https://www.journals.ac.za/sasj <p>The journal will publish innovative contributions to the theory and application of statistics. Authoritative review articles on topics of general interest, which are not readily accessible in a coherent form, will be also be considered for publication. Articles of general or nontechnical nature will also be considered provided that the topic is of current interest to the theory, application or teaching of statistics. All papers are refereed.</p> South African Statistical Association (SASA) en-US South African Statistical Journal 0038-271X https://www.journals.ac.za/sasj/article/view/6673 <p>Rhino poaching in South Africa continues to threaten the existence of African rhino species. Since poachers often attack wildlife parks frequently, predictive models are essential for exploiting the availability of data to gain information about the poachers. Although a number of statistical methods have been applied to poaching prediction, they either do not take the spatial variation of observations into account, require additional observational data, depend on known priors, or result in models that are overfitted and challenging to interpret. This paper proposes the use of point process models to predict the spatial distribution of poaching activity within a wildlife park. Descriptive statistics of poaching spatial point patterns have been considered, as well as univariate non-parametric kernel density estimation. However, the focus of this work is on fitting multivariate parametric point process models, using a number of environmental factors. Since real-world poaching data could not be obtained for this work, due to the sensitivity of the data, a simulation study is performed, where numerous point patterns are generated from the same underlying point process. The method can be used when no data is available, and is based on environmental preferences of poachers, which can be obtained through expert knowledge, literature reviews, or by making intelligent assumptions. The results indicate that the point process models are able to predict the initial probabilities well, for most data generating processes. Point process models thus appear to be a promising method for predicting the spatial distribution of poaching activity.</p> Lisa Kirkland Inger Fabris-Rotelli Johan Pieter de Villiers Copyright (c) 2025 South African Statistical Journal https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode 2025-09-29 2025-09-29 59 2 59 84 10.37920/sasj.2025.59.2.1 https://www.journals.ac.za/sasj/article/view/6317 <p>In this paper, Bayesian statistical process control limits are derived for Cronbach's coefficient alpha in the case of the balanced one-way random effects model. Cronbach's alpha is one of the most commonly used measures for assessing a set of items' internal consistency or reliability, thereby assessing the assumption that they measure the same latent construct. By using the available data and the Jeffreys independence prior, the posterior distribution of and the predictive density of a future (unknown) Cronbach's alpha can be derived. Given a stable Phase I process, the predictive density function and the conditional predictive density functions are used to calculate central values, variances, control limits, run-lengths and the average run-length. The predictive density of a future run-length is the average of a large number of geometrical distributions, each with its own parameter value. Three applications of interest are included in this paper. From the results, it can be seen that the average and median run-lengths are usually larger than the theoretical values. An advantage of the Bayesian procedure, however, is that the control limits can be adjusted in such a way that the average or median run-length has a specific value.</p> Abraham J. van der Merwe Sharkay R. Izally Lizanne Raubenheimer Copyright (c) 2025 South African Statistical Journal https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode 2025-09-29 2025-09-29 59 2 85 108 10.37920/sasj.2025.59.2.2