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Target Estimation and Adjustment Weighting for Survey Nonresponse and Sampling Bias

Target Estimation and Adjustment Weighting for Survey Nonresponse and Sampling Bias

Target Estimation and Adjustment Weighting for Survey Nonresponse and Sampling Bias

Devin Caughey, Massachusetts Institute of Technology
Adam J. Berinsky, Massachusetts Institute of Technology
Sara Chatfield, University of Denver
Erin Hartman, University of California, Los Angeles
Eric Schickler, University of California, Berkeley
Jasjeet S. Sekhon, University of California, Berkeley
October 2020
Available
Paperback
9781108794152
£17.00
GBP
Paperback
USD
eBook

    We elaborate a general workflow of weighting-based survey inference, decomposing it into two main tasks. The first is the estimation of population targets from one or more sources of auxiliary information. The second is the construction of weights that calibrate the survey sample to the population targets. We emphasize that these tasks are predicated on models of the measurement, sampling, and nonresponse process whose assumptions cannot be fully tested. After describing this workflow in abstract terms, we then describe in detail how it can be applied to the analysis of historical and contemporary opinion polls. We also discuss extensions of the basic workflow, particularly inference for causal quantities and multilevel regression and poststratification.

    Product details

    October 2020
    Paperback
    9781108794152
    75 pages
    150 × 230 × 6 mm
    0.16kg
    Available

    Table of Contents

    • 1. The Problem of Unrepresentative Survey Samples
    • 2. Weight Estimation
    • 3. Target Estimation
    • 4. Application to Contemporary Election Surveys
    • 5. Application to Quota-sampled Opinion Polls
    • 6. Extensions and Conclusion.
      Authors
    • Devin Caughey , Massachusetts Institute of Technology
    • Adam J. Berinsky , Massachusetts Institute of Technology
    • Sara Chatfield , University of Denver
    • Erin Hartman , University of California, Los Angeles
    • Eric Schickler , University of California, Berkeley
    • Jasjeet S. Sekhon , University of California, Berkeley