A caution on quantity incentives and the use of economic models for higher education policy

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The external validity of treatment effects: an investigation of educational production

Includes bibliographical references.The thesis begins, in chapter 1, with an overview of recent debates concerning the merits of randomised programme evaluations and a detailed review of the literature on the extrapolation of treatment effects ('external validity'). Building on the insights of Cook and Campbell (1979) and a result by Hotz, Imbens, and Mortimer (2005), I then argue that the fundamental challenge to external validity may be interactive relationships between the treatment variable and other covariates. The empirical relevance of this claim is developed through two contributions to the economics of education literature, using data from the Tennessee class size experiment known as 'Project STAR'. Chapter 2 contributes to the literature on teacher quality, describing and implementing a novel method for constructing a value-added quality measure that uses a single cross-section of data in which students and teachers are randomly assigned to different-sized classes. The core insight is that constructing the value-added measure within treatment categories creates a plausible measure of quality that is simultaneously independent of treatment. The analysis of chapter 3 concerns the literature on class size effects. I argue that the effect of class size on educational achievement may be dependent on other class-level factors and that this should be considered when estimating educational production functions. Using the variable constructed in chapter 2, I estimate interaction effects between class size and teacher quality and find a number of statistically and economically significant effects. Specifically, higher quality teachers are associated with more beneficial effects of smaller classes. Those results suggest a possible unification of the class size and teacher quality literatures, with the policy problem being one of finding an optimal combination of these two factors. The broader contribution, further to the analysis of chapter 1, is to illustrate an obstacle to external validity: class size effects are unlikely to be the same across contexts where the teacher quality distribution differs. The experimental estimation of class size effects therefore serves as an empirical case study of the challenges to external validity that arise from interaction

Considerations for application of skill acquisition in Sport : an example from tennis

Skill acquisition principles are crucial to prepare athletes for superior performance in sport, but, in training athletes, coaches have focused less on these principles than they have on the design of training. This paper provides an overview of how a skill acquisition specialist disseminated scientific knowledge to amateur and professional coaches and initiated collaboration to improve practice design. First, a framework of representative task design is outlined, which considers perception and action components of sports skills in practice tasks relative to the competition setting. Second, with elite tennis as an example, steps are described as to how the skill acquisition specialist can initiate collaboration with coaches to evaluate practice tasks and make recommendations using representative task design. This approach includes delivery of a seminar to educate coaches, observation of practice tasks to rate representative task design with recommendations made, and factors identified by coaches that should be considered when applying skill acquisition principles. Factors identified by coaches related to presentation of anticipatory cues, practice variability, individualization of practice, skill complexity, and consistency of skill tests. Collectively, this paper provides insight into how skill acquisition specialists can collaborate with coaches to disseminate knowledge, and it presents some of the challenges and solutions of designing representative practice tasks in sport

Lessons from the Operational Use of the Gag Jet Engine at Mine Sites

Rio Tinto Coal Australia (RTCA), Queensland Mines Rescue Service (QMRS) and Simtars conducted a partial inertisation of an underground coal mine using the GAG-3A engine in February 2014. This was an ACARP funded project No C23006. This project monitored the environmental conditions and the flow of inert GAG gases into and around the mine. Observations were made by a variety of remote sensing technologies and by direct measurements as well as observations made by mines rescue personnel in the inertised area. This paper documents methodology, observations and the outcomes including a review of previous inertisations. This project proved that the GAG is a reliable and effective inertisation system. It demonstrated the critical requirement for effective sealing of GAG docking points. It is not envisaged that the GAG could be deployed where there is an expectation that mine personnel could be in the vicinity as temperatures of 90°C were measured. Mine infrastructure and strata in zones close to the GAG docking station were detrimentally affected by prolonged exposure to the high humidity and temperature

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

The Semantic Web MIDI Tape: An Interface for Interlinking MIDI and Context Metadata

The Linked Data paradigm has been used to publish a large number of musical datasets and ontologies on the Semantic Web, such as MusicBrainz, AcousticBrainz, and the Music Ontology. Recently, the MIDI Linked Data Cloud has been added to these datasets, representing more than 300,000 pieces in MIDI format as Linked Data, opening up the possibility for linking fine-grained symbolic music representations to existing music metadata databases. Despite the dataset making MIDI resources available in Web data standard formats such as RDF and SPARQL, the important issue of finding meaningful links between these MIDI resources and relevant contextual metadata in other datasets remains. A fundamental barrier for the provision and generation of such links is the difficulty that users have at adding new MIDI performance data and metadata to the platform. In this paper, we propose the Semantic Web MIDI Tape, a set of tools and associated interface for interacting with the MIDI Linked Data Cloud by enabling users to record, enrich, and retrieve MIDI performance data and related metadata in native Web data standards. The goal of such interactions is to find meaningful links between published MIDI resources and their relevant contextual metadata. We evaluate the Semantic Web MIDI Tape in various use cases involving user-contributed content, MIDI similarity querying, and entity recognition methods, and discuss their potential for finding links between MIDI resources and metadata

Simulating the Impact of Personality on Fake News

Fake news is a key issue for social networks. We use an agent-based network simulation to model the spread of (fake) news. The agents' behaviour captures the OCEAN ("big five") personality trait model. The network is homophilic for political preference, analytical thinking and emotion. We studied the system with personality traits and homophily each turned on/off. Personality traits and homophily exhibited a statistically significant but typically minimal impact. Ignoring personality traits when modelling fact-checking can overestimate its effectiveness

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts

Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure