More than just the mean: moving to a dynamic view of performance-based compensation

Compensation decisions have important consequences for employees and organizations and affect factors such as retention, motivation, and recruitment. Past research has primarily focused on mean performance as a predictor of compensation, promoting the implicit assumption that alternative aspects of dynamic performance are not relevant. To address this gap in the literature, we examined the influence of dynamic performance characteristics on compensation decisions in the National Basketball Association (NBA). We predicted that, in addition to performance mean, performance trend and variability would also affect compensation decisions. Results revealed that performance mean and trend, but not variability, were significantly and positively related to changes in compensation levels of NBA players. Moreover, trend (but not mean or variability) predicted compensation when controlling for future performance, suggesting that organizations overweighted trend in their compensation decisions. Theoretical and practical implications are discussed.     

Decomposing task-switching costs with the diffusion model

In four experiments, task-switching processes were investigated with variants of the alternating runs paradigm and the explicit cueing paradigm. The classical diffusion model for binary decisions (Ratcliff, 1978) was used to dissociate different components of task-switching costs. Findings can be reconciled with the view that task-switching processes take place in successive phases as postulated by multiple-components models of task switching (e.g., Mayr & Kliegl, 2003; Ruthruff, Remington, & Johnston, 2001). At an earlier phase, task-set reconfiguration (Rogers & Monsell, 1995) or cue-encoding (Schneider & Logan, 2005) takes place, at a later phase, the response is selected in accord with constraints set in the first phase. Inertia effects (Allport, Styles, & Hsieh, 1994; Allport & Wylie, 2000) were shown to affect this later stage. Additionally, findings support the notion that response caution contributes to both global as well as to local switching costs when task switches are predictable.     

A stochastic lie detector

Surveys on sensitive issues provide distorted prevalence estimates when participants fail to respond truthfully. The randomized-response technique (RRT) encourages more honest responding by adding random noise to responses, thereby removing any direct link between a participant’s response and his or her true status with regard to a sensitive attribute. However, in spite of the increased confidentiality, some respondents still refuse to disclose sensitive attitudes or behaviors. To remedy this problem, we propose an extension of Mangat’s (Journal of the Royal Statistical Society: Series B, 56, 93–95, 1994) variant of the RRT that allows for determining whether participants respond truthfully. This method offers the genuine advantage of providing undistorted prevalence estimates for sensitive attributes even if respondents fail to respond truthfully. We show how to implement the method using both closed-form equations and easily accessible free software for multinomial processing tree models. Moreover, we report the results of two survey experiments that provide evidence for the validity of our extension of Mangat’s RRT approach.     

Components of task switching: A closer look at task switching and cue switching

Research using the diffusion model to decompose task-switching effects has contributed to a better understanding of the processes underlying the observed effect in the explicit task cueing paradigm: Previous findings could be reconciled with multiple component models of task switching or with an account on compound-cue retrieval/repetition priming. In the present study, we used two cues for each task in order to decompose task-switch and cue-switch effects. Response time data support previous findings that comparable parts of the switching effect can be attributed to cue-switching and task-switching. A diffusion model analysis of the data confirmed that non-decision time is increased and drift rates are decreased in unpredicted task-switches. Importantly, it was shown that non-decision time was selectively increased in task-switching trials but not in cue-switching trials. Results of the present study specifically support the notion of additional processes in task-switches and can be reconciled with broader multiple component accounts.