Students pick up the perception that mathematics is abstract and therefore, the learning of mathematics would yield to them no benefit. With their attitude towards mathematics modelled and their interest for mathematics impacted by this automatic generated perception, they may never again appreciate the beauty of mathematics. In this paper, the researchers used structural equation modeling (SEM), to investigate the variables that affect students’ interest, among the variables, students’ confidence and motivation. The foregoing variables were conceptualized to have a direct effect on students’ interest in mathematics, whilst mathematics anxiety and students’ knowledge of the usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics moderated by students’ confidence and motivation. The result showed that significantly students’ confidence directly affects students’ interest in the learning of mathematics and there is a direct relationship between confidence and motivation. A student’s knowledge about the usefulness of mathematics indirectly increases the student’s interest in mathematics.
ObjectiveIdentifying hot spots for the overweight aids in effective public health interventions due to the associated public health burden and morbidities. This study, therefore aimed to explore and determine the spatial disparities in the overweight/obesity prevalence among women in Ghana. The study also aims at modelling the average body mass index (BMI) values using the spatial regression and the performance compared with the standard regression model.DesignThis is a cross-sectional study using data from the 2014 Ghana Demographic and Health Survey (GDHS).SettingThe study was set in Ghana.Participants and methodsData on 4393 non-pregnant women aged 15–49 years from the 2014 GDHS. Both global (Moran’s I) and the local indicators for spatial dependence were examined through the mapped BMI values across the country by clusters. An estimated spatial lag model was used to explain the spatial differences in the average body sizes of women.ResultsThe overall prevalence of overweight/obesity among reproductive women in Ghana was 35.4%, and this was highly prevalent among educated women (p<0.001), those from wealthy households (p<0.001) and dwelling in an urban setting (p<0.001). Significant clustering (Moran’s I=0.3145, p<0.01) of overweight/obesity was observed with hot spots (clustering) in Greater Accra, Central, Western and Ashanti regions. The spatial lag model was the best fit based on the Likelihood ratio test and the Akaike information criterion and Bayesian information criterion values. The mean age of women and household wealth were significant factors accounting for the increase in the average cluster body size (BMI) of women and the spatial differences.ConclusionThe prevalence of overweight/obesity was high and spatially clustered in the southern, middle and coastal regions. Geographic specific and effective public health interventions and strategies are needed to address the growing morbidity burden associated with the rise in the average body sizes of reproductive women.
Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.
River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.
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