Suchergebnisse

The analysis and the comparison of poverty between regions and countries are important topics in social sciences, which have relevant demands of many national (Cáritas, Intermón Oxfam, Cruz Roja, etc) and international (UN, World Bank, OECD, Eurostat, IMF, etc) agencies and organizations. One of the most common poverty indicators in practice is the headcount index, which analyzes the proportion of individuals considered as poor in a population. In this paper, we first analyze the impact on the headcount index when different sampling designs are considered. Note that this study is based on real data sets taken from different countries of the European Union, and the empirical measures for comparisons are based on different Monte Carlo simulation studies. For instance, we observe that stratified sampling has the best performance in comparison to alternative sampling designs. Post-stratification performs similar to simple random sampling without replacement, and the use of auxiliary information provides similar results to ones derived from stratified sampling. Second, we also analyze the empirical performance of different variance estimators under the commented sampling designs. We conclude that they have a similar empirical performance, and they provide, in general, confidence intervals with desirable coverage rates.

Clogg and Eliason (1987) proposed a simple method for taking account of survey weights when fitting log-linear models to contingency tables. This article investigates the properties of this method. A rationale is provided for the method when the weights are constant within the cells of the table. For more general cases, however, it is shown that the standard errors produced by the method are invalid, contrary to claims in the literature. The method is compared to the pseudo maximum likelihood method both theoretically and through an empirical study of social mobility relating daughter's class to father's class using survey data from France. The method of Clogg and Eliason is found to underestimate standard errors systematically. The article concludes by recommending against the use of this method, despite its simplicity. The limitations of the method may be overcome by using the pseudo maximum likelihood method.

Background: Unintended pregnancy related to unmet need is a worldwide problem that affects societies. The main objective of this study was to identify the prevalence and determinants of unmet need for family planning among women aged (15-49) in Ethiopia.Methods: The Performance Monitoring and Accountability2020/Ethiopia was conducted in April 2016 at round-4 from 7494 women with two-stage-stratified sampling. Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted.Results: The prevalence of unmet-need for family planning was 16.2% in Ethiopia. Women between the age range of 15-24 years were 2.266 times more likely to have unmet need family planning compared to above 35 years. Women who were currently married were about 8 times more likely to have unmet need family planning compared to never married women. Women who had no under-five child were 0.125 times less likely to have unmet need family planning compared to those who had more than two-under-5.Conclusion: The key determinants of unmet need family planning in Ethiopia were residence, age, marital-status, education, household members, birth-events and number of under-5 children. Thus the Government of Ethiopia would take immediate steps to address the causes of high unmet need for family planning among women.Keywords: Complex sampling design, Ethiopia, family planning, Performance Monitoring and Accountability, unmet need

Context Feral cats are a major cause of mammal declines and extinctions in Australia. However, cats are elusive and obtaining reliable ecological data is challenging. Although camera traps are increasingly being used to study feral cats, their successful use in northern Australia has been limited. Aims We evaluated the efficacy of camera-trap sampling designs for detecting cats in the tropical savanna of northern Australia. We aimed to develop a camera-trapping method that would yield detection probabilities adequate for precise occupancy estimates. Methods First, we assessed the influence of two micro-habitat placements and three lure types on camera-trap detection rates of feral cats. Second, using multiple camera traps at each site, we examined the relationship between sampling effort and detection probability by using a multi-method occupancy model. Key results We found no significant difference in detection rates of feral cats using a variety of lures and micro-habitat placement. The mean probability of detecting a cat on one camera during one week of sampling was very low (p = 0.15) and had high uncertainty. However, the probability of detecting a cat on at least one of five cameras deployed concurrently on a site was 48% higher (p = 0.22) and had a greater precision. Conclusions The sampling effort required to achieve detection rates adequate to infer occupancy of feral cats by camera trap is considerably higher in northern Australia than has been observed elsewhere in Australia. Adequate detection of feral cats in the tropical savanna of northern Australia will necessitate inclusion of more camera traps and a longer survey duration. Implications Sampling designs using camera traps need to be rigorously trialled and assessed to optimise detection of the target species for different Australian biomes. A standard approach is suggested for detecting feral cats in northern Australian savannas.

"Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations ... Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs."--Publisher

This article discusses the possibilities and constraints of designing an identical or at least comparable sampling strategy across different European countries. It is based on expert reviews from six European Union member states that discuss the possibilities of sampling migrants in their respective countries. The country sample includes two countries from Northern Europe (Sweden, Denmark), two from Continental Europe (Germany, The Netherlands), and two from Southern Europe (Spain, Italy). After a discussion of various definitions of the target population and an overview of existing strategies to sample them, it is investigated which of them can be used in the six countries analyzed in the expert reviews. The focus is on probability samples and the use of population registers, while other sampling strategies are only briefly touched upon. The analysis shows that even with only six European countries an identical register-based sampling design is difficult. The authors propose that, by focusing on sampling immigrants in cities, researchers can better implement sampling strategies which result in comparable samples.

Over the course of 2013 to 2016, over one million asylum seekers arrived in Germany, around 890,000 of them in 2015 alone. The growing refugee population posed a major challenge for Germany's policy makers, civic administrators, and society at large, in finding new approaches to registration procedures, housing, and social and economic integration. To design policies and programs that meet these needs, government administrators, politicians, and the public require robust analyses of the accompanying social and demographic changes based on timely, valid, and reliable empirical data. Yet despite the urgent need for quantitative data on this target group, survey organizations and data collection agencies had little experience gaining access to the target population and approaching and surveying them effectively. In late 2015, when the influx reached its peak, the Institute for Employment Research (IAB), the Migration, Integration and Asylum Research Center at the Federal Office for Migration and Refugees (BAMF-FZ), and the Socio-Economic Panel (SOEP) joined together in a cooperative longitudinal project to survey a nationwide random sample of refugee households in Germany: the IAB-BAMF-SOEP Survey of Refugees. In this paper, we summarize the sampling and fieldwork design as well as the challenges faced in the IAB-BAMF-SOEP Survey of Refugees. We discuss the sequential strategy applied for sampling recent refugees and asylum seekers who arrived in Germany, particularly in 2015, in such large numbers that proper registration was delayed, and in many cases their initial accommodations were only temporary. Moreover, the paper discusses alternative survey instruments introduced for the difficult-to-interview population of the IAB-BAMF-SOEP Survey of Refugees, including translated questionnaires and audio files.

AbstractWe consider the Hanurav–Vijayan (HV) sampling design, which is the default method programmed in the SURVEYSELECT procedure of the SAS software. We prove that it is equivalent to the Sunter procedure but is capable of handling any set of inclusion probabilities. We prove that the Horvitz–Thompson (HT) estimator is not generally consistent under this sampling design. We propose a conditional HT estimator and prove its consistency under a nonstandard assumption on the first-order inclusion probabilities. Since this assumption seems difficult to control in practice, we recommend not to use the HV sampling design.