Selecting Samples from List Frames of Businesses

Sigman, Richard; Monsour, Nash J.

doi:10.1002/9781118150504.ch8

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…The sample allocation to the h ‐th stratum is then made on the basis of the anticipated moments of Y knowing that X is in [ b h −1 , b h ). This approach is discussed in Dayal (1985) and Sigman & Monsour (1995).…”

Section: Introductionmentioning

confidence: 99%

A General Algorithm for Univariate Stratification

Baillargeon¹,

Rivest²

2009

Int Statistical Rev

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This paper presents a general algorithm for constructing strata in a population using "X", a univariate stratification variable known for all the units in the population. Stratum "h" consists of all the units with an "X" value in the interval ["b" "h" - 1 , "b h") . The stratum boundaries {"b h"} are obtained by minimizing the anticipated sample size for estimating the population total of a survey variable "Y" with a given level of precision. The stratification criterion allows the presence of a take-none and of a take-all stratum. The sample is allocated to the strata using a general rule that features proportional allocation, Neyman allocation, and power allocation as special cases. The optimization can take into account a stratum-specific anticipated non-response and a model for the relationship between the stratification variable "X" and the survey variable "Y". A loglinear model with stratum-specific mortality for "Y" given "X" is presented in detail. Two numerical algorithms for determining the optimal stratum boundaries, attributable to Sethi and Kozak, are compared in a numerical study. Several examples illustrate the stratified designs that can be constructed with the proposed methodology. All the calculations presented in this paper were carried out with stratification , an R package that will be available on CRAN (Comprehensive R Archive Network). Copyright (c) 2009 The Authors. Journal compilation (c) 2009 International Statistical Institute.

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Section: Introductionmentioning

confidence: 99%

A General Algorithm for Univariate Stratification

Baillargeon¹,

Rivest²

2009

Int Statistical Rev

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“…Bailar 1975) -the property that a unit's response to a question depends on how many times it has been included in the survey. In principle this effect might be expected in business surveys, and Srinath (1987) and Sigman & Monsour (1995) both mention it as an issue to be considered. However, we have not located any published examples where an attempt has been made to estimate RGB in a business survey.…”

Section: Social Survey Researchers Have Given Considerable Attention ...mentioning

confidence: 93%

A review and evaluation of the use of longitudinal approaches in business surveys

Smith

Yung

2019

Longitudinal and Life Course Studies

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Business surveys are not generally considered to be longitudinal by design. However, the largest businesses are almost always included in each wave of recurrent surveys because they are essential for producing good estimates; and short-period business surveys frequently make use of rotating panel designs to improve the estimates of change by inducing sample overlaps between different periods. These design features mean that business surveys share some methodological challenges with longitudinal surveys. We review the longitudinal methods and approaches which can be used to improve the design and operation of business surveys, giving examples of their use. We also look in the other direction, considering the aspects of longitudinal analysis which have the potential to improve the accuracy, relevance and interpretation of business survey outputs.

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“…Thus, it is more appealing to survey planners to have a take-all stratum with the largest units and some take-some strata with the middle-size and small units. See, for example, Sigman and Monsour (1995) and Slanta and Krenzke (1996).…”

Section: Introductionmentioning

confidence: 99%

On Convergence of Stratification Algorithms for Skewed Populations

Park

2009

Korean Journal of Applied Statistics

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For stratifying skewed populations, the Lavallée-Hidiroglou(LH) algorithm is often considered to have a take-all stratum with the largest units and some take-some strata with the middle-size and small units.Related to its iterative nature have been reported some numerical difficulties such as the dependency of the ultimate stratum boundaries to a choice of initial boundaries and the slow convergence to locally-optimum boundaries. The geometric stratification has been recently proposed to provide initial boundaries that can avoid such numerical difficulties in implementing the LH algorithm. Since the geometric stratification does not pursuit the optimization but the equalization of the stratum CVs, the corresponding stratum boundaries may not be (near) optimal. This paper revisits these issues concerning convergence and near-optimality of optimal stratification algorithms using artificial numerical examples. We also discuss the formation of the strata and the sample allocation under the optimization process and some aspects related to discontinuity arisen from the finiteness of both population and sample as well.

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Selecting Samples from List Frames of Businesses

Cited by 10 publications

References 30 publications

A General Algorithm for Univariate Stratification

A General Algorithm for Univariate Stratification

A review and evaluation of the use of longitudinal approaches in business surveys

On Convergence of Stratification Algorithms for Skewed Populations

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