1987•Journal of the American Statistical AssociationRequires access

Comparison of Purposive and Random Sampling Schemes for Estimating Capital Expenditure

T. S. Karmel, Malti Jain

Open publisher page 35 citations

Abstract

This article reports results of a large-scale study of various sampling strategies. Conventional sampling strategies are compared with model-based strategies on data from over 12,000 businesses included in the annual Manufacturing Census of the Australian Bureau of Statistics. The study has been designed to replicate the quarterly Survey of Capital Expenditure. The results show that for the given data a stratified sample consisting of units with the largest values of the auxiliary variable in each stratum and simple ratio estimation is by far the most efficient of the strategies considered. A meaningful estimate of sampling error can be derived.

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What this paper is about

This article reports results of a large-scale study of various sampling strategies. Conventional sampling strategies are compared with model-based strategies on data from over 12,000 businesses included in the annual Manufacturing Census of the Australian Bureau of Statistics. The study has been designed to replicate the quarterly Survey of Capital Expenditure. The results show that for the given data a stratified sample consisting of units with the largest values of the auxiliary variable in each stratum and simple ratio estimation is by far the most efficient of the strategies considered. A meaningful estimate of sampling error can be derived.

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Available abstract

This article reports results of a large-scale study of various sampling strategies. Conventional sampling strategies are compared with model-based strategies on data from over 12,000 businesses included in the annual Manufacturing Census of the Australian Bureau of Statistics. The study has been designed to replicate the quarterly Survey of Capital Expenditure. The results show that for the given data a stratified sample consisting of units with the largest values of the auxiliary variable in each stratum and simple ratio estimation is by far the most efficient of the strategies considered. A meaningful estimate of sampling error can be derived.

Key concepts: Simple random sample, Nonprobability sampling, Stratified sampling, Statistics, Sampling (signal processing), Econometrics, Replicate, Sampling design

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