Modeling the Airborne Infection Risk of Tuberculosis for a Research Facility in eMalahleni,South Africa

A detailed mathematical modeling framework for the risk of airborne infectious disease transmission in indoor spaces was developed to enable mathematical analysis of experiments conducted at the Airborne Infections Research (AIR) facility, eMalahleni, South Africa. A model was built using this framework to explore possible causes of why an experiment at the AIR facility did not produce expected results. The experiment was conducted at the AIR facility from August 31, 2015 to December 4, 2015, in which the efficacy of upper room germicidal ultraviolet (GUV) irradiation as an environmental control was tested. However, the experiment did not produce the expected outcome of having fewer infections in the test animal room than the control room. The simulation results indicate that dynamic effects, caused by switching the GUV lights, power outages, or introduction of new patients, did not result in the unexpected outcomes. However, a sensitivity analysis highlights that significant uncertainty exists with risk of transmission predictions based on current measurement practices, due to the reliance on large viable literature ranges for parameters.     

How Journalists Think about Media Effects—And Why We Should Care

Research suggests that journalists’ beliefs about media effects are influenced by unsystematically gathered knowledge and subjective-intuitive judgments. However, it has also been shown that these presumptions must be considered important factors for the formation of journalistic coverage. Against this background, this article synthesizes existing research on dimensions, determinants, and consequences of journalists’ presumptions of media effects. The resulting framework offers researchers in the field of journalistic content production a comprehensive overview of the possible role that presumptions of media effects could play for journalistic content creation. In a second step, we summarize the implications that the current state of research points at. We discuss why journalism scholars should integrate presumed media effects into their research agendas and what communication researchers, as well as journalists themselves, could do to promote more realistic beliefs about media effects among journalists.     

Shaped by place? Young people's aspirations in disadvantaged neighbourhoods

This paper aims to better understand the relationship between young people's aspirations towards education and jobs, and the context in which they are formed, especially to understand better the role of disadvantaged places in shaping young people's aspirations. Policy makers maintain that disadvantaged areas are associated with low aspirations and there is support for this position from academic work on neighbourhood effects and local labour markets, but evidence is slim. Using a two-stage survey of young people in disadvantaged settings in three British cities, the paper provides new data on the nature of young peoples’ aspirations, how they change during the teenage years, and how they relate to the places where they are growing up. The findings are that aspirations are very high and, overall, they do not appear to be depressed in relation to the jobs available in the labour market either by the neighbourhood context or by young people's perceptions of local labour markets. However, there are significant differences between the pattern of aspirations and how they change over time in the three locations. The paper then challenges assumptions in policy and in the literature that disadvantaged places equal low aspirations and suggests that understanding how aspirations are formed requires needs a nuanced approach to the nexus of class, ethnicity and institutional influences within local areas.     

Top Managers' cognition facilitates organisational ambidexterity: The mediating role of cognitive processes

It is empirically founded and tested that top management team group processes facilitate ambidexterity, whereas the role of top managers' cognition has remained empirically largely neglected. This study contributes to the literature of top managers' cognition by theoretically developing the relationship between cognitive frames and organisational ambidexterity, and the mediating process of cognitive differentiation and integration. The study empirically tests the proposed model in cross-sectional design by employing a sample of 101 top managers, using partial least squares structural equation modelling.The study shows that top managers' paradoxical frames encourage the engagement in cognitive differentiation and integration, whereas top managers' paradoxical frames are not significantly related to organisational ambidexterity. Solely appreciating exploitation and exploration does not foster ambidexterity, but is an antecedent for top managers' cognitive differentiation and integration. Top managers' cognitive differentiation and integration mediate the relationship between top managers' paradoxical frames and organisational ambidexterity. Additionally, top managers' cognitive integration moderates the effect between top managers' cognitive differentiation and organisational ambidexterity, which stresses the pivotal role of top managers' cognitive integration.     

Size and power of some cointegration tests under structural breaks and heteroskedastic noise

In this paper we examine the power and size distortions of a number of representative cointegration tests in some large-scale Monte Carlo simulations, when the underlying system is subjected to regime shifts and conditional (ARCH-type) heteroskedasticity. Following the suggestion by Gregory and Hansen, we select the minimum (maximum for the Johansen test) value for the statistics evaluated over a set of tentative break points for the regime shifts. The performance of these statistics is compared to the corresponding ordinary statistics in the presence of regime shifts. We demonstrate that the size of the cointegration tests is severely distorted by conditional heteroskedasticity.     

Using quantile regression for duration analysis

Summary Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression addresses the issue of right censoring of the response variable which is common in duration analysis. We compare quantile regression to standard duration models. Quantile regression does not impose a proportional effect of the covariates on the hazard over the duration time. However, the method cannot take account of time-varying covariates and it has not been extended so far to allow for unobserved heterogeneity and competing risks. We also discuss how hazard rates can be estimated using quantile regression methods. This paper benefitted from the helpful comments by an anonymous referee. Due to space constraints, we had to omit the details of the empirical application. These can be found in the long version of this paper, Fitzenberger and Wilke (2005). We gratefully acknowledge financial support by the German Research Foundation (DFG) through the research project ‘Microeconometric modelling of unemployment durations under consideration of the macroeconomic situation’. Thanks are due to Xuan Zhang for excellent research assistance. All errors are our sole responsibility.     

Estimating the reliability of repeatedly measured endpoints based on linear mixed‐effects models. A tutorial

There are various settings in which researchers are interested in the assessment of the correlation between repeated measurements that are taken within the same subject (i.e., reliability). For example, the same rating scale may be used to assess the symptom severity of the same patients by multiple physicians, or the same outcome may be measured repeatedly over time in the same patients. Reliability can be estimated in various ways, for example, using the classical Pearson correlation or the intra‐class correlation in clustered data. However, contemporary data often have a complex structure that goes well beyond the restrictive assumptions that are needed with the more conventional methods to estimate reliability. In the current paper, we propose a general and flexible modeling approach that allows for the derivation of reliability estimates, standard errors, and confidence intervals – appropriately taking hierarchies and covariates in the data into account. Our methodology is developed for continuous outcomes together with covariates of an arbitrary type. The methodology is illustrated in a case study, and a Web Appendix is provided which details the computations using the R package CorrMixed and the SAS software. Copyright © 2016 John Wiley & Sons, Ltd.