When Money is Tight and Requirements are High: Using Nonprobability Samples in Longitudinal Household Studies.

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Longitudinal studies are of paramount importance to study how people’s circumstances change over time and have become increasingly popular in many different research fields. Recent years have seen however a surge in the challenges posed to longitudinal designs. In particular, while the rising pressures on government coffers have clearly limited the amount of resources available for scientific research, the demand for high-quality (panel) data rose considerably as well as the costs necessary to provide the desired quality standard. Against this background, increasing attention is devoted to the use of nonprobability samples for scientific research as cost-effective alternatives. To date, quite a few studies have compared the representativeness of probability and nonprobability samples as well as the quality of the collected measures, both in term of accuracy (i.e. the difference from trusted benchmarks) and validity (such as testing the correlation with theoretically related items or the predictive power of the answers). The evidence so far is however scanty. Furthermore, no study has carried such comparisons within a longitudinal setting. The present work contributes to this literature analyzing the validity of the answers given by a probability and a nonprobability sample in the household panel survey “Saving and Old-Age Provision in Germany” (SAVE), which consists of two subsamples differing by their sampling scheme. The concurrent and predictive validity of the answers as well as the degree of satisficing are taken to assess measurement quality and are compared over time.

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