2012Journal of Beijing Union UniversityRequires access

A Comparative Research Via Sampling Methods——A Case Study of the Commute Time in Beijing

Yao Gui-fen

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Abstract

Based on the two large samples from the surveys about the commute time in Beijing in 2005 and 2010,the schemes and results between the simple random sampling and stratified sampling methods are compared.The results show that the simple random sampling should be taken when there is an absence of prior knowledge.On the other hand,when there is abundant prior knowledge and the research objects are spatial correlated,the stratified sampling is more reasonable.For the spatial stratified sampling,stratifying the samples based on the comprehensive factors can make schemes more reasonable and improve sampling efficiency.

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What this paper is about

Based on the two large samples from the surveys about the commute time in Beijing in 2005 and 2010,the schemes and results between the simple random sampling and stratified sampling methods are compared.The results show that the simple random sampling should be taken when there is an absence of prior knowledge.On the other hand,when there is abundant prior knowledge and the research objects are spatial correlated,the stratified sampling is more reasonable.For the spatial stratified sampling,stratifying the samples based on the comprehensive factors can make schemes more reasonable and improve sampling efficiency.

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Available abstract

Based on the two large samples from the surveys about the commute time in Beijing in 2005 and 2010,the schemes and results between the simple random sampling and stratified sampling methods are compared.The results show that the simple random sampling should be taken when there is an absence of prior knowledge.On the other hand,when there is abundant prior knowledge and the research objects are spatial correlated,the stratified sampling is more reasonable.For the spatial stratified sampling,stratifying the samples based on the comprehensive factors can make schemes more reasonable and improve sampling efficiency.

Key concepts: Stratified sampling, Beijing, Simple random sample, Sampling (signal processing), Systematic sampling, Sampling design, Cluster sampling, Statistics

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