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                <text>This paper explores four Item Response Theory (IRT) models to determine the most appropriate for understanding item non-response. The selected IRT model was used to identify among five categories of survey questions, the most difficult to answer by respondents and determine the underlying mechanism behind missing data which is defined to include ‘don’t know’answers. A questionnaire data on Ghana collected in the fifth wave of the World Values Survey was implored. All items were dichotomously scored. Missing or ‘don’t know’responses were assigned a 0 score whiles answered items were assigned a 1 score. The four IRT models that were explored included both the constrained and unconstrained versions of the Rasch model, the two parameter logistic model (2-PLM), and the three parameter logistic model (3-PLM). The unconstrained Rasch model emerged as the most appropriate model for understanding item non-response. It was observed that, income related questions had the highest difficulty parameter, hence the most difficult category of survey questions to answer. It was also found that, if an individual does not answer a survey question or give a ‘don’t know’answer, it is not only due to the question’s difficulty but also because the respondent doesn’t want to answer.</text>
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Global fight against tuberculosis (TB) has received increasing attention over the years. However, the disease remains one of the top-most global health problems, especially in Sub-Saharan Africa and Ghana.&#13;
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We modelled reported TB cases between 2015 and 2018 using wavelet analysis and applied maximum covariance analysis (MCA) to determine regional and seasonal patterns and the risk of TB exposure in Ghana. This study is based on the old administrative regions of Ghana.</text>
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