The thesis analyses the differences that result from using of specific data collection method, computer assisted telephone interviewing (CATI) and computer assisted personal interviewing (CAPI). The main findings are based on election model built by research agency TNS Aisa for elections to the Chamber of Deputies of the Parliament of the Czech Republic in 2013. The aim of the thesis is to determine whether the chosen method of data collection influences the results of the estimated electoral preferences, to determine in which sociodemographic categories it is happening and whether the differences in the obtained data are related to the ideological orientation of the political parties. The dependence of the results on the data collection method is assessed by the chi-quadrate independence test. Further, through personal interviews with researchers, it is determined at what stage of the research process the data may be distorted and what its possible causes are. The benefit of the thesis is the finding that the chosen method of collection influences the established preferences among voters aged 30-44, university graduates and voters living in Prague. In these groups, the left-hand side ČSSD is preferred more by personal interviewing and right-handed TOP 09 through telephone interviewing. The collection of data itself was evaluated by the most risky phase of the research process, in which possible distortion could occur. While the accuracy of personal interviewing depends largely on the interviewer's personality, the sources of distortion of the telephone inquiry result more from the nature of the use of the phone itself.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

This thesis is focused on problem data reconciliation of measurements. The objective of this thesis was reconciled measured value from electric drum dryer to suit exactly to the mathematical model of drying. For solution was used nonlinear data reconciliation with constrained nonlinear optimization. The entire calculation is processed in programme MATLAB and outputs are graphs of reconciled values of measurement on dryer such as inlet and outlet temperature and humidity, differential pressure of exhaust moisture air, weight of laundry, atmospheric pressure and electric supply. Achieved solution can by characterized by an amount of evaporated water. Weight of wet and dry laundry are 27,7 kg a 17,7 kg. The calculated amount of evaporated water from measurements was almost 18,8 kg. With reconciled measurements it was 9,7 kg. Goals of the thesis were found more realistic values.

No description available.

Energy usage evaluation and condition monitoring for electric machines are important in industry for overall energy savings. Traditionally these functions are realized only for large motors in wired systems formed by communication cables and various types of sensors. The unique characteristics of the wireless sensor networks (WSN) make them the ideal wireless structure for low-cost energy management in industrial plants. This work focuses on developing nonintrusive motor-efficiency-estimation methods, which are essential in the wireless motor-energy-management systems in a WSN architecture that is capable of improving overall energy savings in U.S. industry. This work starts with an investigation of existing motor-efficiency-evaluation methods. Based on the findings, a general approach of developing nonintrusive efficiency-estimation methods is proposed, incorporating sensorless rotor-speed detection, stator-resistance estimation, and loss estimation techniques. Following this approach, two new methods are proposed for estimating the efficiencies of in-service induction motors, using air-gap torque estimation and a modified induction motor equivalent circuit, respectively. The experimental results show that both methods achieve accurate efficiency estimates within ¡À2-3% errors under normal load conditions, using only a few cycles of input voltages and currents. The analytical results obtained from error analysis agree well with the experimental results. Using the proposed efficiency-estimation methods, a closed-loop motor-energy-management scheme for industrial plants with a WSN architecture is proposed. Besides the energy-usage-evaluation algorithms, this scheme also incorporates various sensorless current-based motor-condition-monitoring algorithms. A uniform data interface is defined to seamlessly integrate these energy-evaluation and condition-monitoring algorithms. Prototype wireless sensor devices are designed and implemented to satisfy the specific needs of motor energy management. A WSN test bed is implemented. The applicability of the proposed scheme is validated from the experimental results using multiple motors with different physical configurations under various load conditions. To demonstrate the validity of the measured and estimated motor efficiencies in the experiments presented in this work, an in-depth error analysis on motor efficiency measurement and estimation is conducted, using maximum error estimation, worst-case error estimation, and realistic error estimation techniques. The conclusions, contributions, and recommendations are summarized at the end.

In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.