Livro de amostragem

Dual Frame Surveys: Recent Developments and Challenges
Sharon L. Lohr

Abstract In a dual frame survey, independent samples are drawn from two frames whose union covers the population. Most estimators, however, have been developed under the assumption that there is no nonresponse or measurement error. We review estimators for dual frame surveys and examine their properties in the presence of nonsampling errors. We also discuss implications of nonsampling errors for survey design. Key words: Combining Data from Different Sources, Misclassification, Multiple Frame Survey, Mode Effects, Nonresponse, Sampling for Rare Events

1 Introduction
In an ideal sampling world, we have a finite population U with N units, with yi a measurement on unit i in the population. A probability sample S is taken from the ˆ frame, and the finite population total Y = ∑N yi is estimated by Y = ∑i∈S wi yi , i=1 ˆ where wi = 1/πi is the sampling weight. The estimator Y is unbiased for Y if the sampling frame includes everyone in the target population, if all sampled units respond, and if there is no measurement error. In practice, of course, these assumptions are rarely met. Nonresponse rates are increasing, which means that survey estimates rely more on models and often have nonresponse bias. While sampling frames may be improving, undercoverage of specific subpopulations continues to be a problem. With populations having increasingly diverse languages and technological access, multiple modes may be needed to sample the entire population; this may result in measurement error if there are mode effects. Typically, nonresponse and undercoverage are dealt with through weight adjustments—increasing weights of selected
Sharon L. Lohr School of Mathematical and Statistical Sciences, Arizona State University, Tempe AZ 85287-1804 USA, e-mail: [e-mail]

1

2

Sharon L. Lohr

respondents in an attempt to reduce bias. Attempts are made to reduce measurement error through careful