  • Intracluster correlation and design effects for cluster randomised trials
  • Minimum detectable effects linked to operationally meaningful programme changes
  • Attrition allowances, noncompliance assumptions and unequal allocation ratios
  • Recruitment tracking against sample targets, timelines and field constraints

Participants can specify data collection systems that support valid and secure trial evidence.

  • Baseline instruments mapped to outcomes, covariates and implementation measures
  • Survey question wording, recall periods and response option design
  • Administrative data linkage, unique identifiers and consent permissions
  • Enumerator training, translation protocols and fieldwork standard operating procedures
  • Computer-assisted personal interviewing checks for skips, ranges and duplicates
  • Data protection controls: minimisation, access roles, retention and secure transfer

Participants can monitor whether treatment and control conditions remain valid during delivery.

  • Fidelity indicators distinguish treatment delivery from treatment receipt
  • Process evaluation questions identify bottlenecks, adaptations and contextual factors
  • Monitoring dashboards track enrolment, dosage, attendance and protocol deviations
  • Contamination monitoring documents control-group exposure and informal spillovers
  • Implementation logs reconcile planned activities, costs and actual delivery
  • Adaptive management boundaries protect randomisation while resolving delivery problems

Participants can prepare and analyse trial data using appropriate impact estimation methods.

  • Analysis datasets: treatment flags, outcome construction and reproducible codebooks
  • Intention-to-treat estimation using difference-in-means and regression adjustment
  • Cluster-robust standard errors and multilevel models for grouped assignment
  • Baseline covariate adjustment to improve precision without post-treatment bias
  • Missing-data patterns, multiple imputation and inverse probability weighting
  • Multiple outcomes, subgroup analysis and false discovery rate control

Participants can interpret RCT results accurately and communicate decision-relevant conclusions.

  • CONSORT participant flow, attrition reporting and protocol deviation disclosure
  • Effect sizes, confidence intervals, p-values and practical significance
  • Pre-specified subgroup results distinguished from exploratory heterogeneity analyses
  • Balance tables and baseline equivalence checks without significance-test misuse
  • Threats to external validity: scale, setting, population and delivery
  • Results narratives separating evidence, interpretation, limitations and recommendations

Participants can establish ethical and governance controls for responsible RCT delivery.

  • Ethics review submissions, risk assessment and adverse-event escalation pathways
  • Informed consent scripts for low-literacy, vulnerable and displaced populations
  • Data management plans covering ownership, access, anonymisation and destruction
  • Safeguarding protocols for enumerator conduct, referrals and incident reporting
  • Conflict-of-interest declarations and independence safeguards for evaluators
  • Research governance roles: principal investigator, implementing partner and data controller

Participants can convert trial findings into credible recommendations and an application plan.

  • Decision matrices connect estimated effects, costs, risks and programme choices
  • Cost-effectiveness ratios interpreted alongside uncertainty and implementation feasibility
  • Evidence briefs structured for donors, ministries, boards and programme managers
  • Stakeholder dissemination plans specify audiences, findings, channels and timing
  • Replication decisions distinguish core mechanisms from adaptable delivery features
  • Personal application plan defines trial question, partners, milestones and next actions

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

What you take back to work

  1. RCT design pack template
  2. Theory of Change and estimand worksheet
  3. Randomisation and allocation protocol
  4. Sample-size planning model
  5. Implementation fidelity monitoring checklist
  6. RCT evidence brief template

Who should attend

  • Monitoring and Evaluation Managers — need credible methods for attributing programme results
  • Impact Evaluation Specialists — design and supervise experimental evaluations for funders and partners
  • Programme Managers — use trial evidence to improve delivery models and resource decisions
  • Research Managers — oversee field research, ethics, data quality and external evaluators
  • Humanitarian and Development Advisers — assess whether interventions produce intended beneficiary outcomes
  • Donor and Foundation Programme Officers — commission, review and use causal evidence from grantees

Skills and tools covered

RCT designCausal inferenceSample size planningRandomisation proceduresImpact analysisEvidence reporting

Tools and standards

CONSORT 2010 Statement, SPIRIT 2013 Statement, Theory of Change, Directed Acyclic Graphs

How the course runs

Training methodology

The course uses short instructor briefings to establish causal inference and trial design principles, followed by facilitated case work from development and humanitarian programmes. Participants review programme scenarios, compare design options, assess sample planning assumptions, inspect data-quality issues and interpret RCT outputs. Group exercises focus on practical decisions such as randomisation units, consent procedures, implementation monitoring and evidence reporting. Simulations show how attrition, spillovers and noncompliance affect findings. Individual application planning converts course methods into a draft RCT design pack for a current or prospective programme.

Requirements and prerequisites

Participants should have practical familiarity with monitoring and evaluation concepts, including indicators, baselines, endlines, sampling and survey or administrative data. Experience working with programme teams, researchers or evaluation consultants is useful. Participants should be comfortable reading basic tables, percentages and charts. Prior statistics training is helpful but not essential; the course explains core concepts such as confidence intervals, statistical power and regression adjustment before applying them. No prior RCT design experience, programming ability, advanced econometrics or specialised statistical software knowledge is required.

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 know basic monitoring and evaluation concepts such as indicators, baselines, sampling and data collection. Advanced statistics, econometrics, programming and prior RCT experience are not required.

Bring details of a programme, intervention or evaluation question you may wish to assess. A laptop is useful for reviewing templates and case materials, although no specialist software is required.

The course suits M&E managers, evaluation specialists, programme managers, research staff, donor officers and technical advisers. It is particularly relevant for staff who commission, supervise, review or use impact evaluations.

General M&E courses cover results frameworks, indicators, monitoring systems and evaluation approaches broadly. This course focuses specifically on experimental impact evaluation: random assignment, causal inference, trial operations, analysis and reporting.

You can use the design pack to frame an RCT with internal teams, implementing partners or external evaluators. The methods also help you assess whether an existing RCT protocol, data collection plan or evaluation report is fit for purpose.

Each day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The course progresses from causal questions and design through sampling, field implementation, analysis, ethics, reporting 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

More courses in Monitoring & Evaluation

Monitoring & Evaluation Qualitative Data Analysis Training Course 10 days · from USD 2,200 · next 16 Nov Monitoring & Evaluation Qualitative Interviewing for Evaluation Training Course 10 days · from USD 2,200 · next 16 Nov Monitoring & Evaluation Qualitative Research Methods Training Course 10 days · from USD 2,200 · next 16 Nov Monitoring & Evaluation Quantitative Data Analysis Training Course 5 days · from USD 1,100 · next 16 Nov Monitoring & Evaluation Research Data Collection and Storytelling Training Course 5 days · from USD 1,100 · next 16 Nov Monitoring & Evaluation Research Design and Data Analysis Training Course 10 days · from USD 2,200 · next 16 Nov Monitoring & Evaluation Research Design and Methods Training Course 10 days · from USD 2,200 · next 16 Nov Monitoring & Evaluation Research Design, ODK Data Collection, GIS Mapping and Data Analysis Training Course 10 days · from USD 2,200 · next 16 Nov