Introduction

Regression analysis is a powerful statistical technique widely used in impact evaluation to quantify relationships between variables, measure program effectiveness, and inform data-driven decision-making. Regression Analysis for Impact Evaluation Training Course provides participants with a comprehensive understanding of regression models, including linear, logistic, and multilevel regressions, along with practical applications in monitoring and evaluation (M&E). Emphasis is placed on interpreting coefficients, testing hypotheses, addressing confounding variables, and applying robust analytical methods for rigorous impact assessment. Participants will gain hands-on experience using statistical software, designing regression-based evaluation strategies, and translating results into actionable insights for policy and program improvement.

With increasing demand for evidence-based decision-making, this training equips professionals with trending analytical skills to evaluate program outcomes, optimize interventions, and enhance organizational impact. By integrating case studies, real-world datasets, and interactive exercises, the course ensures practical mastery of regression techniques. Participants will learn how to handle missing data, control for biases, and report findings with clarity, making it ideal for researchers, M&E specialists, policy analysts, and data-driven decision-makers seeking to strengthen their impact evaluation capabilities.

Programme Curriculum

Regression Analysis for Impact Evaluation Training Course

Introduction

Course Duration

5 days

Course Objectives

Understand the fundamentals of regression analysis for impact evaluation.
Apply linear regression models to real-world program data.
Implement logistic regression for binary outcomes in M&E.
Utilize multilevel regression models for hierarchical data.
Test hypotheses and interpret regression coefficients accurately.
Identify and control confounding variables in regression models.
Handle missing data and outliers in impact evaluation datasets.
Conduct sensitivity and robustness checks for reliable results.
Integrate regression analysis with causal inference techniques.
Visualize and communicate regression findings effectively.
Design regression-based evaluation strategies for programs.
Apply software tools (R, Stata, or SPSS) for regression analysis.
Translate regression outputs into actionable program recommendations.

Target Audience

Monitoring & Evaluation Specialists
Policy Analysts and Researchers
Program Managers and Coordinators
Data Analysts and Statisticians
Development Consultants
Social Scientists
Graduate Students in Social Sciences and Public Policy
Decision-Makers and Evidence-Based Practitioners

Course Modules

Module 1: Introduction to Regression Analysis

Fundamentals of regression and correlation
Types of regression models (linear, logistic, multilevel)
Key assumptions in regression
Overview of regression in impact evaluation
Case study: Evaluating education program outcomes

Module 2: Linear Regression for Impact Evaluation

Simple vs. multiple linear regression
Model specification and variable selection
Estimating and interpreting coefficients
Assessing goodness-of-fit
Case study: Health intervention impact on patient outcomes

Module 3: Logistic Regression and Binary Outcomes

Introduction to logistic regression
Odds ratios and probability interpretation
Model diagnostics and evaluation
Handling categorical predictors
Case study: Predicting program success rates

Module 4: Multilevel and Hierarchical Regression

Understanding hierarchical data structures
Random intercept and random slope models
Estimation techniques for multilevel models
Interpreting multilevel regression outputs
Case study: Regional impact analysis of agricultural interventions

Module 5: Handling Confounding and Bias

Identifying confounders in datasets
Techniques to control bias (matching, covariate adjustment)
Sensitivity analysis for robustness
Detecting multicollinearity
Case study: Evaluating social protection programs

Module 6: Dealing with Missing Data and Outliers

Types of missing data
Imputation techniques
Identifying and handling outliers
Impact of missing data on regression results
Case study: Education enrollment data analysis

Module 7: Communicating Regression Results

Data visualization techniques
Reporting regression findings clearly
Using dashboards for decision-making
Translating analysis into recommendations
Case study: NGO program impact report

Module 8: Advanced Regression Applications

Regression with interaction terms
Non-linear regression models
Combining regression with causal inference methods
Forecasting program outcomes
Case study: Evaluating economic empowerment programs

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@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Model building variable selection result interpretation assumption testing

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000