What question did this study set out to answer?

This research aims to compare various estimation techniques for nonprobability samples to identify effective methods.

January 24, 2019Journal of Survey Statistics and MethodologyOpen Access

Comparing Alternatives for Estimation from Nonprobability Samples

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Authors

RVRichard ValliantBureau of Labor Statistics

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Implication

Simulation studies compare quasi-randomization, superpopulation modeling, and doubly robust estimation techniques for reliability in nonprobability samples.

Key Points

This research aims to compare various estimation techniques for nonprobability samples to identify effective methods.
Evaluation of quasi-randomization, superpopulation modeling, and doubly robust estimation techniques.
Use of simulation studies to assess the effectiveness of different estimation methods.
Analysis includes binary regression, regression trees, and calibration techniques.
Quasi-randomization and superpopulation modeling showed distinct outcomes for nonprobability samples.
Doubly robust estimation improved accuracy in projecting sample data to the broader population.
Simulation studies highlighted the strengths and weaknesses of each estimation approach.

Cite This Study

Richard Valliant (2019) studied this question.

synapsesocial.com/papers/6a1fcb00f905d3cd7d6b45ce https://doi.org/10.1093/jssam/smz003

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Implication

Simulation studies compare quasi-randomization, superpopulation modeling, and doubly robust estimation techniques for reliability in nonprobability samples.

Comparing Alternatives for Estimation from Nonprobability Samples

Discussion

Member takes

Implication

Key Points

Cite This Study

Comparing Alternatives for Estimation from Nonprobability Samples

Discussion

Member takes

Implication

Key Points

Cite This Study

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