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The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

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Essen, Universiẗat, Diss., 2006--Duisburg.

This study is unique considering the location (Africa) and the industrial setting (Gold Mining) from which the research was studied as reward systems had mostly been studied in the North-American and European settings. Thus, the study  considered  rewards from the perspective of the African and its natural resource industries such as the gold mining industry.   The methodology employed in the study was based on a case study approach at Golden Star (Bogoso/Prestea) Limited (GSB/PL) with a population size of 1029 employees combining both qualitative and quantitative data obtained through a questionnaire survey of a 278 sample size and structured interview with the Human Resources and Administration Manager. Thus, the method of data collection represents methodological triangulation and the data obtained from the study represents a primary source of data.   The study revealed that all the three generational groups (Baby Boomers, GEN Xers and   GEN Yers) places higher emphasis or priority on financial incentives (high salary and bonuses) over any other incentives when respondents were asked to indicate the reward they prefer most. But when rewards were considered as a total package profile, greater number of  the baby boomers placed more emphasis or priority on packages with highly flexible pension benefits, long term job security and high internal promotions eventhough the salary and bonus components of the packages (profile) were not that attract. The GEN X and GEN Y groups still maintained their reward package profile preferences based on  high financial incentives, training and learning opportunities, personal growth and career advancement.   The study revealed that aside the high preferences for financial incentives such as high salary and bonuses by all the generational groups, few of the  GEN X and GEN Y also exhibited other preferences such as high personal growth, flexible work schedule, attractive company policy and administration, career advancement, working environment, job security and praises and recognition of which the baby boomers did not indicate any preferences or interest.   The study revealed that all the three generational groups (Baby Boomers, GEN X and GEN Y) consider high salary and bonuses as factor which causes employee dissatisfaction when not satisfied or available but when they are satisfied or available also do not motivate or cause satisfaction and thus  confirming Herzberg Two-Factor theory that  factors such as salary or remuneration, job security, working conditions and company policies  only prevent employee dissatisfaction.   The study revealed that all generational groups (baby boomers, GEN X and GEN Y) consider high salaries and bonuses as factor which could lead to lack of satisfaction and motivation of the employee in his current role or position when not available or satisfied and thus this finding confirm the traditional belief that pay is prime, or in some cases the only source of motivation but contradict Herzberg claim that  pay (high salaries and bonuses) is only an extrinsic factor and that when is available or satisfied, pay does not bring satisfaction and motivation but rather prevents dissatisfaction.   The study revealed that GSB/PL rewards systems basically comprises of extrinsic rewards such as high salary levels (pay increases), a bonus scheme,  training  and learning opportunities, job security, Stock options, Retirement/Pension benefits such as social security and provident fund,  promotions,  attractive company policies and administration, praises and recognition, good working environment, flexible work schedule,  Long service awards and benefits such as housing, Health insurance, Vacation/Annual leave benefits, transportation/bussing service, messing (provision of meals to employees only when at work), and educational benefits (for employees dependants).   The study also revealed that the design and implementation of GSB/PL reward systems involves four distinct phases: assessment, design, execution and evaluation phases.   In the end, a suitable conclusion was drawn and a number of recommendations proposed to be implemented by the mining company in safeguarding the interest of both employees and the employer.

1 AbstraktEN The diploma thesis deals with linear mixed effects models. In the first chap- ter, we discuss parameter estimation and hypothesis testing in the linear mixed effects models. The second chapter is dedicated to graphical diagnostics. We look at the suitable diagnostic plots for residuals and random effects estimates. It is closely described, how the violations of assumptions affect the diagnostic plots. In the third chapter we have consequences of the violations of assumptions on the parameter estimates and results of hypothesis testing for fixed effects. 1

Developmental score scales represent the performance of students along a continuum, where as students learn more they move higher along that continuum. Unidimensional item response theory (UIRT) vertical scaling has become a commonly used method to create developmental score scales. Research has shown that UIRT vertical scaling methods can be inconsistent in estimating grade-to-grade growth, within-grade variability, and separation of grade distributions (effect size) of developmental score scale. In particular the finding of scale shrinkage (decreasing within-grade score variability as grade-level increases) has led to concerns about and criticism of IRT vertical scales. The causes of scale shrinkage have yet to be fully understood. Real test data and simulation studies have been unable to provide complete answers as to why IRT vertical scaling inconsistencies occur. Violations of assumptions have been a commonly cited potential cause for the inconsistent results. For this reason, this dissertation is an extensive investigation into how violations of the three assumptions of UIRT vertical scaling - local item dependence, unidimensionality, and similar reliability of grade level tests - affect estimated developmental score scales. Simulated tests were developed that purposefully violated a UIRT vertical scaling assumption. Three sets of simulated tests were created to test the effect of violating a single assumption. First, simulated tests were created with increasing, decreasing, low, medium, and high local item dependence. Second, multidimensional simulated tests were created by varying the correlation between dimensions. Third, simulated tests with dissimilar reliability were created by varying item parameters characteristics of the grade level tests. Multiple versions of twelve simulated tests were used to investigate UIRT vertical scaling assumption violations. The simulated tests were calibrated under the UIRT model to purposefully violate an assumption of UIRT vertical scaling. Each simulated test version was replicated for 1000 random examinee samples to assess the bias and standard error of estimated grade-to-grade-growth, within-grade-variability, and separation-of-grade-distributions (effect size) of the estimated developmental score scales. The results suggest that when UIRT vertical scaling assumptions are violated the resulting estimated developmental score scales contain standard error and bias. For this study, the magnitude of standard error was similar across all simulated tests regardless of the assumption violation. However, bias fluctuated as a result of different types and magnitudes of UIRT vertical scaling assumption violations. More local item dependence resulted in more grade-to-grade-growth and separation-of-grade-distributions bias. And local item dependence resulted in developmental score scales that displayed scale expansion. Multidimensionality resulted in more grade-to-grade-growth and separation-of-grade-distributions bias when the correlation between dimensions was smaller. Multidimensionality resulted in developmental score scales that displayed scale expansion. Dissimilar reliability of grade level tests resulted in more grade-to-grade-growth bias and minimal separation-of-grade-distributions bias. Dissimilar reliability of grade level tests resulted in scale expansion or scale shrinkage depending on the item characteristics of the test. Limitations of this study and future research are discussed.

Work zone tends to cause hazardous conditions for drivers and construction workers since work zones generate conflicts between construction activities and the traffic, therefore aggravate the existing traffic conditions and result in severe traffic safety and operational problems. To address the influence of various factors on the crash severity is beneficial to understand the characteristics of work zone crashes. The understanding can be used to select proper countermeasures for reducing the crash severity at work zones and improving work zone safety. In this dissertation, crash severity models were developed to explore the factor impacts on crash severity for two work zone crash datasets (overall crashes and rear-end crashes). Partial proportional odds logistic regression, which has less restriction to the parallel regression assumption and provides more reasonable interpretations of the coefficients, was used to estimate the models. The factor impacts were summarized to indicate which factors are more likely to increase work zone crash severity or which factors tends to reduce the severity. Because the speed variety is an important factor causing accidents at work zone area, the work zone speed profile was analyzed and modeled to predict the distribution of speed along the distance to the starting point of lane closures. A new learning machine algorithm, support vector regression (SVR), was utilized to develop the speed profile model for freeway work zone sections under various scenarios since its excellent generalization ability. A simulation-based experiment was designed for producing the speed data (output data) and scenario data (input data). Based on these data, the speed profile model was trained and validated. The speed profile model can be used as a reference for designing appropriate traffic control countermeasures to improve the work zone safety.

Perceptual studies suggest that the visual system uses the "rigidity" assumption to recover three dimensional structures from motion. Ullman (1984) recently proposed a computational scheme, the incremental rigidity scheme, which uses the rigidity assumptions to recover the structure of rigid and non-rigid objects in motion. The scheme assumes the input to be discrete positions of elements in motion, under orthographic projection. We present formulations of Ullmans' method that use velocity information and perspective projection in the recovery of structure. Theoretical and computer analyses show that the velocity based formulations provide a rough estimate of structure quickly, but are not robust over an extended time period. The stable long term recovery of structure requires disparate views of moving objects. Our analysis raises interesting questions regarding the recovery of structure from motion in the human visual system.