Accounting for careless and insufficient effort responding in large-scale survey data-development, evaluation, and...

Careless and insufficient effort responding (C/IER) poses a major threat to the quality of large-scale survey data. Traditional indicator-based procedures for its detection are limited in that they are only sensitive to specific types of C/IER behavior, such as straight lining or rapid responding, rely on arbitrary threshold settings, and do not allow taking the uncertainty of C/IER classification into account. Overcoming these limitations, we develop a two-step screen-time-based weighting procedure for computer-administered surveys. The procedure allows considering the uncertainty in C/IER identification, is agnostic towards the specific types of C/IE response patterns, and can feasibly be integrated with common analysis workflows for large-scale survey data. In Step 1, we draw on mixture modeling to identify subcomponents of log screen time distributions presumably stemming from C/IER. In Step 2, the analysis model of choice is applied to item response data, with respondents' posterior class probabilities being employed to downweigh response patterns according to their probability of stemming from C/IER. We illustrate the approach on a sample of more than 400,000 respondents being administered 48 scales of the PISA 2018 background questionnaire. We gather supporting validity evidence by investigating relationships between C/IER proportions and screen characteristics that entail higher cognitive burden, such as screen position and text length, relating identified C/IER proportions to other indicators of C/IER as well as by investigating rank-order consistency in C/IER behavior across screens. Finally, in a re-analysis of the PISA 2018 background questionnaire data, we investigate the impact of the C/IER adjustments on country-level comparisons.