College Students and the Diagnosis of Attention Deficit Hyperactivity Disorder

Abstract

Background: When H1N1 emerged in 2009, institutions of higher education were immediately faced with questions about how best to protect their community from the virus, yet limited information existed to help predict student preventive behaviors. Methods: The authors surveyed students at a large urban university in November 2009 to better understand how students perceived their susceptibility to and the severity of H1N1, which preventive behaviors they engaged in, and if policies impacted their preventive health decisions. Results: Preventive health behavior messaging had a mixed impact on students. Students made simple behavior changes to protect themselves from H1N1, especially if they perceived a high personal risk of contracting H1N1. Although policies were instituted to enable students to avoid classes when ill, almost no student self-isolated for the entire duration of their illness. Conclusions: These findings can help inform future decision making in a university setting to best influence preventive health behaviors.     

THE UNEQUAL ENFORCEMENT OF LIBERALIZATION: EVIDENCE FROM RUSSIA'S REFORM OF BUSINESS REGULATION

We document the unequal enforcement of liberalization reform of business regulation across Russian regions with different governance institutions, which leads to unequal effects of liberalization. National liberalization laws were enforced more effectively in subnational regions with more transparent government, a more informed population, a higher concentration of industry, and stronger fiscal autonomy. As a result, liberalization had a substantial positive effect on the performance of small firms and the growth of the official small business sector in regions with stronger governance institutions. In contrast, in regions with weaker governance institutions, we observe no effect of reform and, in some cases, even a negative effect.     

POLITICIANS AT WORK: THE PRIVATE RETURNS AND SOCIAL COSTS OF POLITICAL CONNECTIONS

We quantify the private returns and social costs of political connections exploiting a unique longitudinal dataset that combines matched employer–employee data for a representative sample of Italian firms with administrative archives on the universe of individuals appointed in local governments over the period 1985–1997. According to our results, the revenue premium granted by political connections amounts to 5.7% on average, it is obtained through changes in domestic sales but not in exports, and it is not related to improvements in firm productivity. The connection premium is positive for upstream producers for the public administration only, and larger (up to 22%) in areas characterized by high public expenditure and high levels of corruption. These findings suggest that the gains in market power derive from public demand shifts towards politically connected firms. We estimate that such shifts reduce the provision of public goods by approximately 20%.     

Are government wages interlinked with private sector wages?

The ongoing fiscal and financial crisis with significant macroeconomic imbalances in the euro area raises the question in how far public–private wage interaction and even wage spirals contribute to fiscal and competitiveness problems. In this vain we analyse empirically whether the evolution of public sector wages is decoupled from that of the private sector. Using data for number of OECD countries, we find: (i) a strong and extremely robust positive annual contemporaneous correlation of public and private sector wages over the business cycle; (ii) evidence of co-movement of these variables over the medium and long run. From a policy perspective, the findings of this study suggest public wage restraint and private wage flexibility coupled—where needed—with institutional reform to prevent or break public–private wage spirals.     

Generalized Least Squares Estimation in Contingency Tables Analysis: Asymptotic Properties and Applications

Different sorts of bilinear models (models with bilinear interaction terms) are currently used when analyzing contingency tables: association models, correlation models... All these can be included in a general family of bilinear models: power models. In this framework, Maximum Likelihood (ML) estimation is not always possible, as explained in an introductory example. Thus, Generalized Least Squares (GLS) estimation is sometimes needed in order to estimate parameters. A subclass of power models is then considered in this paper: separable reduced-rank (SRR) models. They allow an optimal choice of weights for GLS estimation and simplifications in asymptotic studies concerning GLS estimators. Power 2 models belong to the subclass of SRR models and the asymptotic properties of GLS estimators are established. Similar results are also established for association models which are not SRR models. However, these results are more difficult to prove. Finally, 2 examples are considered to illustrate our results.     

Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution

Count data analysis techniques have been developed in biological and medical research areas. In particular, zero-inflated versions of parametric count distributions have been used to model excessive zeros that are often present in these assays. The most common count distributions for analyzing such data are Poisson and negative binomial. However, a Poisson distribution can only handle equidispersed data and a negative binomial distribution can only cope with overdispersion. However, a Conway–Maxwell–Poisson (CMP) distribution [4] can handle a wide range of dispersion. We show, with an illustrative data set on next-generation sequencing of maize hybrids, that both underdispersion and overdispersion can be present in genomic data. Furthermore, the maize data set consists of clustered observations and, therefore, we develop inference procedures for a zero-inflated CMP regression that incorporates a cluster-specific random effect term. Unlike the Gaussian models, the underlying likelihood is computationally challenging. We use a numerical approximation via a Gaussian quadrature to circumvent this issue. A test for checking zero-inflation has also been developed in our setting. Finite sample properties of our estimators and test have been investigated by extensive simulations. Finally, the statistical methodology has been applied to analyze the maize data mentioned before.     

Hausman-type tests for individual and time effects in the panel regression model with incomplete data

By comparing estimators of the variance of idiosyncratic error at different robust levels, two Hausman-type test statistics are respectively constructed for the existence of individual and time effects in the panel regression model with incomplete data. The resultant test statistics have several desired properties. Firstly, they are robust to the presence of one effect when the other is tested. Secondly, they are immune to the non-normal distribution of the disturbances since the distributional conditions are not needed in the construction of the statistics. Thirdly, they have more robust performances than the main competitors in the literature when the covariates are correlated with the effects. Additionally, they are very simple and have no heavy computational burden. Joint tests for both of the two effects are also discussed. Monte Carlo evidence shows that the proposed tests have desired finite sample properties, and a real data analysis gives further support.     

The effect of teaching strategies on student achievement: An analysis using TALIS-PISA-link

The present study investigates the effects of different teaching strategies on student achievement using data from the so-called TALIS-PISA link created by the OECD. This is a recently developed instrument that allows for connecting data about teacher characteristics and practices collected in the TALIS (Teaching and Learning International Survey) survey with students’ academic performance measured in PISA (Programme for International Student Assessment). Our empirical strategy is based on an instrumental variable (IV) approach. Our results suggest that traditional teaching methods have a positive influence on students’ proficiency in mathematics, while the implementation of more innovative active learning strategies seem to have a negative impact on student achievement.     

Truncated regular vines in high dimensions with application to financial data

Using only bivariate copulas as building blocks, regular vine copulas constitute a flexible class of high‐dimensional dependency models. However, the flexibility comes along with an exponentially increasing complexity in larger dimensions. In order to counteract this problem, we propose using statistical model selection techniques to either truncate or simplify a regular vine copula. As a special case, we consider the simplification of a canonical vine copula using a multivariate copula as previously treated by Heinen & Valdesogo ( 2009 ) and Valdesogo ( 2009 ). We validate the proposed approaches by extensive simulation studies and use them to investigate a 19‐dimensional financial data set of Norwegian and international market variables. The Canadian Journal of Statistics 40: 68–85; 2012 © 2012 Statistical Society of Canada