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See [[SurveyDisposition|here]] for details about survey dispositions. ---- == Calculating Weights == The base weight is the inverse of the probability of being sampled. Think ''desired over actual''. As such, the sum of base weights should equal the population size. For a SRS design, this is calculated as a simple rate. Given a population of 20,000 and a sample size of 667, the propbability of being sampled is 20,000/667 = '''29.99'''. For a STSRS design, the same process is applied per stratum. ---- == Non-Response Adjustments == Survey weights can adjust for non-response bias. The core concept is to use auxiliary frame data (i.e. descriptives known for ''both'' respondents and non-respondents) that is correlated with key measures or responsivity. '''Weighting class adjustments''' divides the sample into weighting classes and applies a class-specific adjustment factor to every case. '''Propensity score adjustments''' calculates the inverse of the estimated probability to respond and applies that as a secondary weight. Adjustments are applied in phases. Cases with unknown eligibility often cannot be adjusted through these methods, and need to be removed. Ineligible cases often are undesirable in analysis datasets, so weights are further adjusted to account for their removal. ---- == Calibration Adjustments == Survey weights can be adjusted to ensure that known population descriptives are reflected in the estimates. Methods include: * post-stratification (i.e. ''desired over actual'') * raking * linear calibration (GREG) === Raking === Rakign, or RIM weighting, involves applying post-stratification by each dimension iteratively, until the weights converge. Convergence is defined as the root mean square (RMS) falling below a threshold, typically 0.000005. Raked weights generally should not be applied if their efficiency falls below 70%. |
Survey Weights
Survey weights account for the design of a survey sample and other biases/errors introduced by a survey instrument.
Contents
The Basic Process
- Set survey dispositions
- Calculate base weights
- Apply non-response adjustments to base weights
- Calibrate the weights
See here for details about survey dispositions.
Calculating Weights
The base weight is the inverse of the probability of being sampled. Think desired over actual. As such, the sum of base weights should equal the population size.
For a SRS design, this is calculated as a simple rate. Given a population of 20,000 and a sample size of 667, the propbability of being sampled is 20,000/667 = 29.99.
For a STSRS design, the same process is applied per stratum.
Non-Response Adjustments
Survey weights can adjust for non-response bias. The core concept is to use auxiliary frame data (i.e. descriptives known for both respondents and non-respondents) that is correlated with key measures or responsivity.
Weighting class adjustments divides the sample into weighting classes and applies a class-specific adjustment factor to every case.
Propensity score adjustments calculates the inverse of the estimated probability to respond and applies that as a secondary weight.
Adjustments are applied in phases. Cases with unknown eligibility often cannot be adjusted through these methods, and need to be removed. Ineligible cases often are undesirable in analysis datasets, so weights are further adjusted to account for their removal.
Calibration Adjustments
Survey weights can be adjusted to ensure that known population descriptives are reflected in the estimates.
Methods include:
post-stratification (i.e. desired over actual)
- raking
- linear calibration (GREG)
Raking
Rakign, or RIM weighting, involves applying post-stratification by each dimension iteratively, until the weights converge. Convergence is defined as the root mean square (RMS) falling below a threshold, typically 0.000005.
Raked weights generally should not be applied if their efficiency falls below 70%.