Post-Enumeration Survey Ethiopia Agricultural Census
This intensive course provides a complete, end-to-end grounding in Post-Enumeration Survey (PES) design, implementation, analysis, and reporting—tailored to agricultural censuses. Through six modules, students will master coverage and content error measurement, sampling design, field operations, dual-system estimation, variance estimation, content-error indices, small-area methods, and the communication of findings. The syllabus blends theory, case studies (Namibia 2023, China 2024, South Africa 2022), hands-on simulation exercises, and a capstone applied project.
Enumeration of Nomadic and Semi-Nomadic (Transhumant) Livestock
The overall objective of the training on enumeration of nomadic and semi-nomadic (transhumant livestock) is to strengthen the technical capacity of statistical producers (statistical offices and other institutions involved in the production of agricultural statistics) to apply relevant methods in the collection and compilation of quality nomadic and semi-nomadic livestock statistics.
STATA Survey Data Processing & Analysis
In an era where policy decisions and program evaluations hinge on sound evidence, mastering the end-to-end workflow for survey data is essential. This course bridges theory and practice by guiding participants through the complete lifecycle of complex survey datasets—from import and design declaration to cleaning, imputation, merging, and generation of analysis-ready outputs. Leveraging both SPSS and Stata, you’ll develop reproducible, documented workflows that meet international standards for survey inference. Through hands-on labs using real-world guidebook examples, you will gain the technical skills and methodological understanding needed to deliver reliable, design-correct estimates for national statistics and research.
Sampling Design Concepts
• Provide in-depth knowledge of the theory and practice of sampling design, including probability and complex sampling techniques.
• Equip participants with skills to design stratified, cluster, and multi-stage samples based on real-world needs.
• Enhance understanding of sampling errors, bias, estimation techniques, and confidence intervals.
• Train participants to use weights properly and interpret precision indicators such as the coefficient of variation and design effect.
• Enable participants to produce professional documentation of survey methods, including annexes on accuracy and sampling errors.
• Foster the ability to critically review sampling designs and ensure high-quality, representative data collection.