How do you validate and calibrate survey weights with external data?

2 How to validate survey weights?

One way to validate survey weights is to use poststratification, which is a technique that adjusts the weights so that the weighted sample totals match the population totals for certain variables. For example, you can poststratify by age, gender, and region, using census data as the external source. To do this, you need to calculate the ratio of the population total to the weighted sample total for each poststratification cell, and multiply the original weight by this ratio. This will ensure that your weighted sample reflects the population structure for those variables.

3 What are the benefits of poststratification?

Poststratification can reduce the variance of the estimates, as well as the potential bias due to nonresponse or undercoverage. By aligning the weighted sample with the population, you can increase the precision and accuracy of your estimates. Poststratification can also improve the representativeness of your sample, especially if you have a small or unbalanced sample. However, poststratification also has some limitations, such as requiring reliable external data, introducing new sources of error, and not correcting for measurement errors.

4 How to calibrate survey weights?

Another way to validate and calibrate survey weights is to use calibration, which is a technique that adjusts the weights so that they satisfy some constraints based on external data. For example, you can calibrate by income, education, or political affiliation, using other surveys or administrative data as the external source. To do this, you need to find the optimal set of weights that minimize the deviation from the original weights, while satisfying the calibration equations. This can be done using iterative algorithms or optimization methods.

5 What are the benefits of calibration?

Calibration can also reduce the variance and bias of the estimates, as well as the potential effects of nonresponse or undercoverage. By imposing the constraints based on external data, you can ensure that your weighted estimates are consistent with the population characteristics for those variables. Calibration can also handle multiple calibration variables, complex survey designs, and nonlinear relationships. However, calibration also has some drawbacks, such as requiring valid and relevant external data, introducing new sources of error, and not correcting for measurement errors.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?