Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team.
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Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.
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The strengths of design-based research (DBR from now on) is that it teststheories in the real word of practice and in doing so generates newknowledge and artifacts useful to both the scientist and the practitioner.However, DBR’s strength might also be considered its weakness; becausethe research takes place in real-life settings, controlling for theinnumerable confounding variables is impossible. This means that theknowledge claims made on the basis of the research need to beunderstood not as a causal explanation, but rather as a plausibleinterpretation. This chapter looks at the concept of plausible rivalexplanations as a way to help design-based researchers to understandhow their knowledge claims can be better warranted by incorporating rivalexplanations into their research.
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It is well-documented that international enterprises are more productive. Only few studies have explored the effect of internationalization on productivity and innovation at the firm-level. Using propensity score matching we analyze the causal effects of internationalization on innovation in 10 transition economies. We distinguish between three types of internationalization: exporting, FDI, and international outsourcing. We find that internationalization causes higher levels of innovation. More specifically, we show that (i) exporting results in more R&D, higher sales from product innovation, and an increase in the number of international patents (ii) outward FDI increases R&D and international patents (iii) international outsourcing leads to higher sales from product innovation. The paper provides empirical support to the theoretical literature on heterogeneous firms in international trade that argues that middle income countries gain from trade liberalization through increases in firm productivity and innovative capabilities.
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The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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Praktijkgericht onderzoek is wetenschappelijk onderzoek dat wordt uitgevoerd met als primair doel om praktische impact in relevante werkvelden te realiseren. Praktische relevantie en methodische grondigheid zijn niet alleen abstracte eigenschappen van onderzoek, maar ook competentiedimensies die het praktijkgerichte onderzoek binnen het hoger beroepsonderwijs aandrijven. Bij methodische grondigheid gaat het in de kern om het vermogen de wetenschappelijke bewijskracht van het onderzoek te optimaliseren. Bij praktische relevantie om het vermogen te adviseren en interveniëren in de praktijk op basis van overtuigingskracht en het creëren van draagvlak. Deze twee dimensies verschillen wezenlijk van elkaar en vergroten de conceptuele helderheid binnen het praktijkgerichte onderzoek. Ze dragen zo bij aan betere demarcatie tussen theoriegericht en praktijkgericht onderzoek, betere integratie van onderzoeks- en beroepsonderwijs en betere verbinding tussen onderwijs in onderzoeksvaardigheden en lectoraatsonderzoek. Dit zal leiden tot een aanscherping van de methodologie, didactiek en het assessment van het praktijkgerichte onderzoek en daarmee tot verdere professionalisering en kwaliteitsverbetering.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Background: Collaboration between parents and speech and language therapists (SLTs) is seen as a key element in family-centred models. Collaboration can have positive impacts on parental and children’s outcomes. However, collaborative practice has not been well described and researched in speech and language therapy for children and may not be easy to achieve. It is important that we gain a deeper understanding of collaborative practice with parents, how it can be achieved and how it can impact on outcomes. This understanding could support practitioners in daily practice with regard to achieving collaborative practice with parents in different contexts. Aims: To set a research agenda on collaborative practice between parents and SLTs in order to generate evidence regarding what works, how, for whom, in what circumstances and to what extent. Methods & Procedures: A realist evaluation approach was used to make explicit what collaborative practice with parents entails. The steps suggested by the RAMESES II project were used to draft a preliminary programme theory about collaborative practice between parents and SLTs. This process generates explicit hypotheses which form a potential research agenda. Discussion & Conclusions: A preliminary programme theory of collaborative practice with parents was drafted using a realist approach. Potential contextual factors (C), mechanisms (M) and outcomes (O) were presented which could be configured into causal mechanisms to help explain what works for whom in what circumstances. CMO configurations were drafted, based on the relevant literature, which serve as exemplars to illustrate how this methodology could be used. In order to debate, test and expand our hypothesized programme theory for collaborative practice with parents, further testing against a broader literature is required alongside research to explore the functionality of the configurations across contexts. This paper highlights the importance of further research on collaborative practice with parents and the potential value of realist evaluation methodology
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Over the last two decades, institutions for higher education such as universities and colleges have rapidly expanded and as a result have experienced profound changes in processes of research and organization. However, the rapid expansion and change has fuelled concerns about issues such as educators' technology professional development. Despite the educational value of emerging technologies in schools, the introduction has not yet enjoyed much success. Effective use of information and communication technologies requires a substantial change in pedagogical practice. Traditional training and learning approaches cannot cope with the rising demand on educators to make use of innovative technologies in their teaching. As a result, educational institutions as well as the public are more and more aware of the need for adequate technology professional development. The focus of this paper is to look at action research as a qualitative research methodology for studying technology professional development in HE in order to improve teaching and learning with ICTs at the tertiary level. The data discussed in this paper have been drawn from a cross institutional setting at Fontys University of Applied Sciences, The Netherlands. The data were collected and analysed according to a qualitative approach.
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