Oberski, Daniel L.DeCastellarnau, Anna2021-09-102021-09-102021-09http://hdl.handle.net/10230/48430Social science commonly studies relationships among variables by employing survey questions. Answers to these questions will contain some degree of measurement error, distorting the relationships of interest. Such distortions can be removed by standard statistical methods, when these are provided knowledge of a question’s measurement error variance. However, acquiring this information routinely necessitates additional experimentation, which is infeasible in practice. We use three decades’ worth of survey experiments combined with machine learning methods to show that survey measurement error variance can be predicted from the way a question was asked. By predicting experimentally obtained estimates of survey measurement error variance from question characteristics, we enable researchers to obtain estimates of the extent of measurement error in a survey question without requiring additional data collection. Our results suggest only some commonly accepted best practices in survey design have a noticeable impact on study quality, and that predicting measurement error variance is a useful approach to removing this impact in future social surveys.application/pdfengThis is an Open Access article distributed under the terms of the Creative Commons Attribution License Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properlyattributed.Machine learningMeasurement errorPredictionMultitrait-multimethodSQPinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/openAccess