Coupling methods for multistage sampling

Chauvet, Guillaume

doi:10.1214/15-aos1348

Cited by 38 publications

(30 citation statements)

References 32 publications

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“…The study adopted a multistage sampling technique which is commonly used when the population is scattered over a wide area and three or more stages of sampling applied (Chauvet, 2015). Stage one was identification of Kirinyaga and Murang"a Counties from the 47 Counties using convenience sampling due to their proximity to the researcher.…”

Section: Design Target Population and Samplingmentioning

confidence: 99%

Psychosocial Programmes and Employees Retirement Preparedness: Empirical Evidence From the Educational Sector in Kenya

Gathiira¹,

Muathe²,

Kilika³

2019

IJBA

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Retirement is a process with employees planning decisions generally focusing on the subjective life expectancy, a mental model of years remaining before one dies. Indeed, the real exit of an individual from a career job is accompanied by changes that include social and psychological, resources leading to variations in an individual’s well-being. The purpose of this study was to assess how employees’ engagement in psychosocial programmes affects their retirement preparedness in the education sector in Kenya. The target population was 1,238 teachers aged 50 years and above and employed in public secondary schools by the Teachers Service Commission in Kirinyaga and Murang’a Counties by 2017. A representative sample of 334 respondents was selected using multistage sampling technique. Data was collected using semi structured questionnaire and interview guide. Logit regression was used to establish the relationships between variables in the study and to test the null hypotheses at P ≤ 0.05 confidence level. The findings indicate that even though the sampled pre-retiree teachers were not adequately prepared for retirement psychosocially, yet their engagement in psychosocial programmes increases their retirement preparedness level. The reported findings extend the current understanding of employee separation programmes and raise implications for the various theories that underpin employee separation decisions in HRM.

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Section: Design Target Population and Samplingmentioning

confidence: 99%

Psychosocial Programmes and Employees Retirement Preparedness: Empirical Evidence From the Educational Sector in Kenya

Gathiira¹,

Muathe²,

Kilika³

2019

IJBA

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“…PSU, Secondary Sampling Unit (SSU), Tertiary Sampling Unit (TSU) and so on). Multistage sampling is employed near universally for household surveys, particularly as a cost control strategy where the population is sparsely distributed [51]. For the current work, each stratum has a list of sampling units.…”

Section: Sampling Strategymentioning

confidence: 99%

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

et al. 2020

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Background: Household surveys are the main source of demographic, health and socioeconomic data in low-and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames. Methods: Previously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous. Results: The quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and prewar regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach. Conclusion: Our approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized

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“…The coupling method is a powerful tool to study the asymptotic behaviour of estimators under complex sampling designs. Another coupling algorithm was used by Chauvet () to obtain asymptotic results for multistage sampling designs with stratified simple random without replacement sampling at the first stage.…”

Section: Large Entropy Sampling Designsmentioning

confidence: 99%

“…Krewski and Rao () studied the case when the primary units are selected with replacement, and Ohlsson () derived a general central limit theorem for such designs. Recently, Chauvet () considered coupling methods to prove the asymptotic normality of the HT estimator and the validity of a bootstrap procedure for stratified simple random sampling at the first stage. However, there is a lack of general conditions ensuring that properties (a)–(c) hold for general two‐stage sampling designs, and this is the purpose of the present paper.…”

Section: Introductionmentioning

confidence: 99%

Inference for Two-Stage Sampling Designs

Chauvet

Vallée

2020

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary Two‐stage sampling designs are commonly used for household and health surveys. To produce reliable estimators with associated confidence intervals, some basic statistical properties like consistency and asymptotic normality of the Horvitz–Thompson estimator are desirable, along with the consistency of associated variance estimators. These properties have been mainly studied for single‐stage sampling designs. In this work, we prove the consistency of the Horvitz–Thompson estimator and of associated variance estimators for a general class of two‐stage sampling designs, under mild assumptions. We also study two‐stage sampling with a large entropy sampling design at the first stage and prove that the Horvitz–Thompson estimator is asymptotically normally distributed through a coupling argument. When the first‐stage sampling fraction is negligible, simplified variance estimators which do not require estimating the variance within the primary sampling units are proposed and shown to be consistent. An application to a panel for urban policy, which is the initial motivation for this work, is also presented.

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Coupling methods for multistage sampling

Cited by 38 publications

References 32 publications

Psychosocial Programmes and Employees Retirement Preparedness: Empirical Evidence From the Educational Sector in Kenya

Psychosocial Programmes and Employees Retirement Preparedness: Empirical Evidence From the Educational Sector in Kenya

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

Inference for Two-Stage Sampling Designs

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