School of Science

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Browsing School of Science by Advisor "Kazembe, Lawrence"

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    A hierarchical non-parametric Bayesian testlet model for dual local dependence
    (University of Namibia, 2021) Charamba, Vonai; Kazembe, Lawrence
    The use of psychometric tests to measure the level of individuals on unobservable traits is common in many fields and item response theory (IRT) models are usually used for proficiency measurement. Standard IRT models assume local person and item independence and normality assumption for the true ability distribution. However, respondents are often clustered and test items are often grouped according to sub-content measuring the same stimuli or sub-component of the trait. This study presents a non-parametric polytomous multilevel testlet model for simultaneously modelling person and item clustering effects. The grouping variable is assumed unknown and determined from the data using the Dirichlet Process. The model was compared with a parametric dual model with groups assumed to be known, the testlet model, the Generalised Partial Credit Model and the multilevel model accounting for person dependence effects only in terms of systematic, random and total errors in person and item parameter estimation and test information and reliability, for simulated and real life data. The effects of ignoring dual dependence effects were evaluated for variant group, sample, testlet size, number of response options and mixed items tests. Groups of size 5, 20 and 40 were compared for 400, 1000 and 2000 respondents. The effects of ignoring dependence effects were compared for 6 testlets of 3, 6 and 10 items each for 3, 4 and 5 response categories were compared. Consequences of mis-specifying the slope and proficiency distributional parameters were evaluated where the competing models estimated item and person parameters for skewed, bimodal, normal and uniformly distributed traits and constant and stochastic slopes. Three dependency conditions (0, xi none; 0.5, medium; 1, large), were considered for both local item dependency (LID) and local person dependency (LPD). For each simulation study, a fully-crossed factorial design was employed and the general linear model was employed for comparing estimation errors. Significant different means were detected by use of Cohen’s effect size, f. In general, ignoring LPD effects resulted in increased bias and total errors in the estimation of ability parameters while ignorance of LID negatively impacted on item parameter estimation increasing with sample size, testlet size and number of options. Failure to account for LID resulted in underestimation of proficiency standard errors, thus resulting in overestimation of test information and reliability. When dual dependence effects were ignored, both item and ability parameter estimation accuracy was reduced. The non-parameter model detected the number of groups and group membership well especially for smaller groups, increasingly with sample size, testlet size and number of categories. However, the non-parametric models requires high computational performance Considering the consequences of ignoring random effects and the computation efficiency required by the non-parametric model, it is recommended that the model be used to detect dependence effects and groups, and standard IRT models be applied for independent persons
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    Statistical modelling of the association between dietary diversity, dietary patterns and non-communicable diseases in Namibia
    (University of Namibia, 2024) Mbongo, Laina Tulipomwene; Kazembe, Lawrence
    Globalization coupled with urbanization has placed a significant pressure on the food systems of many developing countries. This has led to lifestyle changes that have become one of the most important influences on dietary patterns. The nutritional transition has affected the dietary pattern and nutrient intake greatly and has led to a rise in the purchases and consumption of processed and convenience foods. Analysis in nutritional epidemiology typically examined diseases in relation to a single or a few nutrients or foods. However, people do not eat isolated nutrients. Instead, they eat meals consisting of a variety of foods with complex combinations of nutrients. The high degree of inter-correlation among nutrients as well as among foods makes it difficult to attribute effects to single dietary components. Dietary patterns can influence health and the risk of developing chronic conditions. Therefore, to gain full understanding of the relationship between diet and the development of non-communicable diseases (NCD), it is desirable to use several methodological approaches. The main objective of this study was to explore the linkages between dietary patterns, dietary diversity and prevalence of non-communicable diseases. Specifically, the study aimed at: (i) applying count models on dietary diversity in Namibia, (ii) using bivariate count modelling approach in analyzing convenience and non-convenience consumption food preference in Windhoek, (iii) applying copula joint modelling of food insecurity indicators with application to food insecurity prevalence (FIP), household dietary diversity score (HDDS) and months of inadequate household food provisioning (MIHFP), (iv) fitting multiple indicators-multiple-cause modelling to examine the relationship between foods consumed and non-communicable diseases. The analysis used two representative survey data, namely the AFSUN-HCP Household Food Security Baseline Survey (2016) and Namibian Household and Income Expenditure (NHIES) of 2015/2016. The study focused on dietary diversity by using different count models. The household dietary diversity score presented a mean score of 6.5, suggesting a moderate diverse diet, with less consumption of food made from beans/lentils; eggs; fruits/vegetables and more consumption of starch food. Determinants for household dietary diversity included educational level, sex of head of household and main source of income (p-value <0.005). The study further used bivariate iii modelling approaches to analyze the food consumption patterns. The results found that, whereas the consumption of food monthly was more on the non-convenience foods, the purchases of convenience was frequent on a weekly basis and in multiple food sources. Moreover, the study employed copula joint modelling of food security indicators. The findings show that AIC of the untruncated (conditional/marginal) Poisson regression model was lower and thus proved to fit the data better. The Frank Copula and Bivariate Normal Copula best fitted the data of establishing the relationship between HFIP and HDDS, and between HFIP and MIHFP respectively. Lastly, we analyzed multiple indicators-multiple causes examining the relationship between foods consumed and non-communicable disease. Principal Component Analysis (PCA) and Structural Equation Models (SEM) were used as data reduction methods to derive dietary patterns. Fruits, foods such as condiments/tea/coffee and potatoes, yams, cassava, or any foods made from roots and tubers accounted for majority of the variation. The study concluded that the usage of appropriate methods for specific data types is very critical. Generalized Poisson Regression models through the usage copula approaches are best to analyze jointly two outcomes in order to test for significant relationships between high-level hierarchical effects (e.g., random effects). Specifically, the bivariate normal and the Frank Copula were found to fit the data best. The unique nature of the bivariate normal model is that it does not allow for a different dependence structure between the outcomes while the frank copula does not have tail dependence and it can model both positive and negative dependencies as the normal copula. SEM and PCA’s were used as data reduction methods. Lastly, the study concludes that food and nutrition insecurity is a major threat to the development of the country and the study recommends for strengthened advocacy for consumption of healthy and diverse diets in the country in order to slow down and arrest proliferation of non-communicable diseases