Generalized Network Psychometrics: Combining Network and Latent Variable Models

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of Structural Equation Modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework Latent Network Modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance-covariance structure of indicators is modeled as a network. We term this generalization Residual Network Modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms performs adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.Comment: Published in Psychometrik

Generalization Bounds in the Predict-then-Optimize Framework

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters. This loss function was recently introduced in Elmachtoub and Grigas (2017) and referred to as the Smart Predict-then-Optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out-of-sample, in the context of the SPO loss. Since the SPO loss is non-convex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points, but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions, and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation.Comment: Preliminary version in NeurIPS 201

Fatigue and progression of corpus callosum atrophy in multiple sclerosis

Fatigue is one of the most disabling symptoms in multiple sclerosis (MS) patients. There is no or only weak correlation between conventional magnetic resonance imaging (MRI) parameters and level of fatigue. The aim of this study was to investigate the relationship between progression of corpus callosum (CC) atrophy and fatigue in MS patients. This was a cohort study in 70 patients with relapsing form of MS (RRMS) and serial MRIs over a mean follow-up of 4.8years [67% female, mean age 42±11years, mean disease duration 9.7±7.6years, mean Expanded Disability Status Scale (EDSS) 2.8±1.6]. Fatigue was assessed by the Fatigue Severity Scale (FSS). CC size was measured with the CC index (CCI). In total, 40% of the patients suffered from fatigue (mean FSS score 5.3±1.1) and 60% patients had no fatigue (mean FSS score of 2.1±1). Patients with fatigue had higher EDSS scores (p=0.01) and CC atrophy was more pronounced in patients with fatigue (−21.8 vs. −12.1%, p=0.005). FSS correlated with CCI change over time (r=−0.33; p=0.009) and EDSS (p=0.008; r=0.361). The association between annualized CCI change and FSS was independent from EDSS, disease duration, gender and age in a multivariate linear regression analysis (p<0.001). Progression of CC atrophy may play a role in the evolution of MS-related fatigu

Development of students’ interest in particle physics as effect of participating in a Masterclass

The International Hands On Particle Physics Masterclasses are enjoying increasing popularity worldwide every year. In Germany a national program was brought to live in 2010, which offers these appreciated events to whole classes or courses of high school students all over the year. These events were evaluated concerning the issues of students’ interest in particle physics and their perception of the events. How several interest variables interact with each other and the perception of the events is answered by structural equation modelling (sect. 5.2). The results give information about the events’ effects on the students’ interest development in particle physics, show which event features are important (e.g. the authenticity) and give information about practical approaches to improve the effects of the Masterclasses. Section 5.3 deals with a group of participants which have a high interest in particle physics 6–8 weeks after the participation. The number of these students is remarkable large, with 26% of all participants. The investigation of this group shows that the Masterclass participation has the same positive effect on both sexes and all levels of physics education

Investigating the Effect of Septic Systems on Surface Water Quality in the Cayuga Lake and Hudson River Watersheds

The purpose of this study was to explore the effect of septic systems on surface water quality by comparing concentrations of faecal indicator bacteria data to the spatial distribution of septic systems and land use practices.This report was prepared for the New York State Water Resources Institute (WRI) and the Hudson River Estuary program of the New York State Department of Environmental Conservation, with support from the NYS Environmental Protection Fun