Experimental and Quasi-Experimental Evaluation Designs Training Course

Introduction

Establishing the true impact of an intervention—proving that a program caused an observed change—is the gold standard in monitoring and evaluation. The Experimental and Quasi-Experimental Evaluation Designs Training Course is meticulously crafted for M&E professionals, evaluators, researchers, and program managers who are dedicated to generating robust evidence of impact. This comprehensive program delves into the most rigorous methodological approaches for attributing outcomes to specific programs or policies, moving beyond mere correlation to demonstrate true causality. Participants will gain the conceptual understanding and practical skills to design and implement evaluations that stand up to scrutiny and genuinely inform decision-making.

This intensive training explores both experimental designs, primarily Randomized Controlled Trials (RCTs), and a range of powerful quasi-experimental designs (QEDs) that can establish causality when randomization isn't feasible. Attendees will learn to identify appropriate designs for different program contexts, understand the assumptions behind each method, and apply techniques to minimize bias and confounding. By mastering experimental and quasi-experimental evaluation designs, professionals will be empowered to conduct more credible impact evaluations, provide compelling evidence for program effectiveness, and contribute to more efficient and accountable development initiatives globally.

Target Audience

• M&E Specialists and Coordinators
• Impact Evaluation Researchers
• Program and Project Managers
• Policy Analysts and Advisors
• Data Scientists and Statisticians
• Public Health and Development Practitioners
• Funders and Donors of Development Programs

Duration

5 Days

Course Objectives

1. Understand the core principles of causal attribution in program evaluation.
2. Distinguish between experimental and quasi-experimental evaluation designs and their applications.
3. Design Randomized Controlled Trials (RCTs) for various intervention contexts.
4. Apply key quasi-experimental methods such as Propensity Score Matching (PSM) and Difference-in-Differences (DiD).
5. Identify and mitigate common threats to the validity of impact evaluation findings.
6. Understand data requirements and analytical approaches for experimental and quasi-experimental designs.
7. Interpret findings from rigorous impact evaluations to inform policy and practice.
8. Address ethical considerations inherent in experimental and quasi-experimental studies.

Course Content

Module 1: Introduction to Impact Evaluation and Causal Inference

• The importance of impact evaluation in M&E
• Understanding causation versus correlation
• The concept of the counterfactual and its role in evaluation
• Overview of the causal inference framework
• Why rigorous designs are essential for credible evidence

Module 2: Foundations of Experimental Designs: Randomized Controlled Trials (RCTs)

• Principles of randomization and its role in creating comparable groups
• Different types of randomization: individual, cluster, stratified
• Advantages of RCTs for establishing causal links
• Common misconceptions and criticisms of RCTs
• Prerequisites for implementing an RCT

Module 3: Designing and Implementing RCTs

• Steps in designing an RCT for a development program
• Determining sample size and power analysis for RCTs
• Practical considerations for randomization in the field
• Baseline data collection and attrition monitoring
• Managing implementation fidelity and contamination

Module 4: Analyzing Data from Experimental Designs

• Basic statistical analysis for comparing treatment and control groups
• Estimating Average Treatment Effects (ATE)
• Handling non-compliance and attrition in RCT analysis
• Regression analysis for RCT data
• Interpreting and presenting RCT results

Module 5: Introduction to Quasi-Experimental Designs (QEDs)

• When and why to use QEDs: alternatives to randomization
• The fundamental challenge of selection bias in QEDs
• Overview of common QED approaches
• Understanding the core assumptions of QEDs
• Comparing strengths and weaknesses of different QEDs

Module 6: Propensity Score Matching (PSM)

• The concept of propensity scores and balancing observable covariates
• Steps for implementing Propensity Score Matching (PSM)
• Methods of matching: nearest neighbor, caliper, kernel
• Assessing balance and common support region
• Interpreting PSM results and limitations

Module 7: Difference-in-Differences (DiD)

• Principles of the Difference-in-Differences (DiD) method
• The critical "parallel trends" assumption and how to test it
• Data requirements and design considerations for DiD
• Estimating treatment effects using DiD regression models
• Extensions of DiD and robustness checks

Module 8: Regression Discontinuity Design (RDD)

• Understanding RDD based on a continuous assignment variable and a sharp cutoff
• Sharp RDD vs. Fuzzy RDD and their applications
• Graphical and regression approaches to RDD analysis
• Assumptions of RDD and assessing its validity
• Interpreting Local Average Treatment Effects (LATE) in RDD

Module 9: Instrumental Variables (IV) and Other QEDs

• The concept of Instrumental Variables (IV) for unobserved confounding
• Assumptions of IV and how to identify a valid instrument
• Application of IV in development contexts
• Brief overview of other QEDs: Interrupted Time Series (ITS), Synthetic Control Method
• Choosing the right QED for your program context

Module 10: Threats to Validity in Experimental and Quasi-Experimental Designs

• Internal validity: confounding, selection bias, attrition bias, spillover effects
• External validity: generalizability and representativeness
• Construct validity: measuring what you intend to measure
• Statistical conclusion validity: ensuring appropriate statistical analysis
• Strategies for mitigating threats to validity in design and analysis

Module 11: Data Collection and Management for Experimental and QEDs

• Planning data collection for rigorous evaluation designs
• Ensuring high-quality baseline and endline data
• Data management protocols for complex datasets
• Using mobile data collection tools for accuracy and efficiency
• Data security and confidentiality in impact evaluations

Module 12: Communicating Findings and Ethical Considerations

• Presenting complex causal evidence clearly and concisely
• Tailoring communication for various audiences (policymakers, practitioners)
• Ethical considerations in conducting impact evaluations (informed consent, do no harm)
• Balancing scientific rigor with program realities
• Lessons learned and best practices in implementing experimental and quasi-experimental designs.

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank