The increased availability of time‐related information as a result of computer‐based assessment has enabled new ways to measure test‐taking engagement. One of these ways is to distinguish between solution and rapid guessing behavior. Prior research has recommended response‐level filtering to deal with rapid guessing. Response‐level filtering can lead to parameter bias if rapid guessing depends on the measured trait or (un‐)observed covariates. Therefore, a model based on Mislevy and Wu (1996) was applied to investigate the assumption of ignorable missing data underlying response‐level filtering. The model allowed us to investigate different approaches to treating response‐level filtered responses in a single framework through model parameterization. The study found that lower‐ability test‐takers tend to rapidly guess more frequently and are more likely to be unable to solve an item they guessed on, indicating a violation of the assumption of ignorable missing data underlying response‐level filtering. Further ability estimation seemed sensitive to different approaches to treating response‐level filtered responses. Moreover, model‐based approaches exhibited better model fit and higher convergent validity evidence compared to more naïve treatments of rapid guessing. The results illustrate the need to thoroughly investigate the assumptions underlying specific treatments of rapid guessing as well as the need for robust methods.

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As researchers in the social sciences, we are often interested in studying not directly observable constructs through assessments and questionnaires. But even in a well-designed and well-implemented study, rapid-guessing behavior may occur. Under rapid-guessing behavior, a task is skimmed shortly but not read and engaged with in-depth. Hence, a response given under rapid-guessing behavior does bias constructs and relations of interest. Bias also appears reasonable for latent speed estimates obtained under rapid-guessing behavior, as well as the identified relation between speed and ability. This bias seems especially problematic considering that the relation between speed and ability has been shown to be able to improve precision in ability estimation. For this reason, we investigate if and how responses and response times obtained under rapid-guessing behavior affect the identified speed–ability relation and the precision of ability estimates in a joint model of speed and ability. Therefore, the study presents an empirical application that highlights a specific methodological problem resulting from rapid-guessing behavior. Here, we could show that different (non-)treatments of rapid guessing can lead to different conclusions about the underlying speed–ability relation. Furthermore, different rapid-guessing treatments led to wildly different conclusions about gains in precision through joint modeling. The results show the importance of taking rapid guessing into account when the psychometric use of response times is of interest.

There is little research on the study success factors of refugee students in higher education. One approach to meeting the growing global demands is to provide online education specifically for refugees. This study examines specific personal characteristics of refugee students and their influence on success and retention in online education. Individual factors such as intrinsic motivation and language proficiency, cognitive functioning, and sociodemographic factors such as gender and country of residence influence retention of refugee students during online studies. The results indicate that sociodemographic factors (e.g., gender), cognitive factors (e.g., English proficiency), and external factors (e.g., country of residence) have a significant influence on study retention on refugee students.

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