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Research and Data Analysis
Propensity Score Matching for Quasi-Experimental Designs Training Course
Introduction
In today’s data-driven research landscape, ensuring accurate causal inference in non-randomized studies is crucial. Propensity Score Matching (PSM) for Quasi-Experimental Designs Training Course is designed to equip researchers, data analysts, and policy evaluators with advanced skills to address selection bias and improve the credibility of their results. With the rise in demand for causal inference, program evaluation, and impact assessment in health, education, social science, and economics, mastering PSM techniques has become a strategic necessity for researchers and analysts. This course combines theoretical depth with practical application, focusing on real-world case studies and hands-on simulations using statistical software.
This course offers a robust understanding of quasi-experimental design, matching algorithms, treatment effect estimation, and sensitivity analysis. Through carefully curated modules, participants will explore how PSM reduces bias in observational studies, ensuring valid and reliable outcomes. Participants will gain fluency in applying PSM using tools like R, STATA, or SPSS, while also learning to critically interpret and present their findings.
Programme Curriculum
Propensity Score Matching for Quasi-Experimental Designs Training Course
Introduction
In today’s data-driven research landscape, ensuring accurate causal inference in non-randomized studies is crucial. Propensity Score Matching (PSM) for Quasi-Experimental Designs Training Course is designed to equip researchers, data analysts, and policy evaluators with advanced skills to address selection bias and improve the credibility of their results. With the rise in demand for causal inference, program evaluation, and impact assessment in health, education, social science, and economics, mastering PSM techniques has become a strategic necessity for researchers and analysts. This course combines theoretical depth with practical application, focusing on real-world case studies and hands-on simulations using statistical software.
This course offers a robust understanding of quasi-experimental design, matching algorithms, treatment effect estimation, and sensitivity analysis. Through carefully curated modules, participants will explore how PSM reduces bias in observational studies, ensuring valid and reliable outcomes. Participants will gain fluency in applying PSM using tools like R, STATA, or SPSS, while also learning to critically interpret and present their findings.
Course Objectives
Understand the fundamentals of quasi-experimental designs in non-randomized studies.
Explore the theoretical framework behind propensity score matching.
Identify the key assumptions for causal inference using PSM.
Learn to estimate propensity scores using logistic regression.
Apply nearest neighbor, caliper, and kernel matching algorithms.
Conduct balance diagnostics to assess matching quality.
Evaluate average treatment effects using matched samples.
Perform sensitivity analysis for hidden bias detection.
Use R, STATA, or SPSS for PSM implementation.
Integrate PSM in program evaluation and impact studies.
Interpret PSM results for evidence-based policy recommendations.
Build publishable research outputs using matched observational data.
Critically appraise PSM applications in peer-reviewed literature.
Target Audiences
Public health researchers
Educational evaluators
Government policy analysts
Data scientists in social sciences
Economists conducting impact assessments
Graduate students in quantitative fields
NGO monitoring and evaluation officers
Healthcare outcomes researchers
Course Duration: 5 days
Course Modules
Module 1: Introduction to Quasi-Experimental Designs
Definition and importance of quasi-experiments
Comparison with randomized controlled trials
Selection bias in observational studies
Role of PSM in quasi-experiments
Overview of real-world applications
Case Study: Evaluating an education reform program
Module 2: Understanding Propensity Scores
Concept and mathematical foundation
Confounders and covariates
Logistic regression for score estimation
Common pitfalls in score estimation
Overlap and common support regions
Case Study: Healthcare access in rural populations
Module 3: Matching Techniques and Algorithms
Nearest neighbor matching
Caliper matching
Radius and kernel matching
Matching with/without replacement
Visualizing matched pairs
Case Study: Employment outcomes in microfinance recipients
Module 4: Balance Diagnostics and Quality Checks
Covariate balance before and after matching
Standardized mean differences
Visual diagnostic plots (Love plots)
Statistical tests for balance
Iterating matching until balance
Case Study: Tobacco control policy evaluation
Module 5: Estimating Treatment Effects
Average Treatment Effect (ATE)
Average Treatment Effect on Treated (ATT)
Unmatched vs. matched estimation
Confidence intervals and statistical inference
Adjusting standard errors
Case Study: Impact of mentoring on student success
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.