Survey Methodology
A small area estimation approach for reconciling differences in two surveys of recreational fishing effort

by Teng Liu, F. Jay Breidt and Jean D. OpsomerNote 1

  • Release date: December 20, 2024
More information PDF version

Abstract

Many studies face the problem of comparing estimates obtained with different survey methodology, including differences in frames, measurement instruments, and modes of delivery. The problem arises in multimode surveys and in surveys that are redesigned. Major redesign of survey processes could affect survey estimates systematically, and it is important to quantify and adjust for such discontinuities between the designs to ensure comparability of estimates over time. We propose a small area estimation approach to reconcile two sets of survey estimates, and apply it to two surveys in the Marine Recreational Information Program (MRIP), which monitors recreational fishing along the Atlantic and Gulf coasts of the United States. We develop a log-normal model for the estimates from the two surveys, accounting for temporal dynamics through regression on population size and state-by-wave seasonal factors, and accounting in part for changing coverage properties through regression on wireless telephone penetration. Using the estimated design variances, we develop a regression model that is analytically consistent with the log-normal mean model. We use the modeled design variances in a Fay-Herriot small area estimation procedure to obtain empirical best linear unbiased predictors of the reconciled estimates of fishing effort (requiring predictions at new sets of covariates), and provide an asymptotically valid mean square error approximation.

Key Words:    Coverage error; EBLUP; Fay-Herriot model; Log-normal model; MSE approximation; Nonsampling error.

Table of contents

How to cite

Liu, T., Breidt, F.J. and Opsomer, J.D. (2024). A small area estimation approach for reconciling differences in two surveys of recreational fishing effort. Survey Methodology, 50(2), 323-345. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2024002/article/00009-eng.pdf.

Note

More information

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.

Submission of Manuscripts

Survey Methodology is published twice a year in electronic format. Authors are invited to submit their articles in English or French in electronic form, preferably in Word to the Editor, (statcan.smj-rte.statcan@canada.ca, Statistics Canada, 150 Tunney’s Pasture Driveway, Ottawa, Ontario, Canada, K1A 0T6). For formatting instructions, please see the guidelines provided in the journal and on the web site (www.statcan.gc.ca/SurveyMethodology).

Note of appreciation

Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.

Copyright

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, 2024

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

Ottawa

close
Date modified: