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Adjusting for systematic and differential errors using simulations

Training materials for short course ‘Adjustment Methods for Data Quality Problems: Missing Data, Measurement Error, and Misclassification.’

Published onDec 22, 2023
Adjusting for systematic and differential errors using simulations
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Abstract

In this workshop we are going to see how we can adjust for systematic and differential forms of measurement error using simulations. This is the least sophisticated way of adjusting for measurement error, but as we will see it is quite flexible in that it can be used to represent a wide range of measurement error processes. This approach involves modifying the error-prone variable in order to strip away the measurement error process at play, so we can retrieve the values of the unobserved true variable. This is the first practical exercise from day 2 of the short course ‘Adjustment Methods for Data Quality Problems: Missing Data, Measurement Error, and Misclassification.’ You can download the associated lecture slides and data here: https://github.com/jmpinasanchez/measurement

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