(In)effectiveness of Frame-Inducing Rating Instructions in State-Trait Research

Negative affectivity (Trait-NA) is often measured at baseline and treated as a control variable in models of workplace stress and well-being. However, recent research suggests that routine procedures for the measurement of Trait-NA (i.e., using trait-inducing instructions) may not adequately distinguish Trait-NA from State-NA, leading to probable model misspecification with substantive theoretical implications. We employ Trait-State-Occasion modelling of intensive longitudinal data to examine the degree to which trait-inducing instructions are actually reflective of Trait-NA, as intended. Results indicated that (a) NA, like other often-used measures (e.g., positive affect and workload) exhibit substantial portions of both State and Trait variance, and (b) that rating instructions were generally ineffective in achieving shifts in rating perspectives (5%-15% or so on the average). We discuss implications of using latent variable modelling to distinguish Traits and States in 'shortitudinal research.'

Effects of face masks on fairness in on-site personnel selection during a pandemic

Introduction

Despite significant challenges, personnel selection procedures had to continue as on-site testing in the Covid-19 pandemic. Health and safety measures and specifically the use of face masks threaten to limit the fairness of cognitive testing and behavioral observation in the assessment center.

Methods

In this study, we compare the performance and pass rates of pilot selection under three different conditions in the selection campaigns of 2019 (pre-pandemic), 2020 (health and safety measures without mask), and 2021 (health and safety measures with mask).

Results

Mask wearing and other health and safety measures had no influence on the objective parameters of pilot selection. However, for some of the areas of competence in the assessment center subjective observability was rated lower for the condition with face masks.

Discussion

We conclude that the fairness and precision of selection processes are not compromised by wearing face masks and that a high degree of standardization in diagnostic instruments prevents a partially reduced subjective observability from affecting the selection's outcome.

Latent state-trait structure of BPRS subscales in clinical high-risk state and first episode psychosis

To investigate the longitudinal latent state-trait structure of the different dimensions of psychosis symptoms in clinical high-risk state (CHRS) and first episode psychosis (FEP) individuals over a one year time-span. This paper examines if the symptom clusters Positive Symptoms, Negative Symptoms, Affectivity, Resistance, Activation, and Excitement according to the Brief Psychiatric Rating Scale (BPRS) differ in their trait and state characters in 196 CHRS and 131 FEP individuals. Statistical analysis was performed using latent state-trait analysis. On average, trait differences accounted for 72.2% of Positive Symptoms, 81.1% of Negative Symptoms, 57.0% of Affectivity, and 69.2% of Activation, whereas 15.0% of the variance of Resistance and 13.2% of the variance of Excitement were explained by trait differences. Explorative analyses showed a trait components' increase of 0.408 in Positive Symptoms from baseline up to the 9th month and an increase of 0.521 in Affectivity from baseline up to the 6th month. Negative Symptoms had the highest trait component levels of all subscales between baseline and 6 months. The finding that an increasing proportion of psychosis symptoms is persisting over time underlines the importance of early intervention programs in individuals with psychotic disorders.

Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

Purpose

To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).

Design/methodology/approach

Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).

Findings

In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.

Originality/value

The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

Do State and Trait Measures Measure States and Traits? The Case of Community-Dwelling Caregivers of Older Adults

Spielberger’s state and trait anxiety and anger scales are widely used and documented, but there is little or no direct evidence that they actually measure their respective state and trait aspects as was intended. We conducted latent state-trait analyses on data collected from 310 community-dwelling caregivers of older adult care recipients and found that (a) both state and trait scales reflected a mixture of state and trait aspects of their latent constructs, (b) state scales reflected more state-like variance than did corresponding trait scales, but (c) both state and trait scales were dominated by stable trait-like variance. Follow-up bivariate latent state-trait analyses indicated that correlations between trait components of anger and anxiety correlated more strongly with trait components of caregiver–care recipient mutually communal behavior and care recipient problem behavior than did state–state component correlations. Implications for the measurement of state and trait components of psychological constructs are discussed.

Complex Problem Solving: What It Is and What It Is Not

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Retaking Employment Tests: What We Know and What We Still Need to Know

The extent to which test scores change upon retesting has important implications for both organizations and individuals who apply to those organizations. We review research on retesting and score changes that dates back nearly 100 years. Our findings suggest that compared to initial test scores, retest scores tend to be higher, more varied, and more reliable and tend to demonstrate somewhat stronger relations with criteria such as academic and job performance. There also is some evidence that retesting can change the constructs test scores reflect. However, empirical research has yet to clearly delineate factors that underlie such differences between initial and retest scores. We discuss implications of these findings for organizations and applicants. We also identify key unanswered questions about retesting that future research should address.