Small Area Statistics


Date and Venue

April 3, 2023 - April 7, 2023
Crowne Plaza Hotel, Dubai Deira. UAE.
July 10, 2023 - July 14, 2023
Sheraton Memphis Downtown Hotel, USA.
Oct. 23, 2023 - Oct. 27, 2023
Kigali Serena Hotel, KN 3 Ave, Kigali, Rwanda
Dec. 11, 2023 - Dec. 15, 2023
University of Ghana, Legon, Accra Ghana


Target Participants

Middle and senior level officers in the entire statistical functionaries in a nation will find the course highly rewarding. This includes Bureau of statistics at both state and federal government levels. Departments of Planning, research and Statistics in the entire Ministry, Departments and Agencies (MDAs) both at state and federal government levels.


Analytic methods such as small area estimation is useful for producing official statistics. Each small area problem needs to be carefully assessed to ensure that the approach taken and techniques applied suit the particular problem at hand.

Also, if say, small area estimates are to be used more as a guide to indicate areas of unmet demand, then  officers  saddled with this responsibility must be sufficiently schooled to ensure accuracy.  This is the raison d'être for this course.

It is important to train the relevant officials to choose the geographic areas and the key output variables carefully to ensure the results will be fit for the purpose. 

This course  will help participants  to answer questions such as:

  • What is the nature and context of the key planning or funding decisions that require small area data?
  • What variables or  indicators are needed to meet these decision making requirements. What disaggregations of these are important and why? What level of geography is needed?
  • What level of accuracy is needed and which small area estimates have the greatest priority in terms of accuracy?
  • What theory is available to help identify the models which can be used to produce small area estimates?
  • What auxiliary data is available to support the modelling process? How is this data collected, for what purpose is it used, and how accurate is it likely to be?

COURSE OBJECTIVESParticipants will gain an understanding of the methods for small area estimation, a topic of practical and theoretical interest due to growing demands for reliable small area statistics. Applications will demonstrate the implementation of the methods in practice.The specific objectives are to make participants:

  • Enhance their skills  for obtaining facts from figures
  • Understand tools for analysis and management of Small Area Statistics.
  • Use popular software package like SAS/STAT  for small area data analysis.
  • Know how to organize, describe and analyze small area data.
  • Be familiar with small area statistics  concepts needed for decision making.
  • Familiarize participants with traditional and model-based methods for small area estimation and their application.
  • Increase knowledge and understanding of small area estimation techniques and ensure greater consistency in their application
  • Provide a guide for choosing the best method to apply for a particular situation
  • Advise on the trade-off between the complexity of method and the quality of the modelled estimates



PART TWO• Introduction to Small Area Estimation, Examples of small - area statistics . A perspective on Fay and Herriot ( 1979 ) . Description of some elementary methods and their shortcomings .

PART THREE• Designing surveys for small - area estimation . Spatial similarity . Fine - tuning small - area estimation to a specific policy . Secondary analysis of small - area estimates . The role of simulations and graphics

PART FOUR• Bayesian methods in Small Area Statistics• Robust, semi and non-parametric modeling• Advanced methods in Small Area StatisticsDirect estimation (design based)Direct estimation (model based)Generalized regression (GREG) estimatorBorrowing strength; Indirect estimationsSynthetic methodLink to regressionComposite Estimation• Linear Mixed Models• Small Area (Explicit) Models; Area level model• Small Area (Explicit) Models; Unit level model

PART FIVEData Analysis Using SAS/STAT