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Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a complex task as it relies on the availability of second-order inclusion probabilities at each stage. To cope with this issue, several bootstrap algorithms have been proposed in the literature in the context of a two-stage sampling design. In this paper, we describe some of these algorithms and compare them empirically in terms of bias, stability, and coverage probability.

The South African smallholder sector is characterised by relatively low productivity due to persistent deterioration in soil fertility owing to declining organic matter and other essential soil nutrients. Consequently, adoption of sustainable agricultural inputs like organic fertiliser is essential. Although there is sufficient advocacy in the adoption of organic fertiliser, the economic linkage between farmers' socioeconomic factors and willingness to pay (WTP) remains under-explored. This study investigated the determinants of WTP a price premium for organic fertiliser among smallholder potato farmers using primary data collected from 189 smallholder farmers in three municipal areas in KwaZulu-Natal, South Africa, through a multi-stage sampling technique. The data was analysed using the ordered logit model and revealed that marital status, access to extension services, and knowledge of organic fertiliser usage, land ownership, livestock size and distance to the source of organic fertiliser influenced the farmers’ WTP for organic fertiliser. The study found that about 83.6% of the sampled smallholder farmers were willing to pay for organic fertiliser, while about 16.4% of them indicated that they were not willing to pay for organic fertiliser. This result justifies the prospect of commercialisation of organic fertiliser to facilitate the availability of organic fertiliser to those that are willing to pay for it. This study recommends improved access to extension services to enhance technical information dissemination and knowledge of organic fertiliser usage among smallholder farmers. Development of policies that strive to institute security of land tenure among smallholder farmers, which will encourage smallholder farmers WTP is also essential.

Health care audits are crucial in managing the government insurance programs that are estimated to have losses amounting to billions of dollars every year. Statistical methods such as sampling have long been used to handle their size and complexity. Sampling from health care claims data can benefit from multi-stage approaches, especially when the evaluation of the tradeoffs between precision and cost is important. The use of decision models could facilitate health care auditors and policy makers make the best use of these sampling outputs. This paper proposes an integrated multi-stage sampling and decision-making framework that enables auditors address the tradeoffs between audit costs and expected overpayment recovery. We illustrate the framework and discuss insights utilizing a variety of overpayment scenarios for payment populations including U.S. Medicare Part B claims payment data. [ABSTRACT FROM AUTHOR]

Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a complex task as it relies on the availability of second-order inclusion probabilities at each stage. To cope with this issue, several bootstrap algorithms have been proposed in the literature in the context of a two-stage sampling design. In this paper, we describe some of these algorithms and compare them empirically in terms of bias, stability, and coverage probability. [ABSTRACT FROM AUTHOR]

The marketing strategy phenomenon improves significantly, narrowing from a general to a specific cultural ethnicity base and from variable to dimension analysis. This study examines the impact of culture, brand image and price on buying decisions. The study population comprised retail consumers in the sampled area of Situbondo, East Java, Indonesia. A multi-stage sampling technique was used to derive a sample of 112 respondents as a primary data source – descriptive statistics allows for the demographic characteristics of retail consumers in East Java, Indonesia. Surprisingly, the data showed that gender involvement in buyer decision-making was dominant. Most retail customers were identified as private-sector employees and indicated for higher income earners. Responses were then analyzed using multiple linear regressions to answer the research hypotheses. The results showed that Hofstede’s culture dimension and the brand image and price significantly affected consumer buying decisions at retail stores in East Java, Indonesia. Regarding the strength of Islamic culture in East Java, price was the primary consideration in buying decisions. Further research, preferably using ethnographic approaches with an emphasis on qualitative research, is needed to investigate the implications of these relationships. AcknowledgmentWe would like to thank the Research Centre (LP2M) of University of Jember, East Java, Indonesia, for their support and funding. We also want to thank Rusdiyanto, a Ph.D. student from the Faculty of Economics and Business, Universitas Airlangga, Surabaya, Indonesia, for his helpful discussions and contributions.

Background: Understanding preferences and practices for delivery place among women would assist in better resource utilization for skilled attendants. Objectives: To study factors influencing women's preference and practice regarding their place of delivery. Methods: A community based cross-sectional study was conducted in Agra from 1st October 2018 to 31st October 2020. Multi-stage random sampling was used. Data was collected using semi-structured interview schedule. Both bivariate and multivariate analysis was done. Result: Majority (72.67%) of women had preference for delivery at government hospital while 19.67% for private hospital and only 7.67% for home. In actual practice, 58.33% had delivered at government hospital and 32.33% delivered at private hospital while 9.33 % at home. On multiple-logistic regression analysis, parity found to have significant association with preference of delivery at government hospital. Preference for delivery in private hospital was found to be significant with OBC caste and in women whose husband has skilled/highly-skilled occupation. In actual practice, Government hospital as delivery place found to have significant association with parity and presence of ASHA. Private hospital as a delivery place found significant with literate mothers and parity. Conclusion: Majority had preferred and practiced institutional delivery, preferring government hospital over private hospital. In actual practice, delivery at private hospital as well as home delivery out-numbered the preferred proportion. [ABSTRACT FROM AUTHOR]

An understanding of the determinants of farm diversity is important in dealing with food and nutrition security concerns. Access to extension is a key determinant of farm diversity through technology adoption. However, limited empirical evidence exists on the effects of different types of extension services on farm diversity. A truncated Poisson regression was used on data collected from 744 households that were selected using a multi-stage sampling technique. Results show that access to government and private extension services increased farm diversity by 81 and 95 percentage points, respectively. The policy implications are that first, government extension services should be focused on farmers who may not afford private extension services given that the least diversified farms are significantly different from the most diversified farms. Second, policies should incentivize the private sector to invest more resources in the development and dissemination of extension services as a complement to government extension and lastly, there is need for policies that guide privatization of extension services especially in the current devolved system of governance in Kenya.

We discuss a two-step approach to test for a mediated effect using data gathered via complex sampling. The approach incorporates design-based multiple linear regressions and a generalized Sobel's method to test for significance of a mediated effect. We illustrate the applications to a study of nicotine dependence, race/ethnicity and cigarette purchase price among daily smokers in the U.S. The study goal was to assess significance of cigarette purchase price as a mediator in the association between race/ethnicity (non-Hispanic Black/African American, non-Hispanic White) and nicotine dependence measured in terms of the average number of cigarettes smoked per day. The single-mediator model incorporated 18 covariates as control factors. The results indicated a significant mediated effect of cigarette purchase price on the association. However, the relative effect size of 5% indicated low practical significance of the cigarette purchase price as a mediator in the association between race/ethnicity and nicotine dependence. The approach can be modified to studies where data are gathered via other types of complex sampling. [ABSTRACT FROM AUTHOR]

The study was conducted in Bako Tibe and Dano districts of Western Oromia region, Ethiopia with the objectives to generate the relevant information regarding breeding practices, objectives, and farmers' trait preferences of local chickens under scavenging production systems. Data were collected using semi-structured formal questionnaires and focus group discussions. A total of 119 households (60 from Bako Tibe and 59 from Dano district), were randomly selected using a multi-stage sampling technique. The study revealed that egg consumption, chick sale, and meat consumption were the main purposes of keeping indigenous chicken in BakoTibe and Dano districts. The result indicates that the majority of the respondents (87.43%) were practiced selection of hens and cocks for breeding purposes emphasizing the production of a high number of eggs, body size, brooding ability, leg length to select hens. Whereas body size, growth rate, leg length, comb shape in their order of importance to select breeding cocks. The selection of these traits indicates that egg and meat production was the major aims of the farmer to raises chickens in the study districts. Generally, chicken production is an important economic activity to improve the livelihood of smallholder households. Therefore the future research has been focused on the farmers' production objectives and trait preference of chickens to improve the productivity o f indigenous chickens through the selection and crossbreeding of local chickens with exotic breeds in the semi -scavenging chicken production systems in the study area. [ABSTRACT FROM AUTHOR]

An understanding of the determinants of farm diversity is important in dealing with food and nutrition security concerns. Access to extension is a key determinant of farm diversity through technology adoption. However, limited empirical evidence exists on the effects of different types of extension services on farm diversity. A truncated Poisson regression was used on data collected from 744 households that were selected using a multi-stage sampling technique. Results show that access to government and private extension services increased farm diversity by 81 and 95 percentage points, respectively. The policy implications are that first, government extension services should be focused on farmers who may not afford private extension services given that the least diversified farms are significantly different from the most diversified farms. Second, policies should incentivize the private sector to invest more resources in the development and dissemination of extension services as a complement to government extension and lastly, there is need for policies that guide privatization of extension services especially in the current devolved system of governance in Kenya. [ABSTRACT FROM AUTHOR]

Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.

Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs. [ABSTRACT FROM AUTHOR]

Developing countries report high prevalence of smoking habits among youngsters. The transition from school to college is a critical phase where young people are more likely to adopt such unhealthy habits and life-style. It is necessary to understand the factors that lead youngsters to smoking and analyze the relationship of these factors with other socio-economic and demographic variables. The present study is a cross sectional empirical research based on multi-stage sampling process. Data analysis reveals some important relationships among the variables under study which have been suitably utilized in the formulation of de-marketing strategies for vulnerable age groups. These de-marketing strategies are based on the notion that consumption behavior among college students varies significantly which is also evident from factor analysis of data.

As a benchmark structure in the retina, the precise localization of the Optic Disc (OD) has received considerable attention from researchers owning to its importance for ophthalmic image analysis and retinopathy screening. In most literature works, although OD localization has achieved fairly good performance, rather less attention has been paid to supervised techniques. As the ODs are common in characteristics and form a fixed pattern, we propose in this paper an OD localization method based on Convolutional Neural Networks (CNNs). In data preparation, the blue channel in the fundus image, which is always empty or noisy and tends to be uninformative, is replaced by coarsely segmented vasculature maps (Red channel, Green channel and vasculatures are denoted as RGV). In addition, the input of the CNN structures is rectangular instead of square in order to involve more context information of the vasculatures. For training, a two-stage process is designed to alleviate the class-imbalance problem by choosing large number of positive samples at the first stage and choosing samples at the center of OD to fine-tune the fist-stage model at the second stage. Then a probability guided search algorithm is developed to improve the efficiency of OD localization by selectively searching patches with large chances to be an OD and their neighbors. Finally, the precise OD position is located as center of the highest probability regions after weighted voting. Extensive experiments on four publicly available databases demonstrate the effectiveness and efficiency of the proposed method, with an average detection rate of 99.11% and detection time less than 15 s per image. [ABSTRACT FROM AUTHOR]

The present study focuses on standardization of sampling technique and comparison of different types of sample allocation methods in combination with various stratification tools (optimum strata boundaries, number of strata and optimum sample size etc.) for obtaining efficient estimators of area and production of apple in Himachal Pradesh. For this purpose, various aspects involved in optimum stratification with reference to data collected from the selected orchardists in district Shimla, during the year 2011-12 have been analyzed. The variable "Area under Apple" was chosen as the stratification variable as it had high correlation (r=0.96) with the estimation variable "Production of Apple". Four methods of construction of strata viz.,equalization of strata total, equali zation of cumulative of √f(y) , equalization of cumulative of ½{r(y)+f(y)} and equalization of cumulative of 2√f(y) were used and their relative efficiencies for estimating total production of apple in the study district of the state have been analyzed. The critical examination of the result revealed that with the increase in number of strata from 2 to 4 and sample size from 10 to 40, equalization of cumulative of 2√f(y) method along with Neyman allocation resulted in least variance (0.89) and maximum percentage gain in efficiency (20418.16). Thus, equalization of cumulative of method 2√f(y) with L>2 can profitably be applied for the estimation of apple production in the study district of the state Himachal Pradesh, India. [ABSTRACT FROM AUTHOR]

This paper evaluates impact of Community Driven Development programme on infrastructure under National Fadama II Project in Oyo State Nigeria. Data were collected from two hundred and sixty-four farmers using multistage sampling procedures. Data were analyzed using descriptive statistics and infrastructure index. The result shows that average infrastructural index in the area was 0.42. Forty-four villages were classified as infrastructural developed villages (IDV) while the remaining were infrastructural under-developed villages (IUV). The study therefore calls for the involvement of both private and public organization in construction and rehabilitation of rural infrastructure, processing services centers and researches on labour saving devices for agriculture in the study area. [ABSTRACT FROM AUTHOR]

Rank tests are widely used for exploratory and formal inference in the health and social sciences. With the widespread use of data from complex survey samples in medical and social research, there is increasing demand for versions of rank tests that account for the sampling design. We propose a general approach to constructing design-based rank tests when comparing groups within a complex sample and when using a national survey as a reference distribution, and illustrate both scenarios with examples. We show that the tests have asymptotically correct levels and that the relative power of different rank tests is not greatly affected by complex sampling. [ABSTRACT FROM AUTHOR]

This research was conducted to explore the factors affecting the technical efficiency (TE) of rice producers and its determinants at the farm level. We used a multi-stage sampling procedure to collect cross-sectional data from 800 rice growers in the Uttar Pradesh state of India, and a stochastic frontier model (SFA) was applied. The results showed that the mean technical efficiency was 72%, suggesting scope for a substantial increment in rice productivity exists while using the current level of inputs and technologies. Furthermore, the MLE results revealed that labor, irrigation, and hybrid seeds had a constructive impact on technical efficiency, while experience and tenure status showed a negative impact on technical efficiency. As unraveled by the results of the study, it can be concluded that the technical efficiency of rice farmers can be improved through timely access to credit and agricultural information delivered to them via extension services. The study, therefore, recommends that the government provide subsidized agrochemicals and focus on developing a robust network of extension services throughout the local districts for proper dissemination of inputs. About 12% of India's rice is produced in the Uttar Pradesh state. So, this study could be an essential tool for the agriculture sector, which could help to solve rice productivity problems for future generations. [ABSTRACT FROM AUTHOR]

This paper describes the method that was implemented by the Statistical Office of France (INSEE) to calculate cross-sectional variance estimates for the French “Labour Force Survey” (LFS). A home-made SAS application, named POULPE, was used for the calculations. After outlining the LFS sample design in the first part, the paper presents the software POULPE, particularly its technical capabilities and the theoretical principles underlying the variance estimation methods it implements. The third part develops how POULPE was utilized to compute variance estimates for the LFS, focusing on the statistical problems which were met and how they were solved. Finally, we provide estimated standard errors for totals and ratios and we make comments about those values.

Background: The National Children's Study (NCS) was established as a national probability sample of births to prospectively study children's health starting from in utero to age 21. The primary sampling unit was 105 study locations (typically a county). The secondary sampling unit was the geographic unit (segment), but this was subsequently perceived to be an inefficient strategy. Methods and Results: This paper proposes that second-stage sampling using prenatal care providers is an efficient and cost-effective method for deriving a national probability sample of births in the US. It offers a rationale for provider-based sampling and discusses a number of strategies for assembling a sampling frame of providers. Also presented are special challenges to provider-based sampling pregnancies, including optimising key sample parameters, retaining geographic diversity, determining the types of providers to include in the sample frame, recruiting women who do not receive prenatal care, and using community engagement to enrol women. There will also be substantial operational challenges to sampling provider groups. Conclusion: We argue that probability sampling is mandatory to capture the full variation in exposure and outcomes expected in a national cohort study, to provide valid and generalisable risk estimates, and to accurately estimate policy (such as screening) benefits from associations reported in the NCS. [ABSTRACT FROM AUTHOR]

Wetlands in the Upper Mississippi River and Great Lakes Region (UMRGLR) must annually sustain populations of migrating waterfowl from the mid-continent of North America. We used multi-stage sampling to estimate plant and invertebrate food biomasses (kg/ha) for ducks in 3 wetland habitat types at 6 stop-over locations in the UMRGLR during 2006 and 2007. Total biomass was greatest in palustrine emergent (PEM; ${\bar {x}}$ = 208 kg/ha, SE = 23, median = 120), followed by palustrine forested (PF; ${\bar {x}}$ = 87 kg/ha, SE = 7; median = 43), and lacustrine-riverine (LR; ${\bar {x}}$ = 52 kg/ha, SE = 7; median = 27) wetlands. Ducks that foraged in forested and LR wetlands encountered the least food abundance during spring in the UMRGLR. Our estimates of food abundance were the lowest reported among other landscape scale surveys from mid-continent North America. About 1 in every 5 PEM wetlands and over half of our PF and LR wetlands that we sampled contained <50 kg/ha of food, suggesting many had little or no forage value to ducks during spring. Biomass of plant foods generally exceeded invertebrate biomass in all habitat types, although invertebrate biomass estimates exceeded plant biomass in 8 of 29 sites when considered by wetland type and year. Total food biomass estimates varied widely ( ${\bar {x}}$ = 6-425 kg/ha) between years and among habitats; thus, using global arithmetic means to estimate food abundance for conservation planning obscures fine scale temporal and spatial variation that may be necessary for management on local and sub-regional levels. Distributions of food biomass estimates were right-skewed, causing us to question whether arithmetic means realistically represent levels of food abundance that all ducks encounter during spring migration. Alternative measures of central tendency (e.g., median) may be more biologically realistic, particularly if spring-migrating ducks are not distributed in an ideal-free manner with respect to food abundance. Future research should determine how ducks distribute themselves in relation to variation in food abundance in space and time during spring migration to strengthen the biological approach to conservation planning in non-breeding Joint Venture areas of the North American Waterfowl Management Plan. © 2011 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Purpose – Recent educational research has demonstrated rural-urban gaps in achievement and schooling conditions. Evidence from developing countries is still sparse. This study seeks to report rural-urban disparities in achievement, student, teacher, and school characteristics based on a nationally representative sample of grade four students from four provinces of Pakistan. The study aims to take into account the limitations of previous research, mainly the issues of non-representative samples and inadequate sampling techniques, by using proportionally adequate sample to address the potential differences in achievement of rural and urban students and how schooling, students and teacher-related factors account for gap in achievement. Design/methodology/approach – The primary data source for the study was the 2006 national assessment survey of year four students in government school across four provinces in four core subjects. The sample design included a two-stage stratified random sample, where the major strata of national interest were student and school gender, geographical location and region. First stage involved selecting schools and in the second stage students were selected from schools. The procedure of estimation involved computing the average of each group's achievement scores and attached standard errors, the gap of standard errors and statistical significance of standard errors at 0.05 level. Findings – The results show that rural and urban students had comparable levels of achievement in some of the tested learning areas. In Balochistan province, rural students outperformed their urban counterparts in three out of the four tested subjects. In Punjab and Sindh, urban students performed significantly better in social studies and language tests; scores on social studies and language did not differ significantly across location in the North West. The differences appeared to be partly explained by variation in schooling conditions, students' home background, and teachers' characteristics. Teachers' training turned out to be decisive in determining students' achievement, whereas availability of resources and multi-grade teaching was less important. Originality/value – Recent educational research from around the world has demonstrated rural-urban gaps in achievement and schooling conditions. Evidence from developing countries is still sparse. This study is the first attempt to report rural-urban disparities in academic achievement, student, teacher, and school characteristics based on a nationally representative sample. The study has employed an appropriate sampling strategy and proportionally adequate sample to address the potential differences in achievement of rural and urban students in four provinces. The findings could therefore be used to guide policy interventions in areas of curriculum differences, schooling conditions, teachers' training and multi-grade teaching across provinces. [ABSTRACT FROM AUTHOR]

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In this paper we discuss the analysis of multi-phase, or multi-stage, case-control studies and present an efficient semiparametric maximum-likelihood approach that unifies and extends earlier work, including the seminal case-control paper by Prentice & Pyke (1979), work by Breslow & Cain (1988), Scott & Wild (1991), Breslow & Holubkov (1997) and others. The theoretical derivations apply to arbitrary binary regression models but we present results for logistic regression and show that the approach can be implemented by including additional intercept terms in the logistic model and then making some simple corrections to the score and information equations used in a Newton–Raphson or Fisher-scoring maximization of the prospective loglikelihood. [ABSTRACT FROM PUBLISHER]

Abstract: The performance of the sampling strategy used in the Botswana Aids Impact Survey II (BAISII) has been studied in detail under a randomized response technique. We have shown that alternative strategies based on the Rao–Harley–Cochran (RHC) sampling scheme for the selection of first stage units perform much better than other strategies. In particular, the combination RHC for the selection of first stage units (fsu''s) and systematic sampling for the selection of second stage units (ssu''s) perform the best when the sample size is small where as the RHC and SRSWOR perform the best when the sample size is large. In view of the present findings it is recommended that the BAISII survey should be studied in more detail incorporating more indicators and increased sample sizes. This is because the BAISII survey design is extensively in use for large scales surveys in Southern African countries. [Copyright &y& Elsevier]

The effects of passive smoking on asthma are well documented, however there is limited research conducted to study the relationship of asthma and smoking among adult populations. This article aims to investigate the gender differences when studying the relationship of asthma prevalence and smoking and further explore if rural/urban living affects the relationship over time. The longitudinal National Population Health Survey (NPHS) dataset was used. For analytic purposes five time periods were used. Generalized estimating equation (GEE) approach was used to obtain the odds ratios and 95% confidence intervals. A total of 11,223 participants ranging in age from 18 to 64; 5,382 men and 5,841 women, were included in the baseline time point (1994–1995). Rural/urban living for the present analysis was an effect modifier for the relationship of asthma prevalence and smoking, and this was true only for women. The results showed that female smokers and ex-smokers residing in rural locations were 1.4 times (95% CI: Rural Smokers = 1.02–1.94, and Rural Ex-smokers = 1.02–2.02) more likely to be diagnosed with asthma compared to non-smoking urban women. Results indicate that the combination of living in a rural area and smoking increases the risk of asthma prevalence among women but not among men. [ABSTRACT FROM AUTHOR]

The article assesses the effect of clustered and biased multi-stage sampling of spatial data for the estimation of the level of radioactivity in Rongelap island in Marshall Islands. A data exploratory method is proposed to detect biased multi-stage collection of spatial data. It describes the corrector models aimed at minimizing the impact on variogram estimation due to the adoption of the type of non-standard sampling designs. A discussion of Rongelap island's data is presented. It proposes a method to adjust for clustering.

Managed moist-soil units support early succession herbaceous vegetation that produces seeds, tubers, and other plant parts used by waterfowl in the Mississippi Alluvial Valley (MAV), USA. We conducted a stratified multi-stage sample survey on state and federal lands in the MAV of Arkansas, Louisiana, Mississippi, and Missouri during autumns 2002-2004 to generate a contemporary estimate of combined dry mass of seeds and tubers (herein seed abundance) in managed moist-soil units for use by the Lower Mississippi Valley Joint Venture (LMVJV) of the North American Waterfowl Management Plan. We also examined variation in mean seed abundance among moist-soil units in 2003 and 2004 in relation to management intensity (active or passive), soil pH and nutrient levels, proportional occurrence of plant life-forms (e.g., grass, flatsedge, and foth; vine; woody plants), and unit area. Estimates of mean seed abundance were similar in 2002 (x̄ = 537.1 kg/ha, SE = 100.1) and 2004 (x̄ = 555.2 kg/ha, SE 105.2) but 35-40% less in 2003 (x̄ 396.8 kg/ha, SE = 116.1). Averaged over years, seed abundance was 496.3 kg/ha (SE = 62.0; CV = 12.5%). Multiple regression analysis indicated seed abundance varied among moist-soil units inversely with proportional occurrence of woody vegetation and unit area and was greater in actively than passively managed units (R²adj = 0.37). Species of early succession grasses occurred more frequently in actively than passively managed units (P ≤ 0.09), whereas mid- and late-succession plants occurred more often in passively managed units (P ≤ 0.02). We recommend the LMVJV consider 556 kg/ha as a measure of seed abundance for use in estimating carrying capacity in managed moist-sod units on public lands in the MAV. We recommend active management of moist-soil units to achieve maximum potential seed production and farther research to determine recovery rates of seeds of various sizes from core samples and the relationship between seed abundance and unit area. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare design-based with model-based methods for analyzing complex survey data. The analysis of survey data collected using a multi-stage sampling design should account for stratification, clustering and unequal inclusion probabilities. We compared the Rao-Wu bootstrap and Taylor linearization (design-based approaches) with logistic regression analysis based on generalized Estimating Equations (GEE) approach (a model-based method). The design and model based approaches were applied and compared using Wave 5 (2002–2003) of the National Population Health Survey (NPHS) dataset. NPHS based on an initial stratified multi-stage design is a continuing longitudinal study under which data is collected on general health information of the Canadian population. Logistic regression was used, as the variable of interest for this study was binary, namely self-reported physician diagnosed asthma. When the three features of the complex survey design were not overlooked standard errors obtained were underestimated. However, accounting for all three features of survey design, the design-based and model-based methods produced similar parameter estimates, while larger standard errors were obtained for design-based methods than for their model-based counterpart. [ABSTRACT FROM AUTHOR]

BranchSampler is a system for computer-assisted manual stereology written for handheld devices running Windows CE. The system has been designed specifically to streamline data collection and optimize sampling of tree-like branching structures, with particular aims of reducing user errors, saving time, and saving data in formats suited for further analysis in other software, for example, a spreadsheet. The system can be applied in a wide range of applications, from biomedical science to agriculture and horticulture. It can be applied for sampling nested generations of lung bronchioles and renal arterioles or for collection and optimizing sampling of crops for precision agriculture. Although the system has been designed specifically for sampling branching structures, it is sufficiently flexible to be used for other applications involving nested stereological designs. We describe the system specifications, software and Graphical User Interface development, functionality and application of the handheld system using four examples: (a) sampling monkey lung bronchioles for estimation of diameter and wall thickness (b) sampling rat kidney for estimating number of arteries and arterioles in a specific generation (c) mapping fruit (apple) tree yield in an orchard and (d) estimating the total leaf surface area of chrysanthemum plants in a greenhouse. [ABSTRACT FROM AUTHOR]

Abstract: Run-to-run variability is a common problem for modeling batch-wise and semi-continuous operated processes. Although observed reactor runs show the same trends in process behaviour, each specific reactor run also shows its own characteristics. Until now, available modeling methods were unable to describe the observed between run variance. In this paper, we present a hierarchical modeling method to solve this problem. A case study for a semi-continuous operated polymer process is analysed to illustrate the hierarchical modeling approach. It was shown that, using the applied modeling method, it is possible to obtain a model which is robust over several reactor runs and which provides a tool for process analysis. Also, the hierarchical model was shown to be superior to a model which lacked an appropriate description of the differences between reactor runs. [Copyright &y& Elsevier]

Abstract: In this paper, methods of deducing the minimum value and the Cauchy–Schwarz inequality were applied to deduce the formulae for the optimum sample sizes for two-stage, two-stage cluster and three-stage sampling, so as to minimize the cost for specified values of the sampling errors and to minimize the sampling errors for specified cost. These formulae were applied successfully in the tuberculosis epidemic survey in Jiangsu Province in China and in the survey of medical cost in Ministry of Railway of China. [Copyright &y& Elsevier]

Abstract: Large-scale sample surveys to estimate abundance and distribution of organisms and their habitats are increasingly important in ecological studies. Multi-stage sampling (MSS) is especially suited to large-scale surveys because of the natural clustering of resources. To illustrate an application, we: (1) designed a stratified MSS to estimate late autumn abundance (kg/ha) of rice seeds in harvested fields as food for waterfowl wintering in the Mississippi Alluvial Valley (MAV); (2) investigated options for improving the MSS design; and (3) compared statistical and cost efficiency of MSS to simulated simple random sampling (SRS). During 2000–2002, we sampled 25–35 landowners per year, 1 or 2 fields per landowner per year, and measured seed mass in 10 soil cores collected within each field. Analysis of variance components and costs for each stage of the survey design indicated that collecting 10 soil cores per field was near the optimum of 11–15, whereas sampling >1 field per landowner provided few benefits because data from fields within landowners were highly correlated. Coefficients of variation (CV) of annual estimates of rice abundance ranged from 0.23 to 0.31 and were limited by variation among landowners and the number of landowners sampled. Design effects representing the statistical efficiency of MSS relative to SRS ranged from 3.2 to 9.0, and simulations indicated SRS would cost, on average, 1.4 times more than MSS because clustering of sample units in MSS decreased travel costs. We recommend MSS as a potential sampling strategy for large-scale natural resource surveys and specifically for future surveys of the availability of rice as food for waterfowl in the MAV and similar areas. [Copyright &y& Elsevier]

This paper provides an overview of the probability sample designs and sampling methods for the Collaborative Psychiatric Epidemiology Studies (CPES): the National Comorbidity Survey Replication (NCS-R), the National Study of American Life (NSAL) and the National Latino and Asian American Study of Mental Health (NLAAS). The multi-stage sample design and respondent selection procedures used in these three studies are based on the University of Michigan Survey Research Center's National Sample designs and operations. The paper begins with a general overview of these designs and procedures and then turns to a more detailed discussion of the adaptation of these general methods to the three specific study designs. The detailed discussions of the individual study samples focus on design characteristics and outcomes that are important to analysts of the CPES data sets and to researchers and statisticians who are planning future studies. The paper describes how the expected survey cost and error structure for each of these surveys influenced the original design of the samples and how actual field experience led to changes and adaptations to arrive at the final samples of each survey population. [ABSTRACT FROM AUTHOR]

Rao (J. Indian Statist. Assoc. 17 (1979) 125) has given a ‘necessary form’ for an unbiased mean square error (MSE) estimator to be ‘uniformly non-negative’. The MSE is of a homogeneous linear estimator ‘subject to a specified constraint’, for a survey population total of a real variable of interest. We present a corresponding theorem when the ‘constraint’ is relaxed. Certain results are added presenting formulae for estimators of MSEs when the variate-values for the sampled individuals are not ascertainable. Though not ascertainable, they are supposed to be suitably estimated either by (1) randomized response techniques covering sensitive issues or by (2) further sampling in ‘subsequent’ stages in specific ways when the initial sampling units are composed of a number of sub-units. Using live numerical data, practical uses of the proposed alternative MSE estimators are demonstrated. [Copyright &y& Elsevier]

Health care audits are crucial in managing the government insurance programs that are estimated to have losses amounting to billions of dollars every year. Statistical methods such as sampling have long been used to handle their size and complexity. Sampling from health care claims data can benefit from multi-stage approaches, especially when the evaluation of the tradeoffs between precision and cost is important. The use of decision models could facilitate health care auditors and policy makers make the best use of these sampling outputs. This paper proposes an integrated multi-stage sampling and decision-making framework that enables auditors address the tradeoffs between audit costs and expected overpayment recovery. We illustrate the framework and discuss insights utilizing a variety of overpayment scenarios for payment populations including U.S. Medicare Part B claims payment data., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 Informa UK Limited, trading as Taylor & Francis Group.)