Title: Detecting the Onset of Careless Responding with the R Package
carelessonset

Authors: Max Welz, Andreas Alfons

Abstract:
Questionnaires in the behavioral sciences tend to be lengthy: survey measures
comprising hundreds of items are the norm rather than the exception. However,
the literature suggests that the longer a questionnaire takes, the higher the
probability that participants lose interest and start responding carelessly.
Consequently, in long surveys a large number of participants may engage in
careless responding, posing a major threat to internal validity. Recently,
Welz and Alfons <arXiv:2303.07167> proposed a novel method to identify the
onset of careless responding (or an absence thereof) for each participant,
which is implemented in the open source R package carelessonset
(https://github.com/mwelz/carelessonset). To enhance computational
performance, the package is primarily developed in C++ and the deep learning
framework tensorflow, and is highly parallelizable. In this workshop, we guide
attendees through the package carelessonset through a brief tutorial and
demonstrate its practical usefulness, intuitive user interface, and rich
methods for plotting and printing by means of an empirical application on a
Big Five administration. We encourage interested attendees to bring their own
questionnaire data to the workshop to try out the package with the guidance of
its developers.

Working paper: https://arxiv.org/abs/2303.07167