Speakers
Description
Background. Careless responding (CR) occurs when individuals do not pay adequate attention to item content. Research has shown that CR introduces bias and compromises data quality (Podsakoff et al., 2012), highlighting the need for effective prevention and management strategies (e.g., Arthur et al., 2021; Edwards, 2019; Ward & Meade, 2022). Different methods have been proposed to detect CR, one of them being Instructed Response Items (IRIs), which direct participants to provide specific answers. Failing these items serves as an indicator of CR. The use of IRIs stands out for its simplicity, transparency, and metric properties (Kam & Chan, 2018). Despite its significance, the nature of CR remains unclear. While some researchers consider CR as a stable trait (Meade & Craig, 2012), others argue it is transient state (Maniaci & Rogge, 2014). However, little empirical evidence has clarified this distinction. A recent study by Tomás et al. (2024), conducted with a sample of adult workers who were paid for their participation in the study, identified subpopulations with distinct CR patterns, some displaying stable CR behaviors, while others exhibited changes over time.
Objectives. This study aims to deepen the understanding of CR’s nature and dynamics by analyzing its patterns over time in a sample with different sociodemographic characteristics (university students) and with different contextual factors (individuals were not financially compensated for their participation). Additionally, we examine whether CR operates as a trait or state for the entire population or if distinct subpopulations exist, some for whom CR is a trait and others for whom it is a state. To detect CR, we utilize IRIs.
Methods. A total of 360 Spanish university students (71.7% women; mean age = 25.6 years, SD = 6.3) participated in the study after being offered a free face-to-face training course. We used a within-subject longitudinal design with three data collection points, spaced at 3-month intervals. Participants were first contacted during their final semester (T1), approximately one month before graduation, followed by assessments nine months post-graduation (T2), and four months after T2 (T3). The trajectory of CR over time was modeled using latent growth modeling (LGM), and latent class growth analysis (LCGA) in Mplus.
Results and Conclusions. The results aligned with previous research (e.g., Tomás et al., 2024): while CR exhibited a stable response pattern over time at the population level, distinct subpopulations emerged, each displaying different CR trajectories. Notably, the subgroups identified in this study differed from those found by Tomás et al. (2024). In this study, three distinct subpopulations emerged: a relatively stable group (careful individuals) and two groups whose inattentiveness increased over time (one initially careful but becoming less attentive and another already careless that became even more inattentive). These findings contribute to the understanding of CR’s nature and dynamics, highlighting the role of personal factors (e.g., age) and contextual factors (e.g., participation compensation) in shaping CR patterns over time.
This study has been developed within the research project PID2022-141339NB-I00, funded by MCIU /AEI /10.13039/501100011033 / and by FEDER A way to make Europe, EU
