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Register a new userInequality in Education: A Comparison of Australian Indigenous and Nonindigenous Populations
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Added by
David Gunawan
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
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Educational achievement distributions for Australian indigenous and nonindigenous populations in the years 2001, 2006, 2014 and 2017 are considered. Bayesian inference is used to analyse how these ordinal categorical distributions have changed over time and to compare indigenous and nonindigenous distributions. Both the level of educational achievement and inequality in educational achievement are considered. To compare changes in levels over time, as well as inequality between the two populations, first order stochastic dominance and an index of educational poverty are used. To examine changes in inequality over time, two inequality indices and generalised Lorenz dominance are considered. Results are presented in terms of posterior densities for the indices and posterior probabilities for dominance for the dominance comparisons. We find some evidence of improvement over time, especially in the lower parts of the indigenous distribution and that inequality has significantly increased from 2001 to 2017.
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Studies in Australian Indigenous astronomical knowledge reveal few accounts of the visible planets in the sky. However, what information we do have tells us that Aboriginal people were close observers of planets and their motions, noting the relative brightness of the planets, their motions along the ecliptic, retrograde motion, the relationship between Venus and its proximity to the Sun, Venus connection to the Sun through zodiacal light, and the synodic cycle of Venus, particularly as it transitions from the Evening Star to the Morning Star. The dearth of descriptions of planets in Aboriginal traditions may be due to the gross incompleteness of recorded astronomical traditions, and of ethnographic bias and misidentification in the anthropological record. Ethnographic fieldwork with Aboriginal and Torres Strait Islander communities is revealing new, previously unrecorded knowledge about the planets and their related phenomena.
To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from elite athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day (rolling average, exponentially weighted moving average, acute:chronic workload ratio, monotony and strain). Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC$<$0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Learning curves suggested logistic regression was underfitting the load-injury relationship and that using a more complex model or increasing the amount of model building data may lead to future improvements. Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting they are limited as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training data may lead to the development of improved predictive models for injury prevention.
Migration the main process shaping patterns of human settlement within and between countries. It is widely acknowledged to be integral to the process of human development as it plays a significant role in enhancing educational outcomes. At regional and national levels, internal migration underpins the efficient functioning of the economy by bringing knowledge and skills to the locations where they are needed. It is the multi-dimensional nature of migration that underlines its significance in the process of human development. Human mobility extends in the spatial domain from local travel to international migration, and in the temporal dimension from short-term stays to permanent relocations. Classification and measurement of such phenomena is inevitably complex, which has severely hindered progress in comparative research, with very few large-scale cross-national comparisons of migration. The linkages between migration and education have been explored in a separate line of inquiry that has predominantly focused on country-specific analyses as to the ways in which migration affects educational outcomes and how educational attainment affects migration behaviour. A recurrent theme has been the educational selectivity of migrants, which in turn leads to an increase of human capital in some regions, primarily cities, at the expense of others. Questions have long been raised as to the links between education and migration in response to educational expansion, but have not yet been fully answered because of the absence, until recently, of adequate data for comparative analysis of migration. In this paper, we bring these two separate strands of research together to systematically explore links between internal migration and education across a global sample of 57 countries at various stages of development, using data drawn from the IPUMS database.
An educational system, the tutor-web (http://tutor-web.net), has been developed and used for educational research. The system is accessible and free to use for anyone having access to the Web. It is based on open source software and the teaching material is licensed under the Creative Commons Attribution-ShareAlike License. The system has been used for computer-assisted education in statistics and mathematics. It offers a unique way to structure and link together teaching material and includes interactive quizzes with the primary purpose of increasing learning rather than mere evaluation. The system was used in a course on basic statistics in 2011. Three types of data were gathered during the course. A randomized crossover experiment was conducted to assess the possible difference in learning (measured by repeated exams) between students using the system and students doing regular homework. The difference between the groups was not found to be significant. Responses to quiz questions were collected and analysed with item response theory type models. These analysis were used to improve the item banks. Finally, the students answered an in-class survey regarding their experience using the tutor-web. The responses of the students gave clear indications of student preferences.
This article describes mathematical methods for estimating the top-tail of the wealth distribution and therefrom the share of total wealth that the richest $p$ percent hold, which is an intuitive measure of inequality. As the data base for the top-tail of the wealth distribution is inevitably less complete than the data for lower wealth, the top-tail distribution is replaced by a parametric model based on a Pareto distribution. The different methods for estimating the parameters are compared and new simulations are presented which favor the maximum-likelihood estimator for the Pareto parameter $alpha$. New criteria for the choice of other parameters are presented which have not yet been discussed in the literature before. The methods are applied to the 2012 data from the ECB Household and Consumption Survey (HFCS) for Germany and the corresponding rich list from the Manager Magazin. In addition to a presentation of all formulas, R scripts implementing them are provided by the author.
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