Analysis

Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

The biases and stabilities of alternative variance estimators for the two stage random group design (Rao et al. 1962) are evaluated in a Monte Carlo study in the context of Canadian Labour Force Survey. The variance formula for raking ratio estimation procedure is derived using Taylor linearization method. The properties of the variance formula are investigated by a Monte Carlo simulation.

The cost-variance optimization of the design of the Canadian Labour Force Survey was carried out in two steps. First, the sample designs were optimized for each of the two major area types, the Self-Representing (SR) and the Non-Self-Representing (NSR) areas. Cost models were developed and parameters estimated from a detailed field study and by simulation, while variances were estimated using data from the Census of Population. The scope of the optimization included the allocation of sample to the two stages in the SR design, and the consideration of two alternatives to the old design in NSR areas. The second stage of optimization was the allocation of sample to SR and NSR areas.

This study considers the suitability of composite estimation techniques for the Canadian Labour Force Survey. The performance of a class of AK composite estimators introduced initially by Gurney and Daly is investigated for several characteristics. While the ordinary composite estimate has a large bias, the AK composite estimate is capable of reducing the bias. Composite estimates having minimum variance and minimum mean square error are compared.

Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

The biases and stabilities of alternative variance estimators for the two stage random group design (Rao et al. 1962) are evaluated in a Monte Carlo study in the context of Canadian Labour Force Survey. The variance formula for raking ratio estimation procedure is derived using Taylor linearization method. The properties of the variance formula are investigated by a Monte Carlo simulation.

The cost-variance optimization of the design of the Canadian Labour Force Survey was carried out in two steps. First, the sample designs were optimized for each of the two major area types, the Self-Representing (SR) and the Non-Self-Representing (NSR) areas. Cost models were developed and parameters estimated from a detailed field study and by simulation, while variances were estimated using data from the Census of Population. The scope of the optimization included the allocation of sample to the two stages in the SR design, and the consideration of two alternatives to the old design in NSR areas. The second stage of optimization was the allocation of sample to SR and NSR areas.

This study considers the suitability of composite estimation techniques for the Canadian Labour Force Survey. The performance of a class of AK composite estimators introduced initially by Gurney and Daly is investigated for several characteristics. While the ordinary composite estimate has a large bias, the AK composite estimate is capable of reducing the bias. Composite estimates having minimum variance and minimum mean square error are compared.