This thesis presents new data on the work motivation of Metropolitan Police Service (MPS) constables. It offers data from two surveys: a snap-shot of the attitudes and behaviours of constables with seven years' service; and a survey of new recruits over their first fifteen months of service. It is perhaps the most comprehensive study undertaken of motivation to work hard and remain in a UK police force and includes a critical review of the motivation literature with specific reference to its relevance to the job of police constable. A provisional model is proposed to explain variance in the dependent variables - motivation to work hard (effort) and motivation to remain in the organisation (intention to stay/leave). New construct variables operationalise effort, reality shock, career frustration, organisational citizenship behaviour, socialisation and performance. Pre-existing scales are used for organisational commitment, management support, intention to leave, self efficacy, higher order need strength, organisational identification, intrinsic motivation and job satisfaction. The model met with a reasonable level of success: up to 46 per cent of the variance in intention to leave and up to 26 per cent of the variance in effort were explained. The effect of the model in explaining the outcomes of two organisational changes on the work motivation of experienced constables is also examined. Following the data analysis, a revised model is proposed. Motivation theories were shown to have validity and contribute to our understanding of work motivation. Variables explaining the work motivation of MPS constables were found to be similar to those in empirical research on other workers. However, probationary constables as a group were shown to have very unrealistic career expectations. Self-reported levels of effort decreased and levels of intention to leave increased over the socialisation period.

The purpose of the present study was to further investigate the effects of frustration on the reward value of a goal object. Under the assumption that animals will learn to take the shortest path to a goal and that the reward value of an object will increase as effort increases, the following hypothesis was proposed: If rats are frustrated in the short path of a single choice maze (experimental group) then the experimental group's running time for the long path will be less than that of a control group not frustrated in the short path.

The present study included three traumatic brain injury (TBI) groups (good effort mild TBI, poor effort mild TBI, and good effort moderate/severe TBI) and two neurologic control groups (dementia and unilateral left hemisphere stroke). Language impairment was examined using the following measures: Wechsler Adult Intelligence Scale-III Verbal Comprehension Index and the Vocabulary, Similarities, Information, and Comprehension subtests; the Boston Naming Test; the Phonemic and Semantic cue conditions of the Controlled Oral Word Association Test; the Auditory Comprehension subtest of the Cognistat; Wide Range Achievement Test-3 Reading subtest; and the Peabody Picture Vocabulary Test. When effort was controlled, there was a significant effect of injury severity on language impairment. Poor effort and diagnosable malingering were responsible for most of the neuropsychological test evidence of language impairment in mild TBI.

Attention impairment is one of the most common complaints following Traumatic Brain Injury (TBI). Multiple studies have shown that performance on neuropsychological tests of attention is affected by many factors, including injury severity and effort. The aim of this study was to determine the effect of injury severity on neuropsychological tests across different domains of attention while controlling for effort. The domains of focused attention, selective attention, divided attention, sustained attention, and working memory were assessed by performance on the Digit Span Forward subtest, the Stroop Color Word Test, the Trail Making Test, the Conners' Continuous Performance Test - II, and Digit Span Backwards subtest, respectively. Effort was determined according to performance on the Portland Digit Recognition Test and the Test of Memory Malingering. Effort was found to have a greater effect on test performance (.79) than injury severity (.47). Clinical implications of the findings are discussed.

Mail questionnaires are a popular and valuable method of data collection. Nonresponse bias is, however, a potentially serious threat to their validity. The best way to combat this threat is to obtain the highest possible return rate. To this end, many factors that are believed to influence return rates have been empirically studied. One factor that has not been empirically examined is the impact of item response effort on return rates, where response effort is defined as the amount of effort that is required by a respondent to answer questionnaire items. The purpose of this study was to determine if the type of item response effort required to complete a questionnaire had any differential impact on the response rate of a mailed questionnaire. For this study, two questionnaires that differed only in the level of item response effort were sent to two randomly selected and assigned groups. The first group received a mailed questionnaire with seven questions that were answered by a simple item response type (5-point Likert scale). The second group received a mailed questionnaire with seven questions that required a more difficult item response type (short answer). A large difference between the return rates of the two questionnaires was observed, with the questionnaire containing questions that could be answered on a Likert scale having a higher return rate (56%) than the questionnaire containing questions requiring a short written response (30%). The results of this study provide evidence that the difficulty of item response effort affects the response rate of mailed questionnaires. The practical application of this finding is that researchers should endeavor to keep the types of item response on mailed questionnaires as simple as possible, to maximize response rates (unless, of course, the needed information can only be elicited by providing written responses).

Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modi ed code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the eort required to correct them. Although software development eort prediction has been in use for many years, research into defect-correction eort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this eld. Eort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources eectively and e ciently. In particular, early defect-correction eort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases. In this work, we address the problem of predicting the eort needed to resolve a software defect. More speci cally, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce eort predictions. This approach usually suers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are aected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known eort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the eort, where the eect of such inconsistencies can be reduced. We have performed a number of experimental studies on the proposed framework to assess the eectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity of the model on real project data. These studies involved the development of multiple tools in both the Java programming language and PHP, each for a certain stage of data analysis and manipulation. The results show that our proposed approach produces signi cant improvements when compared to current methods.

Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modi ed code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the eort required to correct them. Although software development eort prediction has been in use for many years, research into defect-correction eort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this eld. Eort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources eectively and e ciently. In particular, early defect-correction eort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases. In this work, we address the problem of predicting the eort needed to resolve a software defect. More speci cally, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce eort predictions. This approach usually suers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are aected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known eort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the eort, where the eect of such inconsistencies can be reduced. We have performed a number of experimental studies on the proposed framework to assess the eectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity of the model on real project data. These studies involved the development of multiple tools in both the Java programming language and PHP, each for a certain stage of data analysis and manipulation. The results show that our proposed approach produces signi cant improvements when compared to current methods.

One of the fastest growing segments of Internet applications are real-time mul- timedia applications, like Voice over Internet Protocol (VoIP). Real-time multimedia applications use the User Datagram Protocol (UDP) as the transport protocol because of the inherent conservative nature of the congestion avoidance schemes of Transmis- sion Control Protocol (TCP). The e®ects of uncontrolled °ows on the Internet have not yet been felt because UDP tra±c frequently constitutes only » 20% of the total Internet tra±c. It is pertinent that real-time multimedia applications become better citizens of the Internet, while at the same time deliver acceptable Quality of Service (QoS). Traditionally, packet losses and the increase in the end-to-end delay experienced by some of the packets characterizes congestion in the network. These two signals have been used to develop most known °ow control schemes. The current research considers the °ow accumulation in the network as the signal for use in °ow control. The most signi¯cant contribution of the current research is to propose novel end- to-end °ow control schemes for unicast real-time multimedia °ows transmitting over best-e®ort networks. These control schemes are based on predictive control of the accumulation signal. The end-to-end control schemes available in the literature are based on reactive control that do not take into account the feedback delay existing between the sender and the receiver nor the forward delay in the °ow dynamics. The performance of the proposed control schemes has been evaluated using the ns-2 simulation environment. The research concludes that active control of hard real- time °ows delivers the same or somewhat better QoS as High Bit Rate (HBR, no control), but with a lower average bit rate. Consequently, it helps reduce bandwidth use of controlled real-time °ows by anywhere between 31:43% to 43:96%. Proposed reactive control schemes deliver good QoS. However, they do not scale up as well as the predictive control schemes. Proposed predictive control schemes are e®ective in delivering good quality QoS while using up less bandwidth than even the reactive con- trol schemes. They scale up well as more real-time multimedia °ows start employing them.

Thèse de doctorat : Epidémiologie et Santé Publique : Nancy 1 : 2008. / Titre provenant de l'écran-titre.

Thèse de doctorat : Sciences et techniques des activités physiques et sportives : Orléans : 2009. Thèse de doctorat : Sciences et techniques des activités physiques et sportives : Université de Balamand : 2009. / Thèse soutenue en co-tutelle. Titre provenant de l'écran-titre.