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Nowadays, the most pressing task is study of reasons to low motivation of the bailiffs in their performance as well as the development of measures to improve the personnel policy ofthe Public Servants of the Federal Bailiff Service. In pursuance of this objective, the sociological research has been held by authors in structural units of Federal Bailiff Service of the Stavropol region in January-February 2016 on the basis of North-Caucasus Federal University. Sociological researches are led in the questionnaire form and in-depth interviews. The survey objectives were the following: assessment of bailiffs working environment; determination of the relationship between the remuneration of bailiffs and the results of their work; revealing interrelation between the bailiff class rate and remuneration; impact assessment of operating results and bailiffs 'skills upgrading on career growth. Results management of the quantitative researches was performed using the counting program VORTEX 8.0. These methods were aimed to study motivation of bailiffs in their activities, and the subsequent development of measures to improve the personnel policy of the Public Servants of the Federal Bailiff Service. These data revealed the necessity to develop the material incentive system of the Public Servants of the Federal Bailiff Service, and also identified several areas for improving legal and regulatoryframework in this area.

Mapping and monitoring forest with time-series remote sensing methodologies requires reference data constructed in a sampling framework. Few worldwide reference data sets with time-series information are currently available for large areas. We produced a global forest reference set at 30 m with time-series information from 2000 to 2020 based on a stratified random sampling scheme. All available Landsat, high-resolution Google Earth images and other relative land cover/land change products were used in a visual interpretation approach. This reference dataset contains 10339 sample units (6252 persisting forest sample units, 2049 change sample units and 2038 persisting non-forest sample units) attributed with annual forest/non-forest information from 2000 to 2020. The results of our analysis highlight many cases of undetected forest change due to definition and other factors, and suggest that change of forest canopy cover could be monitored instead of land cover types. The reference set can be potentially used for many large-area applications such as validation of forest mapping projects or as auxiliary data for forest monitoring analysis. And this reference set will be available online (). [ABSTRACT FROM AUTHOR]

Resumo: O objetivo deste trabalho é discutir questões relacionadas à amostragem na sociolinguística variacionista, considerando a dimensão probabilística e não probabilística. Conceitos de estatística, como população e amostra, descrição e inferência, são revisados, e os procedimentos de amostragem aleatória e não aleatória são discutidos considerando o viés de seleção e as especificidades da coleta de dados sociolinguísticos para pesquisa de orientação variacionista. Palavras-chave: sociolinguística; amostragem por cotas; estatística. Abstract: This paper goal is to discuss sampling in the variationist sociolinguistic approach, both in its random and non-random dimensions. Statistic concepts, such as population and sample, descriptive and inferential statistics are presented; additionally random and non-random sampling procedures are discussed, taking into account the selection bias and the specificity of data collection within variationist sociolinguistics. Keywords: sociolinguistics; stratified sample; statistics.

This paper develops statistical inference for population mean and total using stratified judgment post-stratified (SJPS) samples. The SJPS design selects a judgment post-stratified sample from each stratum. Hence, in addition to stratum structure, it induces additional ranking structure within stratum samples. SJPS is constructed from a finite population using either a with or without replacement sampling design. Inference is constructed under both randomization theory and a super population model. In both approaches, the paper shows that the estimators of population mean and total are unbiased. The paper also constructs unbiased estimators for the variance (mean square prediction error) of the sample mean (predictor of population mean), and develops confidence and prediction intervals for the population mean. The empirical evidence shows that the proposed estimators perform better than their competitors in the literature. [ABSTRACT FROM AUTHOR]

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results. [ABSTRACT FROM AUTHOR]

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This article develops estimators for certain population characteristics using a judgment post stratified (JPS) sample. The paper first constructs a conditional JPS sample with a reduced set size K by conditioning on the ranks of the measured observations of the original JPS sample of set size $$H \ge K$$ . The paper shows that the estimators of the population mean, median and distribution function based on this conditional JPS sample are consistent and have limiting normal distributions. It is shown that the proposed estimators, unlike the ratio and regression estimators, where they require a strong linearity assumption, only need a monotonic relationship between the response and auxiliary variable. For moderate sample sizes, the paper provides a bootstrap distribution to draw statistical inference. A small-scale simulation study shows that the proposed estimators based on a reduced set JPS sample perform better than the corresponding estimators based on a regular JPS sample. [ABSTRACT FROM AUTHOR]

Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models. For multistage stratified clustered designs, this requires dummy variables to represent strata as well as primary sampling units (PSUs) nested within each stratum in the imputation model. Such a modeling strategy is not only operationally burdensome but also inferentially inefficient when there are many strata in the sample design. Complexity only increases when sampling weights need to be modeled. This article develops a generalpurpose analytic strategy for population inference from complex sample designs with item-level missingness. In a simulation study, the proposed procedures demonstrate efficient estimation and good coverage properties. We also consider an application to accommodate missing body mass index (BMI) data in the analysis of BMI percentiles using National Health and Nutrition Examination Survey (NHANES) III data. We argue that the proposed methods offer an easy-to-implement solution to problems that are not well-handled by current MI techniques. Note that, while the proposed method borrows from the MI framework to develop its inferential methods, it is not designed as an alternative strategy to release multiply imputed datasets for complex sample design data, but rather as an analytic strategy in and of itself. [ABSTRACT FROM AUTHOR]

A major development of the new Millennium in the sphere of business is the emergence of the entrepreneurial 'Spirit 'among the new generation youth. With the shrinking employment opportunities in the government sector and the effect of global business development, it appears as if the entrepreneurial 'bug' has infected the Indian Youth as well. Catering to these needs, venture capitalists and angel capitalists are entering this business space in large numbers. Keeping this growing interest in mind, many universities have introduced entrepreneurship education to promote the interest of students to become future entrepreneurs. This is more line with regards to first generation entrepreneurs. The purpose of this study is to investigate the entrepreneurial interest among University students of Amrita, specifically, the Mysuru Campus. The influence of demographic characteristics and family business background on student's interest on entrepreneurship is also examined. A stratified sample of 100 targeted respondents of final year students from Amrita Mysuru Campus will be taken for this purpose. The students from both commercial and non commercial background will be taken for the study. The study also focuses on whether suitable training programs can increase the likelihood of Amrita University students becoming entrepreneurs. Finally based on the findings suitable recommendations will be made. [ABSTRACT FROM AUTHOR]

This study uses an employee-centric approach to investigate working conditions in nonprofit social service organizations in Memphis, Tennessee. Many entities exert control over the nonprofit labor process, muddling decision making and contributing to the overall opacity of governance. Oftentimes, nonprofits' structures appoint executive directors as the gatekeeper between employees and boards of directors, complicating paths to expressing grievances. Nonprofits' reliance on external funding to operate also imbues funders with enormous influence over daily operations. In turn, many granting agencies impose fiscal accountability systems that force nonprofits to quantify the qualitative work that they do. This relationship pits categories of employees against one another: high-ranking 'administrative' staff must track and report program information to funders; 'programming' staff are responsible for implementing these systems. This dynamic widens the communication gap between the former, who are removed from direct contact with the clients and are low ranking, and the latter, who are responsible for and possess the greatest knowledge of front-line service delivery. Furthermore, programming employees and clients often forge deep caring relationships, which collapse their options for self-advocacy. Yet individual employees, especially at lower levels, strive daily to provide high-quality care and bring about social change within the confines of a hierarchical and increasingly corporatized structure. In addition to these structural conditions, the nonprofit labor process co-opts broader ideologies about gender and race that reinforce this hierarchy of power, pay, and prestige. By privileging the perspectives of employees at all levels, this study shows that program outcomes are linked to donor satisfaction rather than to client satisfaction, suggesting that nonprofits have many 'uses' besides their stated missions and that striving to meet the 'needs' of outside groups often comes at the cost of service delivery to clients and employee satisfaction. [ABSTRACT FROM AUTHOR]

Summary Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16). [ABSTRACT FROM AUTHOR]

The quantitative research on the stratified Romanian car market led to the conclusion that the famous law or Pareto optimal 20/80 can be recast in this area efficiently and with high sufficient coverage. The whole paper is nothing else but a quantitative market research and therefore firstly we select the main aspects to describe the specificity of Romanian car market as a specific and regional one. Introduction anticipates the importance of hypothesis in describing buyer's opinions and attitudes. All the other sections, from the first to the last, are just a natural, detailed and selected story of a marketing research made methodologically correct to understand the traditional buyer. [ABSTRACT FROM AUTHOR]

Sample surveys are routinely used to gather primary data in human geography research. We highlight the difference between design-based analysis and model-based analysis of sample surveys and emphasize the advantages of using the design-based approach with these data. As an example, we demonstrate differences in results from model-based and design-based analyses of cancer prevalence in a population of predominantly minority women in North Carolina and South Carolina. The results from the two approaches reveal differences in population estimates of numerous variables and a different conclusion regarding the significance of an explanatory variable in a logistic regression model to explain colon cancer prevalence. [ABSTRACT FROM AUTHOR]

The article firstly presents concisely the main characteristics of the design and the process of construction of the stratified sample of the «Enquesta de Condicions de Vida i Hàbits de la Població de Catalunya», particularly for the year 2006. Then, the article is centered, and has as specific objective, the presentation of some results that —coming directly or indirectly from the construction of the sample— have a great interest by their sociological implication. The type of sample that is used provides results of high interest for the understanding of the territory, both in the methodological and sociological aspects. This is achieved by means of the procedure followed in the design, the used criteria, the taken decisions and their subsequent use. These results are, namely, the identification of the stratum —that allows a specific analysis of the social structure—, the production of a social cartography that derives from these stratum when considering the census section, the identification of «social zones» using the additional information that it is obtained by the application of the data of the surveys to the stratum, and the reusability for the construction of other samples or other social analyses, or to validate our own results of the survey and other technical aspects. [ABSTRACT FROM AUTHOR]

Analysis of the relationship between distance and willingness to pay (WTP) is important for estimation and transfer of environmental benefits. Several contingent valuation (CV) studies have investigated this topic, but results are mixed. This paper describes a choice modelling (CM) application that estimates distance effects on parameters of three environmental attributes. Combinations of these attributes create different management policies for native vegetation. The CM study is based on a sampling procedure that provides a geographically balanced sample and statistical tests to choose the best specification of the distance–WTP relationship. Welfare analysis shows that disregarding distance causes under-estimation of individual and aggregated benefits and losses, seriously misdirecting resource allocation. [ABSTRACT FROM AUTHOR]

Abstract: A comparison is made between the variance of the estimator of the total of a variable obtained from both a simple and a stratified random sampling, in which the sample sizes of some strata are equal to the stratum population size. It is shown that in this case, the advantage of the stratified sample could depend on the sample size. The paper presents inequalities that determine, as a function of the sample size, when the variance of the estimator obtained with simple sampling is lower than the variance obtained with the stratified sampling. The results give insight in order to prevent overstratification. [Copyright &y& Elsevier]

Abstract: In order to determine the minimum sample size required to accurately estimate growth and mortality, and to assess the effects of using either random or stratified sampling procedures, a large sample of Argyrozona argyrozona (n =618) was collected during February and March 1998 from the central Agulhas bank. To investigate the influence of monthly sampling and integer ageing on growth estimation, monthly samples were collected for a period of 1 year and a single large sample was collected during February and March 1998 in the Tsitsikamma National Park. Decimal and integer ageing of monthly samples produced similar estimates of growth (p >0.05), fishing mortality (F) and spawner biomass-per-recruit (SB/R). Estimates of growth (p >0.5), F and SB/R derived from random monthly (n =298) and single annual random samples (n =300), and based on integer ageing, were also very similar. Random and stratified draws from the large Agulhas bank sample revealed that a minimum of 300 random samples or at least 10 fishes per 2cm size class (i.e. n =193) were necessary to provide reliable estimates of growth, F and SB/R. To optimise the trade-off between cost and parameter precision, A. argyrozona are best monitored with a stratified sample of 10 fishes per 2cm size class that is taken during the spawning season. [Copyright &y& Elsevier]

Background: The single-item measure on self-assessed health has been widely used, as it presents researchers with a summary of an individual's general state of health. A qualitative study was initiated to find out which particular aspects are included in health self-assessments; which aspects do people consider when answering the question 'How is your health in general?'. Subgroup differences were studied with respect to gender, age, health status and health assessment. Methods: Qualitative study with stratification by background characteristic, health status and health assessment (n = 40). Results: Almost 80% of the participants referred to one or more physical aspects (chronic illness, physical problems, medical treatment, age-related complaints, prognosis, bodily mechanics, and resilience). However, when assessing their health, participants also include aspects that go beyond the physical dimension of health. In total, 80 percent of the participants--whether or not in addition to physical aspects--referred to other health dimensions. Besides physical aspects, participants considered the extent to which they are able to perform (functional dimension -28%), the extent to which they adapted to, or their attitude towards an existing illness (coping dimension -28%), and simply the way they feel (wellbeing dimension -20%). In this study, health behaviour or lifestyle factors (behavioural dimension -3%) proved to be relatively unimportant in health selfassessments. Conclusions Selfassessed health proved to be a multidimensional concept. For most part, subgroup differences in selfassessed health could be attributed to experience with ill health: being relatively inexperienced with health problems versus having a history of health problems. [ABSTRACT FROM AUTHOR]

Recognizing that the efficiency in relative risk estimation for the Cox proportional hazards model is largely constrained by the total number of cases, Prentice (1986) proposed the case-cohort design in which covariates are measured on all cases and on a random sample of the cohort. Subsequent to Prentice, other methods of estimation and sampling have been proposed for these designs. We formalize an approach to variance estimation suggested by Barlow (1994), and derive a robust variance estimator based on the influence function. We consider the applicability of the variance estimator to all the proposed case-cohort estimators, and derive the influence function when known sampling probabilities in the estimators are replaced by observed sampling fractions. We discuss the modifications required when cases are missing covariate information. The missingness may occur by chance, and be completely at random; or may occur as part of the sampling design, and depend upon other observed covariates. We provide an adaptation of S-plus code that allows estimating influence function variances in the presence of such missing covariates. Using examples from our current case-cohort studies on esophageal and gastric cancer, we illustrate how our results our useful in solving design and analytic issues that arise in practice. [ABSTRACT FROM AUTHOR]

This article derives large-sample properties and provides asymptotically distribution-free statistical inference procedures for an alternative approach to inequality orderings--normalized stochastic dominance (NSD). NSD is a straightforward extension of standard dominance techniques. As such, it can be generalized to higher orders of dominance, thereby providing a more powerful technique for ranking distributions. We illustrate the NSD method by applying it to Current Population Survey data for 1970-1990. [ABSTRACT FROM AUTHOR]

Preferential sampling is a form of data collection that may significantly distort the histogram and the semivariogram of spatially correlated data. Typical situations are a higher sampling density at high-valued areas favorable for mining, and highly contaminated areas in need of environmental remediation. Multiple statistical procedures are devoted to obtaining representative statistics, whose magnitudes should be close to the respective population values. This paper proposes a resampling method that can compensate for preferential sampling of spatially correlated data without using declustering weights. The application of the method herein generates a dataset of median estimates of quantiles of multiple stratified resamples that is free of preferential sampling. The methodology is illustrated with two examples. The first one involves values actually measured in the field and has the advantage of representing a real scenario of spatial fluctuations and preferential sampling. A second dataset is synthetic and has the main benefit of a priori knowledge of the underlying spatial distribution, thus allowing a satisfactory evaluation of the results against the known baseline. Access to computer code is offered for practical application of the method. • Cumulative frequencies and semivariograms of preferentially sampled spatial data often are distorted. • Stratified resampling allows elimination of the effects of preferential sampling. • Calculation of medians of quantiles of multiple resamples add robustness to the approach. • The method has low sensitivity to fluctuations in its parameters. • Access to computers code is offered for practical applications. [ABSTRACT FROM AUTHOR]