This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.

This paper discusses the importance of statistics and statisticians in national development with emphasize that government at all levels should embark on building a very viable information system in order to have adequate statistical information for designing a formidable evidence based policy. Given the relevant statistics cited from both Botswana and Nigeria statistical database, this study identifies that no meaningful national development can take place without empowering the national statistical system. In national development, the aspirations of a policy is to attain national goals and to achieve a fair measure of success in the goals, there is need to map out strategic plans, set up machinery for execution of the plans and monitor the implementation process; this is exactly the point at which the role of statistics is vital and relevant.

Warner's randomized response (RR) model is used to collect sensitive information for a broad range of surveys, but it possesses several limitations such as lack of reproducibility, higher costs and it is not feasible for mail questionnaires. To overcome such difficulties, nonrandomized response (NRR) surveys have been proposed. The proposed NRR surveys are limited to simple random sampling with replacement (SRSWR) design. In this paper, NRR procedures are extended to complex survey designs in a unified setup, which is applicable to any sampling design and wider classes of estimators. Existing results for NRR can be derived from the proposed method as special cases.

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A just identified two-equation econometric model is simulated using both Classical and Bayesian procedures. The estimates of the parameters for both methods were compared under a wide range of scenarios; sample size, residual variance and variance of the data on the predetermined variable. The Monte Carlo experiment was performed using E-veiws and WinBUGS computer softwares. The median, being a robust estimator of average in terms of validity, was used as the posterior estimate. As indicated in similar research in the past where the posterior mode was used as estimate, the Bayesian procedure performed better in most cases, while some scenarios showed similar behavior for the two procedures.