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Evaluating Online Nonprobability Surveys 1. The top performing sample was nearly 1. The most poorly performing samples yielded estimates that were about 10 percentage points off from the benchmark values on average. In general, samples with more elaborate sampling and weighting procedures and longer field periods produced more accurate results.
The best performing samples, by contrast, were balanced not just on those characteristics but also on variables such as education and income.
This latter set of samples also tended to be in the field longer, which is likely indicative of the fact that applying more rigorous selection procedures is more time consuming than using less stringent procedures. Our data come from just nine samples, none of which experimentally manipulated these design features.
Consequently, the effects of those features are not well isolated.
In total, 20 benchmark measurements were used in this study see Appendix D. They touch on a number of different topics including smoking, health care coverage, income, participation in civic or recreational Benfits and drivers, voting, household composition, internet usage and more.
The benchmarks were derived from high quality federal sources based either on national surveys or administrative data. For example, the fact that sample G yielded an average estimated bias of 8. The rule employed in this study was to apply whichever weight vendor-provided or our standardized weight performed better in terms of minimizing the average estimated bias.
Vendors supplying the other four samples A, D, G and H declined to provide weights, signaling that the sample balancing e. The benchmarking results suggest that imposing a few broad quotas is not, in fact, sufficient for at least some of these samples.
While multiple vendors have the ability to sample or weight on a range of variables that go beyond standard demographics, sample I was unusual in this respect. Two of the 20 benchmarks, voter registration and internet usage, were among the variables on which sample I was adjusted.
For various reasons, the weighted estimates from that sample hit neither of the benchmark values exactly. Specifically, it raises the question of whether the other samples would have performed better if they too had been selected and weighted the way that sample I was.
In the interest of not putting our thumb on the scale, particularly since the ATP is one of the comparison points, we allowed both of those variables to remain in the benchmarking analysis and for sample I to benefit from its better performing vendor weight.
To understand what effect those decisions have on the benchmarking results, we re-ran the analysis using just the 18 variables which, to the best of our knowledge, were not used in the sampling or weighting of any of the samples.
We also re-ran the analysis imposing the standardized weight on sample I, rather than the vendor weight. In each instance, sample I still showed the smallest average estimated bias. Tension between conditioning and measurement While sample I performed the best on the benchmarking analysis, the manner in which that outcome was achieved highlights a critical issue for survey researchers in this era of ever-growing reliance on models to fix sample deficiencies.
The design of sample I conditioned on several variables that many social scientists study as survey outcomes — political party, ideology, political interest, voter registration and internet usage.Get news and updates about CoPilot Truck for Drivers Follow @copilottruck ALK Technologies is a transportation technology company dedicated to defining the optimal route to success through innovative routing, mileage, mapping, and navigation solutions.
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