Detecting neglectful respondents in a Computer-Aided Web Interview (CAWI)

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By Diane MAILLOT-TCHOFO, Fabienne LE SAGER and Louis MAREC, from Médiamétrie's Data & Methods Department, and Tom DEVYNCK, Médiamétrie and Toulouse School of Economics

The most familiar observational error within surveys is associated with respondents’ inability or
unwillingness to provide the correct answer.

In this context, the will to correctly estimate digital devices ownership (e.g. TV, smartphone) drove Médiamétrie to develop an ambivalent method to detect neglectful respondents in a Computer-Aided Web Interview (CAWI).

We drew from the literature (Laura Gamble, 2023 and Anvita Mahajan, 2023) works and derived an ambivalent method combining both approaches. Our first approach makes use of the questionnaire’s completion times. The second approach is a two-step clustering algorithm focused on the ownership of digital equipment. A K-Means  was applied on the respondent’s household socio-demographic characteristics.

Then, machine learning models were applied to each cluster to contain the models’ shortcomings.
Our final list of sloppy respondents was obtained by combining the results of the two approaches.