A structured approach to web panel surveys: the use of a sequential framework for non-random survey sampling inference

摘要

Web access panels are self selected panels constructed with the aim of drawing inference for general populations, including large segments of the population who rarely or never access the Internet. A common approach for modeling survey data collected over access panels is combing it with data collected by a randomlyudselected reference survey sample from the target population of Interest. The act of joining the panel is then treated as a random process where each memberudof the population has a positive probability of participating in the survey. The combined reference and panel survey sample can then be used for different estimation approaches which model either the selection process or the measurement of interest, or some case the two together. Most practitioners and academics whoudhave considered this combined sample approach, model the selection process by a single phase process from the target population directly to the observed sampleudset.udIn the following work, I assume selection into the panel is a sequential rather than a single phase process and offer several estimators that are underlined byudappropriate sequential models. After a careful investigation of a variety of single phase methods applied in practice, I demonstrate the benefits a sequential framework has to the panel problem. One notable strength of this approach is that byudassuming a sequential framework the modeler can include important variables associated with Internet and Web usage. Under a single phase model inclusionudof such information would invalidate basic assumptions such as independence between selection and model covariates.udIn this work I also suggest a carefully structured panel estimation strategy, combining a sample selection design with chosen estimator. Under the sequentialudframework I demonstrate the potential of combining a within-panel random sampling procedure, that is balanced on a sequence of target statistics, with estimators that are modeled over both the selection process and the variable of interest. I show that this strategy has several robustness properties over and beyond currently applied estimators. I conclude by describing an estimation algorithm which applies this estimation strategy to the combined panel and reference survey sample case.