Survey Methodology
Multiple-frame surveys for a multiple-data-source world

by Sharon L. LohrNote 1

  • Release date: January 6, 2022
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Abstract

Multiple-frame surveys, in which independent probability samples are selected from each of Q MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiFu0Je9sqqrpepC0xbbL8F4rqqrFfpu0de9LqFHe9Lq pepeea0xd9q8as0=LqLs=Jirpepeea0=as0Fb9pgea0lrP0xe9Fve9 Fve9qapdbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyuaaaa@38EC@ sampling frames, have long been used to improve coverage, to reduce costs, or to increase sample sizes for subpopulations of interest. Much of the theory has been developed assuming that (1) the union of the frames covers the population of interest, (2) a full-response probability sample is selected from each frame, (3) the variables of interest are measured in each sample with no measurement error, and (4) sufficient information exists to account for frame overlap when computing estimates. After reviewing design, estimation, and calibration for traditional multiple-frame surveys, I consider modifications of the assumptions that allow a multiple-frame structure to serve as an organizing principle for other data combination methods such as mass imputation, sample matching, small area estimation, and capture-recapture estimation. Finally, I discuss how results from multiple-frame survey research can be used when designing and evaluating data collection systems that integrate multiple sources of data.

Key Words:   Combining data; Data integration; Dual-frame survey; Indirect sampling; Mass imputation; Misclassification; Survey design; Undercoverage.

Table of contents

How to cite

Lohr, S.L. (2021). Multiple-frame surveys for a multiple-data-source world. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 47, No. 2. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021002/article/00008-eng.htm.

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ISSN : 1492-0921

Editorial policy

Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada as represented by the Minister of Industry, 2022

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Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Classical multiple-frame survey structure and assumptions
Section 3. Estimation in classical multiple-frame surveys
Section 4. Multiple-frame surveys and data integration
Section 5. Design of data collection systems
Acknowledgements
References

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