The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis.

ABSTRACT: In this study, we demonstrate how supervised learning can extract interpretable survey motivation measurements from a large number of responses to an open-ended question. We manually coded a subsample of 5,000 responses to an open-ended question on survey motivation from the GESIS Panel (25,000 responses in total); we utilized supervised machine learning to classify the remaining responses. We can demonstrate that the responses on survey motivation in the GESIS Panel are particularly well suited for automated classification, since they are mostly one-dimensional. The evaluation of the test set also indicates very good overall performance. We present the pre-processing steps and methods we used for our data, and by discussing other popular options that might be more suitable in other cases,

SUBMITTER: Haensch AC 

PROVIDER: S-EPMC9403118 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature