SEQUENTIAL DATA WEIGHTING PROCEDURES FOR COMBINED RATIO ESTIMATORS IN COMPLEX SAMPLE SURVEYS

In sample surveys weighting is applied to data to increase the quality of estimates. Data weighting can be used for several purposes. Sample design weights can be used to adjust the differences in selection probabilities for non-self weighting sample designs. Sample design weights, adjusted for nonresponse and non-coverage through the sequential data weighting process. The unequal selection probability designs represented the complex sampling designs. Among many reasons of weighting, the most important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance estimation for the population ratio mean. To illustrate the utility of the proposed estimator, Turkish Demographic and Health Survey 2003 real life data is used. It is shown that the use of auxiliary information on weights can considerably improve the efficiency of the estimates

Models for survey nonresponse and bias adjustment techniques

Survey statisticians have been dealing with the issues of nonresponse in sample surveys for many years. Due to the complex nature of the mechanism, so far it has not been easy to find a general solution to this problem. In this paper, several aspects of this topic will be elaborated on: the survey unit nonresponse bias has been examined alternatively by taking response amounts which are fixed initially and also by taking the response amounts as random variables. An overview of the components of the bias due to nonresponse will be performed. Nonresponse bias components are illustrated for each alternative approach and the amount of bias was computed for each case

Sequential data weighting procedures for combined ratio estimators ın complex sample surveys

In sample surveys weighting is applied to data to increase the quality of estimates. Data weighting can be used for several purposes. Sample design weights can be used to adjust the differences in selection probabilities for non-self weighting sample designs. Sample design weights, adjusted for nonresponse and noncoverage through the sequential data weighting process. The unequal selection probability designs represented the complex sampling designs. Among many reasons of weighting, the most important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance estimation for the population ratio mean. To illustrate the utility of the proposed estimator, Turkish Demographic and Health Survey 2003 real life data is used. It is shown that the use of auxiliary information on weights can considerably improve the efficiency of the estimates

Probability Sample Selection Method in Household Surveys When Current Data on Regional Population is Unavailable

Availability of the perfect sampling frame only exists in developed countries, which covers a very small proportion of the world countries. On the other hand, in developing countries lists of the latest population census counts are generally used as the sampling frame for sample surveys. Therefore, in developing countries surveys which are planned for future periods long after the census date, cannot be representative of the related time period if the same census counts are utilized. Instead, population projections and data adjustment methodologies must be used to provide a representative probability selection of the updated population. This article proposes a population projection and adjustment methodology in order to establish the ideal selection probability for household surveys. The method contains the correction on the differences of the sum of strata and aggregated values. Comparative examples are also provided to clarify the proposed methodology

Interviewer allocation through interview-reinterview nested design for response error estimation in sample surveys

In surveys, non-sampling errors, due to their complex nature, are more challenging to quantify compared to sampling errors. Avoiding the release of these errors, however, results in biased survey estimates. In our previous paper, we devised the best interviewer allocation technique by using a nested experimental design to study response error estimation. In this study, in order to illustrate the effectiveness of this methodology in a different context, we apply it in interview-reinterview surveys relating to the time use and life satisfaction of academicians at Middle East Technical University, Turkey. An analysis of the pilot survey data showed that only half of the data was reliable, while the other half revealed interviewer effects. Prior to the main survey, interviewers underwent training in the course of which particular emphasis was put on the above-mentioned questions. In effect, the previously observed response variances which accounted for the total variance and data unreliability, were reduced considerably, increasing the quality of the main survey

Memory recall errors in retrospective surveys: A reverse record check study

memory recall error, record linkage, matching procedures, record check study,