Browsing by Author "Luus, Retha"
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Item Assessing the effect of imbalance correction through oversampling in the prediction of injury prevalence in distance runners(University of the Western Cape, 2023) Ngoto, Simcengile; Luus, RethaPrediction of injury prevalence in distance runnersItem Maths4Stats: Opleiding vir onderwysers(AOSIS, 2013) Blignaut, Renette; Luus, Retha; Lombard, Ronell; Latief, Abduraghiem; Kotze, DanelleMaths4Stats: Educating teachers. The inadequate nature of the education infrastructure in South Africa has led to poor academic performance at public schools. Problems within schools such as under-qualified teachers and poor teacher performance arise due to the poorly constructed education system in our country. The implementation in 2012 of the Curriculum and Assessment Policy Statement (CAPS) at public schools in South Africa saw the further crippling of some teachers, as they were unfamiliar with parts of the CAPS subject content. The Statistics and Population Studies department at the University of the Western Cape was asked to join the Maths4Stats project in 2012. This project was launched by Statistics South Africa in an effort to assist in training the teachers in statistical content within the CAPS Mathematics curricula. The University of the Western Cape's team would like to share their experience of being part of the Maths4Stats training in the Western Cape. This article focuses on how the training sessions were planned and what the outcomes were. With the knowledge gained from our first Maths4Stats experience, it is recommended that future interventions are still needed to ensure that mathematics teachers become well-informed and confident to teach topics such as data handling, probability and regression analysis.Item Overweight/obesity and chronic health conditions in older people with intellectual disability in Ireland(Wiley, 2021) Ryan, Jennifer M.; Mccallion, Philip; Luus, RethaThis study examines overweight/obesity and chronic health conditions (CHCs) in older people with intellectual disability (ID).Data for this cross-sectional observational study emanated from Wave 2 of the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing, a longitudinal study assessing the health and well-being of older Irish adults with ID aged ≥40 years across all levels of ID. Participation involves an interview process and collation of objective health measures. In this study, body mass index (BMI) (n = 572), used as a measure of weight status, was examined with clustered doctor’s diagnosed CHCs. Descriptive analysis was conducted where counts (n) and proportions (%) were used to summarise the variables univariately, while cross-tabulations were used for bivariate summary into counts and proportions. With overweight/obesity prevalence established and patterns described using logistical regression, Pearson’s chi-squared test was used to test for significant associations.Item The statistical analysis of complex sampling data(University of the Western Cape, 2018) Paulse, Bradley; Luus, Retha; Blignaut, RénetteMost standard statistical techniques illustrated in text books assume that the data are collected from a simple random sample (SRS) and hence are independently and identically distributed (i.i.d.). In reality, data are often sourced through complex sampling (CS) designs, with a combination of stratification and clustering at different levels of the design. Consequently, the CS data are not i.i.d. and sampling weights that are developed over different stages, are calculated and included in the analysis of this data to account for the sampling design. Logistic regression is often employed in the modelling of survey data since the response under investigation typically has a dichotomous outcome. Furthermore, since the logistic regression model has no homogeneity or normality assumptions, it is appealing when modelling a dichotomous response from survey data. This research considers the comparison of the estimates of the logistic regression model parameters when the CS design is accounted for, i.e. weighting is present, to when the data are modelled using an SRS design, i.e. no weighting. In addition, the standard errors of the estimators will be obtained using three different variance techniques, viz. Taylor series linearization, the jackknife and the bootstrap. The different estimated standard errors will be used in the calculation of the standard (asymptotic) interval which will be compared to the bootstrap percentile interval in terms of the interval coverage probability. A further level of comparison is obtained when using only design weights to those obtained using calibrated and integrated sampling weights. This simulation study is based on the Income and Expenditure Survey (IES) of 2005/2006. The results showed that generally when weighting was used the estimators performed better as opposed to when the design was ignored, i.e. under the assumption of SRS, with the results for the Taylor series linearization being more stable.