Unlocking True Impact: Impact Evaluation and Randomized Controlled Trials (RCTs) Training Course

Introduction

In the complex landscape of development, public policy, and social programming, knowing whether an intervention works and why is paramount for effective resource allocation and sustainable progress. Impact evaluation provides the rigorous analytical framework to attribute observed changes directly to specific programs or policies, moving beyond mere correlation to establish clear cause-and-effect relationships. Among the various methodologies, Randomized Controlled Trials (RCTs) stand as the gold standard, offering the most robust approach to isolating program effects by creating credible counterfactuals.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of impact evaluation principles and, specifically, the design, implementation, and analysis of Randomized Controlled Trials. From understanding the core concepts of causality and experimental design to mastering the practical steps of randomization, data collection, and statistical analysis, you will gain the expertise to conduct high-quality evaluations. This empowers you to generate credible evidence that truly informs policy, improves program effectiveness, and drives impactful change in your respective fields.

Target Audience

  • Development practitioners and program managers.
  • Researchers and academics in social sciences, public health, and economics.
  • M&E (Monitoring & Evaluation) specialists in government agencies and NGOs.
  • Policy analysts and advisors.
  • Philanthropy and impact investing professionals.
  • Data scientists and statisticians involved in program assessment.
  • International development consultants.
  • Graduate students (Master's and PhD) in relevant fields.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the fundamental principles of impact evaluation and the importance of causality.
  • Grasp the theoretical underpinnings and practical applications of Randomized Controlled Trials (RCTs).
  • Analyze the key steps involved in designing a robust RCT, including randomization strategies and sample size determination.
  • Comprehend the challenges and ethical considerations in implementing RCTs in real-world settings.
  • Evaluate the appropriate statistical methods for analyzing data from RCTs and interpreting results.
  • Develop practical skills in using software to manage and analyze data from impact evaluations.
  • Navigate common pitfalls and best practices in reporting and communicating impact evaluation findings.
  • Formulate a strategic approach for integrating rigorous impact evaluation into program design and policy-making.

Course Content

  1. Introduction to Impact Evaluation and Causal Inference
  • The importance of impact evaluation in development and policy
  • Defining impact evaluation: attribution vs. correlation
  • The concept of causality and the counterfactual
  • The fundamental problem of causal inference: missing the counterfactual
  • Overview of different impact evaluation methods: experimental, quasi-experimental, non-experimental
  1. Randomized Controlled Trials (RCTs): The Gold Standard
  • Principles of randomization: why it works for establishing causality
  • Types of randomization: individual, cluster, stratified randomization
  • The role of randomization in creating a valid control group
  • Advantages of RCTs: internal validity, unbiased impact estimates
  • Limitations of RCTs: external validity, ethical concerns, cost, feasibility
  1. Designing an RCT: From Theory to Practice
  • Defining the program/intervention and theory of change
  • Identifying the target population and units of randomization
  • Outcome indicators: selection, measurement, and data collection tools
  • Baseline surveys: why they are crucial for precision and balance checks
  • Endline surveys and follow-up data collection
  1. Sample Size Determination and Power Calculations
  • Understanding statistical power and Type I/II errors
  • Factors influencing sample size: minimum detectable effect, variance, clustering
  • Formulas and software tools for power calculations (e.g., Stata, R, Optimal Design)
  • Practical considerations for sample size in real-world RCTs
  • Managing attrition and its impact on power
  1. Implementation and Monitoring of RCTs
  • Practical steps for conducting randomization (e.g., using random number generators)
  • Ensuring fidelity of treatment assignment: preventing contamination and spillover
  • Monitoring implementation progress and data quality
  • Adapting to unforeseen circumstances during implementation
  • Building strong relationships with implementing partners and local communities
  1. Data Management and Analysis for RCTs
  • Data cleaning, validation, and management best practices
  • Statistical software for analysis (e.g., Stata, R, Python): basic commands for RCT analysis
  • Comparing means between treatment and control groups (t-tests, regressions)
  • Using regression analysis to estimate treatment effects (OLS)
  • Accounting for cluster randomization and baseline controls in analysis
  1. Addressing Challenges in RCTs
  • Attrition and missing data: strategies for mitigation and analysis (e.g., ITT, LATE)
  • Contamination and spillover effects: detection and handling
  • Generalizability (external validity) of RCT findings
  • Ethical considerations: informed consent, equipoise, harms and benefits
  • Operational challenges: logistics, cost, time
  1. Advanced Topics in RCT Design and Analysis
  • Encouragement designs and Instrumental Variables (IV) for imperfect compliance
  • Factorial designs: testing multiple interventions simultaneously
  • Stepped-wedge designs and phased rollouts
  • Bayesian approaches to RCT analysis (conceptual overview)
  • Heterogeneous treatment effects: identifying for whom the program works best
  1. Policy Relevance and Communication of Findings
  • Translating rigorous evidence into policy recommendations
  • Writing effective evaluation reports and policy briefs
  • Presenting findings to diverse audiences (policymakers, practitioners, donors)
  • The role of evidence in decision-making and scale-up
  • Building an evidence-informed culture within organizations
  1. Complementary Methods and Future of Impact Evaluation
  • Introduction to quasi-experimental methods (matching, DiD, RDD) when RCTs are not feasible
  • Mixed-methods approaches: combining quantitative and qualitative data for richer insights
  • Ethics in impact evaluation and responsible data use
  • The evolving landscape of impact evaluation: big data, machine learning, and rapid evaluations
  • Sustaining evidence use in development and public policy.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Unlocking True Impact: Impact Evaluation And Randomized Controlled Trials (rcts) Training Course in Kenya
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