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Abstract:
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One objective of adaptive data collection is to secure a better balanced survey response. Methods exist for this purpose, including balancing with respect to selected auxiliary variables. Such variables are also used at the estimation stage for (calibrated) nonresponse weighting adjustment. A key question is how the regression relationship between the survey variable and the auxiliary vector presents itself in the sample as opposed to the response. The dilemma with nonresponse is one of inconsistent regression: A regression model appropriate for the sample often fails for the responding subset, because nonresponse is selective, non-random. In this article, we examine how nonresponse bias in survey estimates depends on regression inconsistency, both seen as functions of response imbalance. As a measure of bias we use the deviation of the calibration adjusted estimator from the unbiased estimate under full response. We study how the deviation and the regression inconsistency depend on the imbalance. We observe in empirical work that both can be reduced, to a degree, by efforts to reduce imbalance by an adaptiv data Collection.
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