Assessing Internet addiction using the parsimonious Internet addiction components model - a preliminary study [forthcoming]

Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (2005), including salience, mood modification, tolerance, withdrawal, conflict, and relapse. Two validated measures of Internet addiction were used (Compulsive Internet Use Scale [CIUS], Meerkerk et al., 2009, and Assessment for Internet and Computer Game Addiction Scale [AICA-S], Beutel et al., 2010) in two independent samples (ns = 3,105 and 2,257). The fit of the model was analysed using Confirmatory Factor Analysis. Results indicate that the Internet addiction components model fits the data in both samples well. The two sample/two instrument approach provides converging evidence concerning the degree to which the components model can organize the self-reported behavioural components of Internet addiction. Recommendations for future research include a more detailed assessment of tolerance as addiction component

Asian Americans respond less favorably to excitement (vs. calm)-focused physicians compared to European Americans

OBJECTIVES: Despite being considered a model minority, Asian Americans report worse health care encounters than do European Americans. This may be due to affective mismatches between Asian American patients and their European American physicians. We predicted that because Asian Americans value excitement (vs. calm) less than European Americans, they will respond less favorably to excitement-focused (vs. calm) physicians. METHOD: In Study 1, 198 European American, Chinese American, and Hong Kong Chinese community adults read a medical scenario and indicated their preference for an excitement-focused versus calm-focused physician. In Study 2, 81 European American and Asian American community college students listened to recommendations made by an excitement-focused or calm-focused physician in a video, and later attempted to recall the recommendations. In Study 3, 101 European American and Asian American middle-aged and older adults had multiple online encounters with an excitement-focused or calm-focused physician and then evaluated their physicians\u27 trustworthiness, competence, and knowledge. RESULTS: As predicted, Hong Kong Chinese preferred excitement-focused physicians less than European Americans, with Chinese Americans falling in the middle (Study 1). Similarly, Asian Americans remembered health information delivered by an excitement-focused physician less well than did European Americans (Study 2). Finally, Asian Americans evaluated an excitement-focused physician less positively than did European Americans (Study 3). CONCLUSIONS: These findings suggest that while physicians who promote and emphasize excitement states may be effective with European Americans, they may be less so with Asian Americans and other ethnic minorities who value different affective states

Knowledge Spaces and Learning Spaces

How to design automated procedures which (i) accurately assess the knowledge of a student, and (ii) efficiently provide advices for further study? To produce well-founded answers, Knowledge Space Theory relies on a combinatorial viewpoint on the assessment of knowledge, and thus departs from common, numerical evaluation. Its assessment procedures fundamentally differ from other current ones (such as those of S.A.T. and A.C.T.). They are adaptative (taking into account the possible correctness of previous answers from the student) and they produce an outcome which is far more informative than a crude numerical mark. This chapter recapitulates the main concepts underlying Knowledge Space Theory and its special case, Learning Space Theory. We begin by describing the combinatorial core of the theory, in the form of two basic axioms and the main ensuing results (most of which we give without proofs). In practical applications, learning spaces are huge combinatorial structures which may be difficult to manage. We outline methods providing efficient and comprehensive summaries of such large structures. We then describe the probabilistic part of the theory, especially the Markovian type processes which are instrumental in uncovering the knowledge states of individuals. In the guise of the ALEKS system, which includes a teaching component, these methods have been used by millions of students in schools and colleges, and by home schooled students. We summarize some of the results of these applications

Sparsity with sign-coherent groups of variables via the cooperative-Lasso

We consider the problems of estimation and selection of parameters endowed with a known group structure, when the groups are assumed to be sign-coherent, that is, gathering either nonnegative, nonpositive or null parameters. To tackle this problem, we propose the cooperative-Lasso penalty. We derive the optimality conditions defining the cooperative-Lasso estimate for generalized linear models, and propose an efficient active set algorithm suited to high-dimensional problems. We study the asymptotic consistency of the estimator in the linear regression setup and derive its irrepresentable conditions, which are milder than the ones of the group-Lasso regarding the matching of groups with the sparsity pattern of the true parameters. We also address the problem of model selection in linear regression by deriving an approximation of the degrees of freedom of the cooperative-Lasso estimator. Simulations comparing the proposed estimator to the group and sparse group-Lasso comply with our theoretical results, showing consistent improvements in support recovery for sign-coherent groups. We finally propose two examples illustrating the wide applicability of the cooperative-Lasso: first to the processing of ordinal variables, where the penalty acts as a monotonicity prior; second to the processing of genomic data, where the set of differentially expressed probes is enriched by incorporating all the probes of the microarray that are related to the corresponding genes.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS520 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Loud and clear? Can we hear when the SARB speaks?

Inflation targeting is a forward-looking framework for monetary policy that has brought unprecedented transparency to the process of monetary policy. This paper aims to assess the degree to which the South African Reserve Bank’s (SARB) Monetary Policy Committee (MPC) has, since the introduction of inflation targeting, successfully communicated to the public its policy analysis, and, in particular, the expected future policy changes. This paper follows international literature (Rosa and Verga (2007), Ehrmann and Fratszcher (2005)) in constructing a numerical index that is used to reflect the information content of the SARB’s communications, specifically the monetary policy statements that accompanied each of the MPC meetings since 2000. Relating this index to subsequent policy decisions reveals the informativeness of the index and, by implication, the informativeness of the underlying monetary policy statements. This method allows us to judge, systematically, the degree to which the MPC has communicated successfully, and the evolution of that success over the past nine years. We find evidence that the MPC has succeeded in signalling their likely future policy decision with consistency over this period.South Africa, Inflation targeting, Central bank communication, Effective monetary policy, forward-looking policy framework