The number of studies estimating neighbourhood effects has increased rapidly during the last two decades. Although results from these studies vary, a majority find at least small effects. But to what extent can we trust these estimates? Neighbourhood effect studies face many serious methodological challenges, of which some are related to the fact that people move. The mobility of individuals may cause neighbourhoods to change over time, result in exposure times that are too short and seriously bias estimates. These methodological problems have not been given enough attention in the neighbourhood effect literature: no study controls for them all, and implications of mobility are rarely included in theoretical discussions of neighbourhood effects. In a comprehensive summary and five different papers, I argue that the two scholarly fields of residential mobility and neighbourhood effect studies are intrinsically connected and that any arbitrary separation between the two is both conceptually problematic and risks leading to erroneous conclusions. Studies of neighbourhood effects must address the problems caused by mobility, before it can be convincingly argued that results actually show neighbourhood effects. To do this, longitudinal data are necessary. Furthermore, the connection between the two fields may also have implications for studies of residential mobility.

The project presented in this report is a master's thesis performed at Scania CV. The main purpose is to develop an algorithm that estimates the offset of the values that the steering wheel angle sensor reports in a truck or tractor, and also to investigate the possibility to estimate the steering wheel angle in real-time. The developed algorithm successfully estimates the offset to an accuracy on the order of degrees, and the uncertainty of the estimate is ultimately determined by the backlash in the steering system, which may range up to approximately 15 degrees or more depending on service standards. The investigation also shows that two general approaches to estimate the steering wheel angle in real-time can produce unbiased estimates only when the vehicle is cornering at low speeds.

Publication bias (PB) makes the resources for meta-analysis (M-A) unreliable in the sense of completion and accuracy, so to investigate, identify and correct PB is a very important issue in M-A. The current study proposed an empirical comparison in both detection and correcting PB, using a Monte Carlo study. Conditions to be manipulated include the number of primary studies, number of missing studies and true effect size. RANNOR in SAS will be used to generate normally distributed random variables and, for each condition, 10,000 M-As will be simulated. Type I error rates are to be calculated for the conditions with no PB and powers were estimated for the conditions with PB and adequate type I error control. Finally, a demonstration of how M-A can and should be used as a part of program evaluations was given. / text

The purpose of the present study was to determine whether computer-based cognitive bias modification (CBM) procedures could alter appraisal style toward viewing events from a big picture perspective and thereby influence emotional reactivity. Big picture appraisal entails viewing difficult situations and one's reactions to them in terms of a larger context. Appraisal training was implicit in that participants completed a series of vignettes, framed as a reading comprehension task, which trained either a big picture perspective or a personal/evaluative focus. When subsequently confronted with novel vignettes, participants produced interpretations that were consistent with assigned training condition. In addition, participants trained in big picture as compared to personal/evaluative appraisal subsequently demonstrated less emotional reactivity to a stressful task. / text

A simplistic view of a dataset is that it is collection of numbers. In fact data are much more than that and all data are collected at a set place and time. Often either the location, or the time, is fixed within the dataset and one or both are disregarded. When the place and time of the collection are incorporated into the analysis, the result is a spatio-temporal model. Spatio-temporal data are the focus of this thesis. The majority of the datasets used are radio tracking studies of animals where the objective is to measure the habitat use. Observations are made over a long period of time and a large area. The largest dataset analysed tracks over a hundred animals, in an area larger than 40 square miles, for multiple years. In this context understanding the spatio-temporal relationships between observations is essential. Even data that do not have an obvious spatial component can benefit from spatio-temporal analysis. For example, the data presented on volatility in the stock market do not have an obvious spatial component. The spatial component is the location in the market, not a physical location. Two different methods for measuring and correcting bias are presented. One method relies on direct modelling of the underlying process being observed. The underlying process is animal movement. A model for animal movement is constructed and used to estimate the missing observations that are thought to be the cause of the bias. The second method does not model the animal movement, but instead relies on a Bayesian Hierarchical Model with some simple assumptions. A long running estimation is used to calculate the most likely result without ever directly estimating the underlying equations. In the second section of the thesis two methods for measuring change from shifts in both spatial and temporal location are presented. The methods, Large Diffeomorphic Deformation Metric Mapping (LDDMM) and Diffeomorphic Demons (DD), were originally developed for anatomical data and are adapted here for nonparametric regression surfaces. These are the first applications of LDDMM and DD outside of computational anatomy.

This research has two objectives. The first objective is to analyze how former combatants of World War II now present the 'facts' of that struggle to their current student population. To accomplish this, eight secondary school history textbooks were selected with the assistance of the International Textbook Institute in Braunschweig, Federal Republic of Germany. The chosen texts are from The United States, England, Italy, West Germany, The Soviet Union, Poland, Hungary, and East Germany. The six non-English textbooks were literally translated into English. The second objective is to create comparative education research methodologies that are compatable with the incipient power of microcomputers. The 92,707 words in the bodies of the textbooks are submitted to six analytic techniques to assess the nature of the information within them. The first three techniques are 'time-centered', and the last three are 'event-oriented'. All of the six techniques are structured as ad interim algorithms that are imposed onto a generic 'electronic calculating sheet' software program for microcomputers. All appendices included in this study are data outputs from the computer program. This research suggests certain conclusions. First, that the specific affiliation of selected countries during World War II is not significant in the presentation of the 'facts' in their textbooks; whereas, the present affiliation (Nato/Warsaw Pact) is significant. Second, the communist texts are, relative to the Western texts, quite political; however, the Western texts are generally academically less rigorous. Third, all of the selected texts tend to be ethnocentric by selecting and avoiding 'facts', and ignoring some of their negative behaviors in the struggle.

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I examine whether religiosity is positively associated with the valuation multiples investors assign to fair valued items that are prone to managerial bias. Using a sample of U.S. banking firms, I find that the value relevance of net assets that are hard to verify is higher for firms located in more religious areas than for those located in less religious areas. Moreover, I hypothesize and find that audit quality and firm information environment quality moderate the positive association between religiosity and value relevance. I perform several robustness checks. First, I rule out several alternative explanations to my results. Second, I address the concern that my results suffer from an omitted correlated variable problem. Third, I show that my results hold for firms located in Urban as well as Rural areas. / Thesis (Ph.D, Management) -- Queen's University, 2013-11-28 11:01:35.578