Abstract:
The lack of a “gold standard” to determine and predict household food insecurity is well
documented. While a considerable volume of research continues to explore universally
applicable measurement approaches, robust statistical techniques have not been applied in food
security monitoring and early warning systems, especially in countries where food insecurity is
chronic. This study explored the application of various Ordinal Logistic Regression techniques in
the analysis of national data from Southern Sudan. Five Link Functions of the Ordinal
Regression model were tested. Of these techniques, the Probit Model was found to be the most
efficient for predicting food security using ordered categorical outcomes (Food Consumption
Scores). The study presents the first rigorous analysis of national food security levels in postconflict
Southern Sudan and shows the power of the model in identifying significant predictors
of food insecurity, surveillance, monitoring and early warning.
