Feed the Future Innovation Lab for Food Security Policy Research, Capacity, and Influence (PRCI) is an innovation lab supported by USAID’s Bureau for Resilience and Food Security (RFS). It is a collaborative effort among consortium partners that seeks to enhance the ability of local policy research organizations to conduct high-quality food security policy research and influence food security policy more effectively while becoming increasingly self-reliant. The lab is led by Michigan State University Food Security Group (part of the Department of Agricultural, Food, and Resource Economics) and consortium members include ReNAPRI (Regional Network of Agricultural Policy Research Institutes), ISSER (Institute for Statistical, Social, and Economic Research - Ghana), IFPRI (International Food Policy Research Institute), and Cornell University.
The following provides links to policy analysis training modules currently available on the PRCI website:
Module 1: Integrating Gender in Policy Research and Outreach
Learn about key gender issues and entry points for policy research and outreach.
Module 2: Module 2: Stata Basics - Working with Complex Survey Data and Descriptive Statistics
Learn the basics of working with complex survey data in Stata.
Module 4: Research Transparency and Reproducibility
Learn the principles of the scientific method and the steps needed to develop a repudiable research project.
Module 5: Avoiding Unintentional Plagiarism
Learn what constitutes plagiarism and effective strategies for avoiding it in your work.
Module 6: Ordinary Least Squares
A review of the most commonly used linear regression approach, ordinary least squares (OLS).
Module 7: Binary Response Models
Learn how to use the Linear Probability Model, Probit models and logit models.
Module 8: Impact Evaluation - Introduction and Methods Overview
Learn concepts, challenges, methods, and practical considerations for doing impact evaluation.
Module 9: Testing and Correcting for Endogeneity in Linear Models
Learn about endogeneity, and ways of identifying and correcting for endogeneity in linear models.
Module 10: Linear Panel Data Models
Learn the fundamentals of linear panel data models as well as testing and correcting for attrition bias.
Learn the fundamentals of the Type I Tobit model and the situations in which it is appropriate to use it.
Module 12: Double Hurdle Models
Learn the fundamentals of hurdle models, situations in which it is appropriate to use them and how to estimate hurdle models in STATA.
*Module 3 is not currently available as it was intended to be used only for those with a direct connection to Michigan State University.