Analysis of speed of improved maize (BH-540) variety adoption among smallholder farmers in Northwestern Ethiopia: count outcome model.

Low maize yield and productivity are major contributors to Ethiopia's severe food insecurity and poverty. Generous efforts have been made by various stakeholders such as producers, and governmental and non-governmental organizations, to increase the country's maize yield and productivity. However, the outcome is not as expected to achieve food security and poverty reduction. Hence, the purpose of this study was to determine factors influencing the speed of adoption of the improved maize (BH-540) variety in the Central Gondar zone. A three-stage sampling method was used to select a total of 385 smallholder farmers. Moreover, a negative binomial regression was used to determine factors influencing the speed of adopting the improved maize (BH-540) variety. The negative binomial regression model revealed that the age of the household head, farm size, and membership of the cooperative were statistically significant and positively affected the speed of adopting the improved maize (BH-540) variety, whereas distance to the nearest market and access to credit were statistically significant and inversely affected the speed of adopting the improved maize (BH-540) variety. Therefore, this study suggests that the native administration ought to organize skill division and provide short-range keeping fit packages to input suppliers, producers, traders, and development agents in each district. Moreover, supporting and strengthening the current agricultural cooperatives is advisable to strengthen farmer-to-farmer skill allotment by providing mindfulness conception, benefits, and numerous infrastructures. Furthermore, the trade and market development department should be designed to establish improved seed market institutions in each district.

Integrated statistical and decision models for multi-stage health care audit sampling.

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.

A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs.

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.

Design-based single-mediator approach for complex survey data.

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.

A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits.

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.