Browsing by Author "Lokosang, Laila Barnaba."

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Flexible statistical modelling in food insecurity risk assessment.
(2015) Lokosang, Laila Barnaba.; Ramroop, Shaun.; Zewotir, Temesgen Tenaw.
Food insecurity has remained a persistent problem in Sub-Saharan Africa. Conflict and other protracted crisis have rendered a significant proportion of Africa’s populations to suffer the risk of food insecurity, as their resilience to livelihood shocks weakens. A significant and immense body of research in the past two decades has largely centred on describing the incidence of food insecurity and vulnerability. Limited research was done using statistical methods to determine the likelihood of food insecurity risk. The use of flexible statistical techniques for a sound and purposive monitoring, evaluation, planning and decision making in food security and resilience was limited. The study aimed to extend the use of statistics into the expanding field of food security and resilience, and also to provide new direction for future research involving applications of the methods explored, such as adjustments in statistical methods, sampling and data collection. The study specifically aims at helping food security analysts with tested and statistically robust tools for use in the analyses of the likelihood of food insecurity risk in settings with structural food insecurity issues. Moreover, it aimed to inform practice, policy and analysis in monitoring and evaluation of food insecurity risk in protracted crisis; thus helping in improving risk aversion measures. Utilising secondary data, the research examines relevant statistical techniques for determining predictors of food insecurity risk, namely; Principal Component Analysis; Multiple Correspondence Analysis; Classification and Regression Tree Analysis; Survey Logistic Regression, Generalized Linear Mixed Models for Ordered Categorical Data; and Joint Modelling. The study was conducted in the form of structured analysis of different datasets vi collected in the conflict-ridden South Sudan. Assets owned by households, as well as availability of livelihood endowments, was used as proxy for determining the level of resilience in particular demographic unit or geographical setting. The study highlighted the strengths and weaknesses of the techniques explored in the analysis as identifying or classifying potential predictors of food insecurity outcomes. Each technique is capable of generating a unique composite index for measuring the amount of resilience and predicting and classifying households according to food insecurity phase based on factor loadings. In general, the study determined that each method explored has peculiar strengths as well as limitations. However, a noteworthy implication observed is that asset-based statistical analysis, whether based on composite index that can be used as proxy for measuring the amount of resilience to food insecurity eventualities or on regression modelling approaches, does assure sufficient rigour in drawing conclusions about the wellbeing of households or populations under study and how they might withstand food insecurity and livelihood shocks. As food insecurity and malnutrition continue to attract substantial attention, such flexible analytical approaches exert potential usefulness in determining food insecurity risks, especially in protracted crisis settings.
Statistical analysis of determinants of household food insecurity in post-conflict Southern Sudan.
(2009) Lokosang, Laila Barnaba.; Hendriks, Sheryl Lee.; Ramroop, Shaun.
Hunger and food insecurity has remained an endemic problem in Southern Sudan for the last three decades. Lack of a “gold standard” measure for determining causes of household food insecurity is well documented in the Food Security literature and the chase is still on for universally agreed standards. However, the Comprehensive African Agriculture Development Programme (CAADP)1 Framework for African Food Security (FAFS) has outlined four categorical measures for structured monitoring of household food insecurity, which are yet to be rolled out for implementation by country-level Food Security programmes. Analysis of a national household survey dataset has not been done using robust logistic regression techniques for statistically determining the factors influencing food insecurity in Southern Sudan. If such attempts are made, national food security programmes and the government statistical agency are not formally made aware of the results or do not own them. Hence, the agency has continued to lack institutional capacity to adapt the tools and techniques. This project attempts to explore the use of robust statistical techniques featuring the Ordinal Logistic Regression procedures of SPSS for analysing the Sudan Household Health Survey (2006) dataset and determine the strengths and magnitude of relationships of nineteen independent variables in predicting categories of food consumption scores. Food Security experts and international organisations, have regarded Food Consumption Scores as a proxy measure of Food Insecurity. Twelve factors were found to statistically determine food consumption. It is, therefore, ascertained that if this form of analysis were carried out immediately after the survey was completed it would have enabled prediction of the outcome of food insecurity in Southern Sudan for at least the following year. Nevertheless, the study found out that the same statistical modelling procedures could be adopted in similar national surveys. Indeed the study provides a basis for creating an institutional memory for statistical agencies to carry out similar analysis and thereby reducing data processing time without due reliance on outsourced international expertise.