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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.

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.

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.

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.

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]

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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]

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]

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: 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]

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.)