Applicant Faking on Personality Tests: Good or Bad and Why Should We Care?

The unitarian understanding of construct validity holds that deliberate response distortion in completing self-report personality tests (i.e., faking) threatens trait-based inferences drawn from test scores. This “faking-is-bad” (FIB) perspective is being challenged by an emerging “faking-is-good” (FIG) position that condones or favors faking and its underlying attributes (e.g., social skill, ATIC) to the degree they contribute to predictor–criterion correlations and are job relevant. Based on the unitarian model of validity and relevant empirical evidence, we argue the FIG perspective is psychometrically flawed and counterproductive to personality-based selection targeting trait-based fit. Carrying forward both positions leads to variously dark futures for self-report personality tests as selection tools. Projections under FIG, we suggest, are particularly serious. FIB offers a more optimistic future but only to the degree faking can be mitigated. Evidence suggesting increasing applicant faking rates and other alarming trends makes the FIB versus FIG debate a timely if not urgent matter

Faking Is as Faking Does: A Rejoinder to Marcus (2021)

Applicant faking poses serious threats to achieving personality-based fit, negatively affecting both the worker and the organization. In articulating this “faking-is-bad” (FIB) position, Tett and Simonet (2021) identify Marcus’ (2009) self-presentation theory (SPT) as representative of the contrarian “faking-is-good” camp by its advancement of self-presentation as beneficial in hiring contexts. In this rejoinder, we address 20 of Marcus’ (2021) claims in highlighting his reliance on an outdated empiricist rendering of validity, loosely justified rejection of the negative and moralistic “faking” label, disregard for the many challenges posed by blatant forms of faking, inattention to faking research supporting the FIB position, indefensibly ambiguous constructs, and deep misunderstanding of person–workplace fit based on personality assessment. In demonstrating these and other limitations of Marcus’ critique, we firmly uphold the FIB position and clarify SPT as headed in the wrong direction

Assessment centers at the crossroads: Toward a reconceptualization of assessment center exercises

Exercises are key components of assessment centers (ACs). However, little is known about the nature and determinants of AC exercise performance. The traditional exercise paradigm primarily emphasizes the need to simulate task, social, and organizational demands in AC exercises. This chapter draws on trait activation theory in proposing a new AC exercise paradigm. First, we develop a theoretical framework that addresses the complexity of situational characteristics of AC exercises as determinants of AC performance. Second, we argue for planting multiple stimuli within exercises as a structured means of eliciting candidate behavior. Third, we show how the new paradigm also has key insights for the rating part of ACs, namely, in selecting dimensions, designing behavioral checklists, screening assessors, and training assessors. Finally, the impact of this new AC exercise paradigm is anticipated on important AC outcomes such as reliability, internal/external construct-related validity, criterion-related validity, assessee perceptions, and feedback effectiveness

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed Sea Surface Temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a ‘storyline’ approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change