Multilevel Modelling of Survey Data

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Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019, background): “• School (Goldstein, 1991) 6 consists of m = 15362 total student examination records described by D = 17 features, with an overall score in the range [0 , 70] from K = 140 secondary schools.”
Computationally Efficient Convolved Multiple Output Gaussian Processes (2011, background): “Band 1 corresponds to the highest 25%, band 2 corresponds to the next 50% and band 3 the bottom 25% (Nuttall et al., 1989; Goldstein, 1991).”
Convex multi-task feature learning (2008, methodology): “4 This data set has been used in previous work on multitask learning, for example in (Bakker and Heskes 2003; Evgeniou et al. 2005; Goldstein 1991).”
Age at first marriage in Malawi: a Bayesian multilevel analysis using a discrete time‐to‐event model (2005, background): “An analysis that assumes independence of the outcomes may result in an underestimation of variances (due to the reduced effective sample size) leading to an inflation of the significance of the effects (Goldstein, 1991).”
Efficient Multitask Feature and Relationship Learning (2017, background): “School Data This dataset consists of the examination scores of 15,362 students from 139 secondary schools (Goldstein, 1991).”