Dual-frame Survey Research Articles

Estimation in Dual Frame Surveys With Complex Designs

Abstract In a dual frame survey, samples are drawn independently from two overlapping frames that are assumed to cover the population of interest. This article considers the case when at least one of the samples is selected by a complex design involving, e.g., multistage sampling. A “pseudo”—maximum likelihood estimator of a population total or a mean for such dual frame surveys is proposed. An advantage of the proposed estimator is that the same weights are used for all the variables, unlike the estimators of Hartley and Fuller and Burmeister. Asymptotic properties of the estimator are studied, including its efficiency. An alternative “single frame” estimator, based on the design induced by the two separate designs, is also studied. Results of a limited simulation study indicate that our estimator is essentially as efficient as those of Hartley and Fuller and Burmeister and can lead to significant efficiency gains over the single frame estimator.

On the Efficiency of Raking Ratio Estimation for Multiple Frame Surveys

Abstract Multiple frame surveys involve the combination of samples selected from separate sampling frames. A topical example is a dual-frame survey that combines a sample of households interviewed by telephone with a sample drawn from a frame including nontelephone households. Various estimators of population totals and means have been proposed for multiple frame surveys. In particular Bankier proposed the application of raking ratio estimation and compared its performance to alternative estimators in a numerical study based on dual-frame Statistics Canada data. He concluded that the raking ratio estimator performed well but gave no theoretical results to support his empirical findings. This article provides a theoretical study of the efficiency of the raking ratio estimator for multiple-frame surveys. Attention is restricted mainly to the classical, unstratified two-frame case considered by Hartley. For the estimation of totals, we give a closed-form expression for the limiting form of the raking ratio e...

On the Efficiency of Raking Ratio Estimation for Multiple Frame Surveys

Abstract Multiple frame surveys involve the combination of samples selected from separate sampling frames. A topical example is a dual-frame survey that combines a sample of households interviewed by telephone with a sample drawn from a frame including nontelephone households. Various estimators of population totals and means have been proposed for multiple frame surveys. In particular Bankier proposed the application of raking ratio estimation and compared its performance to alternative estimators in a numerical study based on dual-frame Statistics Canada data. He concluded that the raking ratio estimator performed well but gave no theoretical results to support his empirical findings. This article provides a theoretical study of the efficiency of the raking ratio estimator for multiple-frame surveys. Attention is restricted mainly to the classical, unstratified two-frame case considered by Hartley. For the estimation of totals, we give a closed-form expression for the limiting form of the raking ratio estimator as the number of iterations increases. We show that this limiting estimator is consistent and obtain an expression for its asymptotic variance. Using this expression we show that this estimator is uniformly less efficient than an estimator proposed by Fuller and Burmeister. However, the loss of efficiency is small in some special cases. The raking ratio estimator has the advantage that it extends simply to more than two frames and to stratified sampling, whereas the corresponding extension of the Fuller–Burmeister estimator is more problematic.

A Mean Squared Error Model for Dual Frame, Mixed Mode Survey Design

Abstract An error model for dual frame survey designs is developed. It includes components of error for sampling variance, interviewer variance, and bias in each frame. A cost model that attempts to capture the complexity of a full scale dual frame survey is presented. The error and cost models are applied to a large national survey, the National Crime Survey, and the effect that alternative levels of bias in both frames have on the optimal allocation of sample to the two frames is examined for two types of crime.