No description available.

In the present work we study methodology of different kinds of sample surveys and their design in SAS software. Creating of SAS Enterprise Guide Add-In was the fundamental creative part of this work. This Add-In enables to compute important statistics of sample surveys, without need of being familiar with SAS code. Add-In was created in MSFT Visual Studio 2003 in C # language using a tamplate for Add-Ins provided by SAS. This work contains a general description of the creation of an Add-In as well as the description of the created Add-In for handling the sample surveys and its usage.

It is a standard practice in small area estimation (SAE) to use a model-based approach to borrow information from neighboring areas or from areas with similar characteristics. However, survey data tend to have gaps, ties and outliers, and parametric models may be problematic because statistical inference is sensitive to parametric assumptions. We propose nonparametric hierarchical Bayesian models for multi-stage finite population sampling to robustify the inference and allow for heterogeneity, outliers, skewness, etc. Bayesian predictive inference for SAE is studied by embedding a parametric model in a nonparametric model. The Dirichlet process (DP) has attractive properties such as clustering that permits borrowing information. We exemplify by considering in detail two-stage and three-stage hierarchical Bayesian models with DPs at various stages. The computational difficulties of the predictive inference when the population size is much larger than the sample size can be overcome by the stick-breaking algorithm and approximate methods. Moreover, the model comparison is conducted by computing log pseudo marginal likelihood and Bayes factors. We illustrate the methodology using body mass index (BMI) data from the National Health and Nutrition Examination Survey and simulated data. We conclude that a nonparametric model should be used unless there is a strong belief in the specific parametric form of a model.

This thesis develops techniques for adjusting for selection bias using Gaussian process models. Selection bias is a key issue both in sample surveys and in observational studies for causal inference. Despite recently emerged techniques for dealing with selection bias in high-dimensional or complex situations, use of Gaussian process models and Bayesian hierarchical models in general has not been explored. Three approaches are developed for using Gaussian process models to estimate the population mean of a response variable with binary selection mechanism. The first approach models only the response with the selection probability being ignored. The second approach incorporates the selection probability when modeling the response using dependent Gaussian process priors. The third approach uses the selection probability as an additional covariate when modeling the response. The third approach requires knowledge of the selection probability, while the second approach can be used even when the selection probability is not available. In addition to these Gaussian process approaches, a new version of the Horvitz-Thompson estimator is also developed, which follows the conditionality principle and relates to importance sampling for Monte Carlo simulations. Simulation studies and the analysis of an example due to Kang and Schafer show that the Gaussian process approaches that consider the selection probability are able to not only correct selection bias effectively, but also control the sampling errors well, and therefore can often provide more efficient estimates than the methods tested that are not based on Gaussian process models, in both simple and complex situations. Even the Gaussian process approach that ignores the selection probability often, though not always, performs well when some selection bias is present. These results demonstrate the strength of Gaussian process models in dealing with selection bias, especially in high-dimensional or complex situations. These results also demonstrate that Gaussian process models can be implemented rather effectively so that the benefits of using Gaussian process models can be realized in practice, contrary to the common belief that highly flexible models are too complex to use practically for dealing with selection bias.

Η δειγματοληπτική έρευνα είναι η έρευνα η οποία βασίζεται στο δείγμα για να εξάγει συμπεράσματα για τον πληθυσμό. Στη δειγματοληπτική έρευνα υπάρχουν σφάλματα. Τα σφάλματα είναι οι αποκλίσεις των αποτελεσμάτων της δειγματοληπτικής έρευνας από τις αντίστοιχες πραγματικές τους τιμές. Στην έρευνα αυτή προσπαθούμε να τα ελαχιστοποιήσουμε. Προσπαθούμε να παρέμβουμε επί της διαδικασίας δειγματοληπτικής έρευνας στην οποία υπάρχουν με τέτοιον τρόπο έτσι ώστε τα εξαγόμενα αποτελέσματά της να είναι όσο το δυνατόν πιο κοντά στα αντίστοιχα πραγματικά. / In sample survey there are errors. The errors are the declinations between the estimated values and the real ones. In this thesis we try to find ways in order to minimize them.

Religion in the Czech Republic: Basic analysis and the relationship to selected socio-demographic topics Abstract The aim of this thesis is to analyse the inhabitants of the Czech Republic according to religious belief using data from surveys and also individual 2011 census data. Contemporary position of religion in society is described in the first part, focusing on characteristics of believers and non-believers. Results of surveys concerning religious belief in our region are stated in detail in the next part, oriented mainly on the period after 1989. The part with the analysis of differences in attitudes to problematic demographic behaviour according to religious belief follows, using the data from European Values Study and International Social Survey Programme. Individual data from 2011 census are analysed more in detail in the last part, focusing on newly spread religious groups and furthermore there is an analysis of believers using logistic regression. Keywords: religion, sample survey, Czech Republic, attitudes, atheism

Magister Psychologiae - MPsych / In the context of recent attacks that occurred against foreign nationals (migrants) in South Africa, this study sought to explore attitudes toward migrants and immigration among a sample of students at the University of the Western Cape. Previous studies in South Africa and elsewhere have suggested that in most countries, nationals tend to hold negative attitudes toward migrants and express protectionist attitudes toward migration policies. Research around students' attitudes toward migrants and immigration has shown similar trends. The present study employed a sample survey design to investigate: a) Students' attitudes toward African migrants, b) Students' attitudes toward migration into South Africa and c) Degree of contact that students have with African Migrants. A convenience sample of 183 undergraduate psychology students was used. Students‟ age ranged between 18 and 38 years of age (x = 20 years). Data was collected using a questionnaire comprising of 27 questions related to attitudes towards migrants and immigration as well as a section on contact with migrants. The results show that students showed exclusionary attitudes in terms of immigration, limited contact with migrants and negative attitudes toward African migrants. However, attitudes toward migrant‟s rights were positive. The implications of these findings are discussed.

As the globalisation process accelerates there is a growing need for individual countries to understand the bases for effective performance in international trade. Because it makes up such a large share of world trade, it is especially important to understand what determines effectiveness in exporting. Despite much empirical research, especially over recent decades, the state of knowledge on this topic remains fragmented, unclear and unsatisfactory. The motivation for the present study was therefore twofold: dissatisfaction with the present state of knowledge in this vital area and the importance to the UK economy of improving its export performance in a world of increasing competition. Its aim was to contribute to the resolution of both. In addition to finding what appeared to be quite serious methodological problems in a group of earlier studies, our review of the literature indicated that the best prospects for identifying the determinants of effective exporting were to be found, not at national or sectoral level but at that of the individual firm. Accordingly, an empirical survey research project was developed. To minimise unquantifiable inter-sectoral variability, it was focused on a single sector of industry. For a range of reasons, including the limited amount of information available about its current export activity and prospects, the UK knitted apparel industry was chosen. Special care having been taken to assemble the fullest possible sampling frame and to develop a suitable instrument (which included an export performance model), a mail survey in the form of a stratified random sample of exporting UK manufacturers of knitted apparel was carried through from late 2000. Persistent follow-up by mail and telephone generated a response rate of 70 per cent, comprising close to half of the sampling frame, that was representative of all company size bands, levels of exporting and products. The overall quality of the responses was good; tests of non-response did not find any indications of non-response bias. Data analysis, designed to test thoroughly our 10 export-determinants hypotheses, relied primarily on Pearsonian correlation at the bivariate level then sequentially on Multiple Regression Analysis, Canonical Correlation Analysis and Partial Least Squares. A perhaps slightly novel aspect of the research was that it was not solely cross-sectional in format; a longitudinal element was provided by drawing on the researcher's earlier surveys ; and a panel element by following-up, in 2007, the main 2000 field survey. Where possible, these data were drawn upon in the analysis and interpretation. There did not appear to be any conflict between the three multivariate techniques employed and indeed their findings were not dissimilar. The outcome of the data analysis was to uphold, to varying degrees, most of our hypotheses about the determinants of effective or successful exporting. Those that did not find support were three: firm size, product adaptation, and price determination method. Most strongly supported as determinants were promotional intensity, serving many markets and visits to trade fairs/exhibitions; others which were statistically significant, included management commitment, special staff skills and the use of Commission Agents. While the conclusions must remain a bit tentative they are encouraging.

Cette thèse porte sur la consommation énergétique du secteur tertiaire marchand en France. Notre approche est empirique, et s’appuie sur l’Enquête sur les Consommations d’Énergie dans le Tertiaire (ECET). À partir d’une revue de littérature consacrée à l’étude économétrique des déterminants de la consommation énergétique des bâtiments (résidentiels ou tertiaires), nous mettons en évidence la coexistence de deux méthodes d’estimation, sur lesquelles nous revenons. Nous exposons également les consensus ou les débats portant sur les effets des déterminants étudiés. Dans la mesure où les données mobilisées dans cette thèse sont issues d’une enquête à plan de sondage complexe, nous présentons les outils statistiques adaptés à l’exploitation de données issues de ce type d’échantillonnage, puis nous analysons la controverse qui continue d’exister sur l’estimation de modèles économétriques sur données d’enquête. Nous mobilisons alors notre base de données pour fournir une première description statistique des consommations énergétiques du secteur tertiaire marchand en France. Cette description statistique s’appuie sur nomenclature que nous construisons afin d’obtenir des informations à l’échelle infra-sectorielle. Enfin, nous utilisons l’ensemble des méthodes et approches préalablement identifiées pour étudier les déterminants de la demande énergétique des établissements tertiaires sur la base d’une analyse économétrique des données de l’ECET. Cela nous conduit à effectuer une double lecture de nos résultats, à la fois comme éléments de réponse à la question de l’impact des variables étudiées sur la demande d’énergie des établissements, et comme éléments de comparaison des différentes méthodes. / This thesis deals with energy consumption in the French tertiary buildings. We adopt an empirical approach based on a French national survey, the Tertiary Buildings Energy Consumption Survey (l’Enquête sur les Consommations d’Énergie dans le Tertiaire (ECET)). From a literature review, we present the econometric analyses of the drivers of energy consumption in buildings (residential and tertiary). This review highlights the coexistence of two estimation methods. We discuss these methods, and then we detail consensus and debates on the effects of every driver that has already been analyzed in the literature. Because the data we use are complex sample survey data, we describe the statistical tools that must be used to analyze this type of data, and next present the still controversial issue of econometric modeling based on survey data. Then we use our database to produce a first statistical description of energy consumption in tertiary buildings. This description is based on a nomenclature that we establish to obtain information at the subsector level. Finally, we use all the methods and approaches identified previously to study the drivers of the tertiary buildings’ energy demand by implementing an econometric analysis on the ECET data. This lead to a double analysis of our results, both as elements of knowledge on the impact of each driver on energy consumption and as materials that help to compare the different methods to use.

As the globalisation process accelerates there is a growing need for individual countries to understand the bases for effective performance in international trade. Because it makes up such a large share of world trade, it is especially important to understand what determines effectiveness in exporting. Despite much empirical research, especially over recent decades, the state of knowledge on this topic remains fragmented, unclear and unsatisfactory. The motivation for the present study was therefore twofold: dissatisfaction with the present state of knowledge in this vital area and the importance to the UK economy of improving its export performance in a world of increasing competition. Its aim was to contribute to the resolution of both. In addition to finding what appeared to be quite serious methodological problems in a group of earlier studies, our review of the literature indicated that the best prospects for identifying the determinants of effective exporting were to be found, not at national or sectoral level but at that of the individual firm. Accordingly, an empirical survey research project was developed. To minimise unquantifiable inter-sectoral variability, it was focused on a single sector of industry. For a range of reasons, including the limited amount of information available about its current export activity and prospects, the UK knitted apparel industry was chosen.. Special care having been taken to assemble the fullest possible sampling frame and to develop a suitable instrument (which included an export performance model), a mail survey in the form of a stratified random sample of exporting UK manufacturers of knitted apparel was carried through from late 2000. Persistent follow-up by mail and telephone generated a response rate of 70 per cent, comprising close to half of the sampling frame, that was representative of all company size bands, levels of exporting and products. The overall quality of the responses was good; tests of non-response did not find any indications of non-response bias. Data analysis, designed to test thoroughly our 10 export-determinants hypotheses, relied primarily on Pearsonian correlation at the bivariate level then sequentially on Multiple Regression Analysis, Canonical Correlation Analysis and Partial Least Squares. A perhaps slightly novel aspect of the research was that it was not solely cross-sectional in format; a longitudinal element was provided by drawing on the researcher¿s earlier surveys ; and a panel element by following-up, in 2007, the main 2000 field survey. Where possible, these data were drawn upon in the analysis and interpretation. There did not appear to be any conflict between the three multivariate techniques employed and indeed their findings were not dissimilar. The outcome of the data analysis was to uphold, to varying degrees, most of our hypotheses about the determinants of effective/ or successful exporting. Those that did not find support were three: firm size, product adaptation, and price determination method. Most strongly supported as determinants were promotional intensity, serving many markets and visits to trade fairs/ exhibitions; others which were statistically significant, included management commitment, special staff skills and the use of Commission Agents. While the conclusions must remain a bit tentative they are encouraging.