Quasi-Experimental Methods in M&E Training Course

Quasi-Experimental Methods in M&E Training Course

Introduction

Quasi-experimental methods are increasingly becoming indispensable in the field of Monitoring and Evaluation (M&E), enabling organizations to measure program impacts with rigor and credibility, even in the absence of randomized control trials. Quasi-Experimental Methods in M&E Training Course offers a deep dive into quasi-experimental designs, covering methodologies such as difference-in-differences, propensity score matching, regression discontinuity, and interrupted time series. Participants will gain practical skills to design, implement, and interpret quasi-experimental evaluations that drive evidence-based decision-making, enhance program accountability, and maximize development outcomes.

Through a combination of theoretical frameworks, real-world case studies, and hands-on exercises, this course empowers M&E professionals, program managers, and data analysts to strengthen evaluation capacity, produce actionable insights, and improve policy formulation. The course emphasizes trending analytics tools, data triangulation, bias mitigation, and outcome measurement strategies to ensure robust and credible evaluation results, even in complex program environments.

Course Duration

5 days

Course Objectives

By the end of this training, participants will be able to:

1. Understand the foundations and significance of quasi-experimental designs in M&E.
2. Differentiate quasi-experimental methods from randomized controlled trials.
3. Apply difference-in-differences (DiD) methods to assess program impacts.
4. Implement propensity score matching (PSM) for non-randomized evaluations.
5. Use regression discontinuity designs to evaluate policy thresholds.
6. Conduct interrupted time series analyses for program trend assessments.
7. Identify and mitigate selection bias and confounding variables.
8. Design quasi-experimental evaluations aligned with program objectives.
9. Analyze quantitative and qualitative data for robust impact assessment.
10. Integrate emerging data analytics tools into evaluation workflows.
11. Present actionable findings to stakeholders for evidence-based decisions.
12. Document and report quasi-experimental evaluations using international standards.
13. Translate evaluation insights into recommendations for policy and practice improvement.

Target Audience

1. Monitoring and Evaluation Officers
2. Program Managers and Coordinators
3. Data Analysts and Statisticians
4. Policy Advisors and Planners
5. Research Officers in NGOs and Donor Agencies
6. Academic Researchers and Postgraduate Students in Social Sciences
7. Government M&E Practitioners
8. Impact Assessment Consultants

Course Modules

Module 1: Introduction to Quasi-Experimental Methods

• Overview of quasi-experimental designs and M&E relevance
• Key differences between experimental and quasi-experimental approaches
• Strengths, limitations, and applicability in development programs
• Ethical considerations and data integrity
• Case Study: Evaluating school feeding programs in Kenya

Module 2: Difference-in-Differences (DiD) Approach

• Understanding pre-post comparison frameworks
• Implementing DiD in longitudinal data
• Controlling for confounding variables
• Interpreting DiD coefficients and results
• Case Study: Health intervention impact on maternal outcomes

Module 3: Propensity Score Matching (PSM)

• Basics of treatment and control group selection
• Matching techniques: nearest neighbor, kernel, and stratification
• Assessing balance and model diagnostics
• Applications in non-randomized program settings
• Case Study: Cash transfer program evaluation

Module 4: Regression Discontinuity Design (RDD)

• Understanding assignment thresholds and cutoffs
• Implementing RDD in policy and program evaluations
• Robustness checks and sensitivity analysis
• Graphical and statistical interpretation
• Case Study: Evaluating scholarship eligibility impacts

Module 5: Interrupted Time Series (ITS) Analysis

• Designing ITS for program trend assessment
• Handling autocorrelation and seasonality
• Detecting structural breaks in data
• Visualizing trends and interpreting results
• Case Study: Assessing public health campaigns

Module 6: Bias Identification and Mitigation

• Types of bias in quasi-experimental designs
• Confounding, selection, and measurement biases
• Strategies for bias reduction and validation
• Sensitivity and robustness analysis techniques
• Case Study: Microfinance program impact evaluation

Module 7: Data Analysis and Interpretation

• Statistical software tools for quasi-experimental evaluation
• Quantitative and qualitative data integration
• Reporting effect sizes and confidence intervals
• Presenting findings for multiple stakeholders
• Case Study: Agricultural extension program evaluation

Module 8: Translating Findings into Action

• Communicating results to policymakers and stakeholders
• Crafting actionable recommendations
• Designing feedback loops for program improvement
• Scaling and replicating evidence-based interventions
• Case Study: Social protection program policy adjustments

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.