Estimation in Elusive Populations Using Multiple Frames and Two-Phase Multiple Frames in the Presence of Measurement and Response Errors
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 5, September 2020, Pages: 173-184
Received: Aug. 4, 2020;
Accepted: Aug. 17, 2020;
Published: Sep. 8, 2020
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Mutanu Beth, Mathematics and Actuarial Science Department, South Eastern Kenya University, Kitui, Kenya
Kahiri James, Mathematics and Actuarial Science Department, Kenyatta University, Nairobi, Kenya
Odongo Leo, Mathematics and Actuarial Science Department, Kenyatta University, Nairobi, Kenya
Accurate survey data is important for planning and decision making. The presence of measurement and response errors in surveys has been known to negatively affect the efficiency of estimates as well as to create biases in estimates. It is important to investigate the effects of measurement and response errors when computing survey data so as to obtain reliable information for use by statisticians and policy makers. Unavailability of a sampling frame in a survey for elusive populations has led to the application of multiple frames in sample selection processes. This paper investigates the effect of measurement and response errors in population estimation under multiple and two-phase multiple frames for elusive populations. The effect of random errors and biases from systematic errors on simple and correlated response variances under various levels of multiplicity adjustment factor in multiple frames is carried out. A numerical example is given assuming simple random sampling. The net effect of the errors has been found to inflate simple and correlated response variances and hence overestimation of the variances under different variance estimators. It is therefore recommended that both measurement and response errors be put into consideration when designing and carrying out a survey for more accurate results.
Estimation in Elusive Populations Using Multiple Frames and Two-Phase Multiple Frames in the Presence of Measurement and Response Errors, American Journal of Theoretical and Applied Statistics.
Vol. 9, No. 5,
2020, pp. 173-184.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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