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Cited by 520

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Inference from Complex Samples

Open access
Kish, Leslie, Frankel, Martin Richard·1974·Journal of the Royal Statistical Society: Series B (Methodological)
DOI 10.1111/j.2517-6161.1974.tb00981.x
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Abstract

Summary: The design of complex samples induces correlations between element values. In stratification negative correlation reduces the variance; but that gain is less for subclass means, and even less for their differences and for complex statistics. Clustering induces larger and positive correlations between element values. The resulting increase in variance is measured by the ratio deff, and is often severe. This is reduced but persists for subclass means, their differences, and for analytical statistics. Three methods for computing variances are compared in a large empirical study. The results are encouraging and useful.

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Crossref · 10.1111/j.2517-6161.1974.tb00981.x

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Metadata

Authors
Kish, L., & Frankel, M. R.
Venue
Journal of the Royal Statistical Society: Series B (Methodological)
Publisher
Oxford University Press (OUP)
Published
1974-09
Volume
36
Issue
1
Pages
1-22
ISSN
  • 0035-9246 (ISSN-L)
  • 2517-6161
DOI
10.1111/j.2517-6161.1974.tb00981.x
OpenAlex
W143431577
Cited by
520
References
1
FWCI
6.39
Type
journal article
Language
en
Open access
green

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.