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The experiment evaluated a method for statistically assessing the accuracy of technologies that measure intramuscular fat percentage (IMF%), enabling referencing against accreditation accuracy thresholds. To compare this method to the existing rules-based industry standard we simulated data for 4 separate devices that predicted IMF% across a range between 0.5–9.5% for sheep meat. These devices were simulated to reflect increasingly inaccurate predictions, and the two methods for statistically assessing accuracy were then applied. We found that for the technology which only just meets the accreditation accuracy standards, as few as 25 samples were required within each quarter of the IMF% range to achieve 80% likelihood of passing accreditation. In contrast, using the rules based approach at least 200 samples were required within each quarter of the IMF% range, and this increased the likelihood of passing to only 50%. This method has been developed into an on-line analysis App, which commercial users can freely access to test the accuracy of their technologies.

In the context of generalized linear models (GLMs), interactions are automatically induced on the natural scale of the data. The conventional approach to measuring effects in GLMs based on significance testing (e.g. the Wald test or using deviance to assess model fit) is not always appropriate. The objective of this paper is to demonstrate the limitations of these conventional approaches and to explore alternative strategies for determining the importance of effects. The paper compares four approaches to determining the importance of effects in the GLM using 1) the Wald statistic, 2) change in deviance (model fitting criteria), 3) Bayesian GLM using vaguely informative priors and 4) Bayesian Model Averaging analysis. The main points in this paper are illustrated using an example study, which examines the risk factors for cyber abuse victimization, and are further examined using a simulation study. Analysis of our example dataset shows that, in terms of a logistic GLM, the conventional methods using the Wald test and the change in deviance can produce results that are difficult to interpret; Bayesian analysis of GLM is a suitable alternative, which is enhanced with prior knowledge about the direction of the effects; and Bayesian Model Averaging (BMA) is especially suited for new areas of research, particularly in the absence of theory. We recommend that social scientists consider including BMA in their standard toolbox for analysis of GLMs.

The aim of this study was to identify sensory subtypes in children on the autism spectrum using the Short Sensory Profile-2 (SSP-2). Caregivers of children on the autism spectrum aged 4–11 years ( n  = 271) completed the SSP-2. Analysis using Dirichlet process mixture model identified a two-cluster model which provided the best solution to subtype sensory responses. Two distinct subtypes were identified: Uniformly elevated (67%) with high scores across all quadrants and Raised avoiding and sensitivity (33%) with raised scores in the avoiding and sensitivity quadrants. There were no differences between subtypes based on chronological age and autism characteristics measured using the social communication questionnaire (total score). Based on the SSP-2, children were reported to experience differences in responses to sensory input, in particular in the area of sensitivity and avoiding.

A preventative approach to classroom management is associated with increases in student engagement and improved teacher well-being. However, research indicates that many teachers use predominantly reactive practices, aimed at controlling student behaviour. Queensland state secondary school teachers (N = 587) were surveyed about the classroom management practices they most often used to prevent and respond to unproductive student behaviours in their classrooms. Findings indicated that teachers mainly relied on practices to establish expectations, practices from the Essential Skills for Classroom Management professional development resource, and practices which sanction students for behavioural infractions. There were, however, also encouraging indications that teachers try to prevent unproductive behaviour by fostering student engagement in learning activities while addressing misbehaviour in low-key ways. Implications for pre-service teacher preparation and ongoing professional development are discussed. [Author abstract]

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: * Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. * Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. * Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

This paper presents the findings from a mixed-methods examination of self-protective behaviours (SPBs) adopted by victims of cyber abuse from the rational choice perspective. The data from a sample of the U.S. adults (N=746), members of an online opt-in panel, were analysed to first distinguish the types of SPBs adopted by victims of cyber abuse using a thematic analysis of open-ended responses. We then identified the factors associated with an increased likelihood of adopting SPBs and the specific identified types of SPBs using logistic regression with Bayesian variable selection and a stochastic search algorithm. Of the six identified types of SPBs, adjusting privacy settings was the most commonly reported response, and improving security (e.g. changing passwords, etc.) was the least common SPB. Older victims who reported higher than the average perceived impact from victimisation, were abused by a stranger and experienced either surveillance of their online activities or multiple types of abuse, were significantly more likely to adopt an SPB. Our findings inform strategies for both Internet user education and for preventing cyber abuse victimisation.

Background This study has two objectives: (1) to identify the attitudinal and demographic factors most closely associated with performance-enhancing drugs (PED) use in sports; (2) to propose a new method for estimating PED use prevalence that mitigates the limitations of self-report surveys. Methods We analyse survey data from a sample of 2113 athletes attending ten UK universities, using a two-step approach. First, Bayesian Variable Selection (BVS) is used to identify the factors most closely associated with PED use. Second, these factors are incorporated into a Bayesian Profile Regression (BPR) model to identify clusters of athletes with similar attitudes toward PEDs. The prevalence of PED use is then estimated by linking these clusters with reported PED use via a logit model. Results Four key factors are significantly associated with PED use: being male, believing PED use is necessary to excel in sports, having a perceived estimate of PED use prevalence among elite athletes higher than the sample average, and having a perceived estimate of PED use prevalence among sportspeople in general higher than the sample average. BPR identifies four distinct clusters of athletes: (1) non-users; (2) use-admitters; (3) use-admitters and non-admitters; (4) most likely non-users. Using this new methodology, PED use prevalence is estimated at 13.7%, considerably higher than the 3.4% obtained through direct questioning. Conclusion The proposed method identifies attitudinal and demographic factors associated with PED use and provides a more accurate estimate of its prevalence, which can inform the development of more effective anti-doping programmes.

Lamb growth can be optimised with genetic selection using sire Australian sheep breeding values, however, breeding value expression has been shown to be reduced with poor nutrition. It was therefore hypothesised that the genetic potential for lamb growth would also be reduced, where production factors such as multiple births limit growth. Live weights at birth, weaning, and post-weaning were collected from more than 18,000 lambs produced over five years and eight locations of the Sheep Cooperative Research Centre Information Nucleus Flock experiment, and the impact of environment, production factors, and genotype was determined using mixed effects regression. The genetic potential for lamb growth was moderated by environment, multiple births, and sire type (p < 0.05). Twin lambs achieved 76% of the expected weight gain at weaning and 58% post-weaning. For triplet lambs weight gains were drastically less at approximately 30% of the expected gain at the same time points. Lambs born to maternal sires consistently had the poorest response to genetic selection, achieving approximately half the expected weight gain. Hence, producers need to temper expectations for growth based on genetic selection, or employ mitigation strategies such as precision feeding, the use of alternate breeds, or place emphasis on the genetic merit of other desirable traits.

Background It is well documented that mothers of children with intellectual disabilities or autism experience elevated stress, with mental health compromised. However, comparatively little is known about mothers of children with rare genetic syndromes. This study describes mental health and well-being in mothers of children with 13 rare genetic syndromes and contrasts the results with mothers of children with autism. Methods Mothers of children with 13 genetic syndromes ( n  = 646; Angelman, Cornelia de Lange, Down, Fragile-X, Phelan McDermid, Prader-Willi, Rett, Rubenstein Taybi, Smith Magenis, Soto, Tuberous Sclerosis Complex, 1p36 deletion and 8p23 deletion syndromes) and mothers of children with autism ( n  = 66) completed measures of positive mental health, stress and depression. Using Bayesian methodology, the influence of syndrome, child ability, and mother and child age were explored in relation to each outcome. Bayesian Model Averaging was used to explore maternal depression, positive gain and positive affect, and maternal stress was tested using an ordinal probit regression model. Results Different child and mother factors influenced different aspects of mental well-being, and critically, the importance of these factors differed between syndromes. Maternal depression was influenced by child ability in only four syndromes, with the other syndromes reporting elevated or lower levels of maternal depression regardless of child factors. Maternal stress showed a more complex pattern of interaction with child ability, and for some groups, child age. Within positive mental health, mother and child age were more influential than child ability. Some syndromes reported comparable levels of depression (SMS, 1p36, CdLS) and stress (SMS, AS) to mothers of children with autism. Conclusions Bayesian methodology was used in a novel manner to explore factors that explain variability in mental health amongst mothers of children with rare genetic disorders. Significant proportions of mothers of children with specific genetic syndromes experienced levels of depression and stress similar to those reported by mothers of children with autism. Identifying such high-risk mothers allows for potential early intervention and the implementation of support structures.